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This major reference work provides the most up-to-date research advances and theories in cognitive radio technology, from cognitive radio principles and theory to cognitive radio standards and systems, from fundamental limits of cognitive radio channels to cognitive radio networks, from the current cognitive radio practices and examples to future 5G cognitive cellular networks. This handbook will include some emerging applications of cognitive radio in areas such as smart grid, internet-of-things, big data, small cell/heterogeneous networks, and in 5G. The potential readers include postgraduate students, academic staff, telecommunications engineering, spectrum policy makers, and industry entrepreneurs.

Inhaltsverzeichnis

Frontmatter

Cognitive Radio Communications

Frontmatter

1. Multiple Antenna Spectrum Sensing in Colored Noise

The problem of multiple antenna spectrum sensing is addressed where the receiver noise is allowed to be temporally colored with unknown power spectral density, but must be spatially uncorrelated. The signal is received over a possibly frequency-selective, unknown channel. A comprehensive overview of spectrum sensing approaches under colored noise is presented. Both time-domain and frequency-domain approaches exploiting stationarity are presented. Cyclostationarity-based spectrum sensing methods are also reviewed.

Jitendra K. Tugnait

2. Spectrum Sensing Using Markovian Models

Markovian models, as well as other statistical models, have been applied in the context of cognitive radio communications to characterize user activity in a given spectrum band and to develop algorithms for temporal spectrum sensing. In this chapter, we discuss spectrum sensing based on Markovian models. We provide an overview of the related literature and then discuss the application of discrete-time Markov chain models to spectrum sensing, in particular the hidden bivariate Markov chain. We focus on the modeling of cognitive radio channels using Markov chains, spectrum detection, and parameter estimation. We then discuss various spectrum sensing scenarios in which the Markovian models are used. Finally, we discuss open problems and topics for further research related to spectrum sensing using Markovian models.

Joseph M. Bruno, Yariv Ephraim, Brian L. Mark, Zhi Tian

3. Waveform Designs for Cognitive Radio and Dynamic Spectrum Access Applications

Cognitive radio and dynamic spectrum access systems are effective ways of using radio spectrum which is a scarce source. Cognitive radio applications changed the paradigm for the wireless communications systems in the past decades. Besides that, different communications systems and wireless communications channels require different waveform designs and radio access technologies. In this study, a general design and evaluation procedure for the new waveform techniques are presented based on cognitive radio and dynamic spectrum access requirements. Radio access technology researches for the future-generation cellular systems and cognitive radio systems intersect to each other. Therefore, some of the future waveform designs and related modifications are analyzed under the cognitive radio perspective. Several waveforms which have various trade-off situations are discussed from a general perspective and an adaptivity/flexibility perspective.

Ahmet Yazar, Mohamed Elkourdi, Huseyin Arslan

4. Modeling and Performance Analysis of Cognitive Radio Systems from a Deployment Perspective

A cognitive radio (CR) system aims at an efficient utilization of the spectrum below 6 GHz – suitable for mobile communications – by enabling a secondary access to the licensed spectrum while ensuring a sufficient protection to the licensed users. Despite the fact that an extensive amount of literature has been dedicated to the field of CR, its performance analysis has been dealt inadequately from a deployment perspective, therefore making it difficult to understand the extent of vulnerability caused to the primary system. Following a deployment perspective, it has been identified that the involved channels’ knowledge is pivotal for the realization of CR techniques. However, the aspect of channel knowledge in context of CR systems, particularly its detrimental effect on the performance, has not been clearly understood. With the purpose of curtailing this gap, this chapter proposes a successful integration of this knowledge by carrying out estimation of the involved channels within a CR system. More specifically, this chapter outlines the following two aspects: first, this chapter establishes an analytical framework to characterize the degradation in the performance due to effects such as time allocation and variation, arising due to imperfect channel knowledge. Second, this chapter features performance tradeoffs that determine the maximum achievable throughput of the CR systems while satisfying the interference constraint.

Ankit Kaushik, Shree Krishna Sharma, Symeon Chatzinotas, Björn Ottersten, Friedrich K. Jondral

5. Spectrum Sensing, Measurement, and Modeling

Modeling spectrum sensing is a critical step that paves the way to (i) identify the key impairments that affect the detection performance and (ii) help develop algorithms and receiver architectures that mitigate these impairments. In this chapter, realistic and practical sensing models are presented beyond those developed for classical detection theory. These models capture the impact of different sensing receiver impairments on several detectors such as the energy, the pilot, and the cyclostationarity detectors. Several receiver nonidealities are investigated, including noise uncertainty, imperfect synchronization, and cyclic frequency offsets. In addition, challenges and impairments pertaining to wideband sensing are analyzed, including the presence of strong adjacent interferers as well as the nonlinearities of the receiver RF front-end. From these models, several mitigation techniques are developed to compensate for the presence of the different sensing receiver impairments. Measurements and simulation results are presented throughout the chapter to show the negative impact of such impairments and validate that the developed mitigation techniques provide tangible performance gains.

Ghaith Hattab, Danijela Cabric

6. Spectrum Sensing Methods and Their Performance

Spectrum sensing is the process of determining if a spectrum slot is occupied or not by a primary signal. This tutorial emphasizes energy detection based spectrum sensing and provides a broad overview of the tools necessary for performance analysis of several spectrum sensing algorithms. A detailed description of the spectrum sensing problem is provided as a binary hypothesis test. The main parameters of interest – decision statistic, detection, and false-alarm probabilities and the decision threshold – are discussed. These parameters of the energy detector, which computes the energy of the received signal, are described. The use of the central limit theorem (CLT) to achieve energy detection with prescribed performance level is discussed. The receiver operating characteristic (ROC) curve and area under the curve (AUC) are described. Fading, a fundamental wireless channel impairment, can be mitigated with multiple antenna techniques, which provide spatial diversity gains. The performance of the energy detector with two low-complexity diversity techniques is described. The performance is analyzed for Rayleigh fading, for spatial correlation, and in the high signal-to-noise ratio (SNR) regime. General analytical techniques are highlighted. Double-threshold energy detector, P-norm detector, and energy detection for full-duplex nodes are described. Alternative to energy detection includes cyclostationary detection, matched filter-based detection, and waveform-based detection. These methods are briefly discussed. Spectrum sensing is an essential part of smart grid, Internet of things, and cognitive radio. An overview is provided.

Chintha Tellambura

7. Non-cooperative and Cooperative Spectrum Sensing in 5G Cognitive Networks

5G is the expected next step of the mobile cellular network evolution, and it is considered as the answer to the ongoing huge increase of cellular users and services. The architecture envisioned for 5G includes a large number of different network entities and systems that share a common spectrum resource via a dynamic spectrum access (DSA) approach. This solution is expected to significantly increase the overall spectrum efficiency but also introduces the challenge of optimizing the coexistence between the entities forming the overall network, by limiting their mutual interference. Within this context, the cognitive radio (CR) paradigm, mainly focusing on its peculiar function, that is, spectrum sensing (SS), is being currently proposed as one of the main enablers for efficient DSA with limited interference. The goal of this chapter is to provide a comparative analysis on CR-inspired spectrum resource management (CR-SRM) mechanisms recently proposed for the 5G architecture, which mainly exploit SS, in order to characterize up-to-date research trends on the topic and highlight still-open challenges and possible future work directions.

Giuseppe Caso, Mai T. Phuong Le, Luca De Nardis, Maria-Gabriella Di Benedetto

8. Spectrum Sensing, Database, and Its Hybrid

The rising popularity of wireless services resulting in spectrum shortage has motivated dynamic spectrum sharing to facilitate efficient usage of the underutilized spectrum. Cognitive radio has emerged as one of the most promising candidate solutions to improve spectrum utilization, by allowing secondary users (SUs) to opportunistically access the temporarily unused spectrum, without introducing harmful interference to primary users (PUs). A crucial requirement in cognitive radio networks (CRNs) is wideband spectrum sensing, in which SUs should detect spectral opportunities across a wide frequency range. However, wideband spectrum sensing could lead to unaffordable high sampling rates at energy-constrained SUs. Sub-Nyquist sampling was developed to overcome this issue by exploiting the sparse property of the wideband signals. Additionally, to relax the sensing requirements, hybrid framework that combines the advantages of both geo-location database and spectrum sensing is explored. The experimental results show that the hybrid schemes can achieve improved detection performance with reduced hardware and computation complexity in comparison with the sensing and database only approach.

Yue Gao, Yuan Ma

9. Sequential Methods for Spectrum Sensing

Spectrum sensing is widely regarded as a key enabling technology to support dynamic spectrum access (DSA) for cognitive radio (CR). Though in principle spectrum sensing can be viewed as a traditional signal detection problem, the design of spectrum sensing algorithms needs to take into account certain stringent requirements due to the nature of CR systems. Firstly, it is important for spectrum sensing algorithms to be robust to signal models as it is often difficult in practice for secondary users (SUs) to acquire complete or even partial knowledge about primary signals. Secondly, a small detection delayDetection delay is essential for the spectrum sensing even under a fairly low detection signal-to-noise ratio (SNR) level with low detection error probabilities. This chapter focuses on a particular type of spectrum sensing algorithms, called sequential spectrum sensing algorithms for CR systems. Compared with block-based sensing algorithms, sequential sensing algorithms enable us to make detection decision with minimum delay while still providing certain performance guarantee. We will first illustrate the benefits of sequential detection for a single-band system. We will then discuss how to design quickest sequential scanning algorithms for multiband systems to quickly identify free channels.

Yan Xin, Lifeng Lai

10. Cooperative Spectrum Sensing: From Fundamental Limits to Practical Designs

The first and foremost function in cognitive radio is spectrum sensing. To overcome the performance bottleneck created by fading and shadowing effects of the wireless channel, cooperation among sensing users is proposed as a promising solution. However, most designs of cooperative sensing strategies are developed in a rather ad hoc manner due to the lack of the fundamental knowledge on the cooperation gain. Hence, in this chapter, the cooperation gain in the context of cooperative spectrum sensing is rigorously quantified via diversity and error exponent analyses. The fundamental limits of the diversity and error exponent are derived as functions of key system parameters. After that, a couple of case studies are presented to illustrate how the concepts of diversity and error exponent can guide practical designs of cooperative spectrum sensing.

Dongliang Duan, Liuqing Yang, Shuguang Cui

11. Analog to Digital Cognitive Radio

Enabling cognitive radio (CR) requires revisiting the traditional task of spectrum sensing with specific and demanding requirements in terms of detection performance, real-time processing, and robustness to noise. Unfortunately, conventional spectrum sensing methods do not satisfy these demands. In particular, the Nyquist rate of signals typically sensed by a CR is prohibitively high so that sampling at this rate necessitates sophisticated and expensive analog to digital converters, which lead to a torrent of samples. Over the past few years, several sampling methods have been proposed that exploit signals’ a priori known structure to sample them below Nyquist. In this chapter, we review some of these techniques and tie them to the task of spectrum sensing for CRs. We then show how other spectrum sensing challenges can be tackled in the sub-Nyquist regime. First, to cope with low signal-to-noise ratios, spectrum sensing may be based on second-order statistics recovered from the low rate samples. In particular, cyclostationary detection allows to differentiate between communication signals and stationary noise. Next, CR networks, that perform collaborative low rate spectrum sensing, have been proposed to overcome fading and shadowing channel effects. Last, to enhance CR efficiency, we present joint spectrum sensing and direction of arrival estimation methods from sub-Nyquist samples. These allow to map the temporarily vacant bands both in terms of frequency and space. Throughout this chapter, we highlight the relation between theoretical algorithms and results and their practical implementation. We show hardware simulations performed on a prototype built with off-the-shelf devices, demonstrating the feasibility of sub-Nyquist spectrum sensing in the context of CR.

Deborah Cohen, Shahar Tsiper, Yonina C. Eldar

Dynamic Spectrum Access and Sharing

Frontmatter

12. Principles and Challenges of Cooperative Spectrum Sensing in Cognitive Radio Networks

Cognitive radio (CR) technology is a promising solution to the inevitable problem of spectrum scarcity and underutilization. Cognitive radios can perform spectrum sensing, dynamically identify unused spectrum, and opportunistically utilize those spectrum holes for their own transmission. Cognitive radio technology is also a key concept suggested to be part of the fifth generation of cellular wireless standards (5G). Efficient spectrum sensing is crucial to the effective deployment of CR networks. Cooperative spectrum sensing (CSS) schemes can significantly improve the sensing accuracy of CR networks by exploiting multiuser spatial diversity. However, the cooperative gain can be impacted by factors such as the detection performance of each secondary user (SU) and the fusion techniques used to combine the secondary users’ decisions. Moreover, CSS incurs cooperation overhead that may deteriorate its overall performance. In this chapter, we provide a comprehensive survey on the different factors that contribute to the efficient design of CSS schemes for cognitive radio networks. We specifically focus on the elements of cooperative sensing that can leverage the achievable cooperative gain, limit the cooperation overhead, or provide trade-off between the gain and overhead such as the number of channels sensed in each sensing period, the selection of secondary users, the selection of the fusion scheme, and the correlation between the cooperating secondary users. We also highlight key open research challenges in cooperative spectrum sensing.

Lamiaa Khalid, Alagan Anpalagan

13. Application-Aware Spectrum Sharing

In this chapter, an application-aware spectrum sharing and allocation problem for cellular systems with multiple frequency bands is presented. Mobile users are categorized based on applications running on their devices. They could be either delay-tolerant or real-time applications which are approximated by logarithmic utility functions and sigmoidal-like utility functions, respectively. The objective is to share spectrum resources from multiple base stations with different frequency bands according to a utility proportional fairness policy. This policy guarantees no user is dropped, i.e., allocated zero resource. Additionally, it ensures that mobile users with real-time applications are given priority in resource allocation to achieve higher overall user satisfaction with the available shared resources. Hence, this problem is casted as a convex optimization problem to ensure optimality and the existence of a tractable global optimal solution. Using optimization techniques, e.g., duality and Lagrange multipliers, a distributed spectrum sharing and allocation algorithm is constructed. This algorithm is tested for convergence in different traffic conditions. Based on the convergence analysis, a robust resource allocation and sharing algorithm is developed to allocate the optimal resources for high-traffic situations where conventional resource allocation algorithms fail to converge. Additionally, this algorithm provides the option of traffic-dependent pricing for network providers. This pricing approach can be used to flatten the network traffic and decrease cost per bandwidth for mobile users. The simulation results of the performance of this robust optimal algorithm are explored for a single-carrier and two-carrier scenarios.

Ahmed Abdelhadi, Charles Clancy

14. Autonomous Spectrum Sharing by Well-Designed Games

From static spectrum allocation to nowadays more liberated policies such as spectrum refarming or even opportunistically exploiting the so-called spectrum holes or “white spaces,” we have witnessed many changes all over the world regarding how spectrum is being allocated. The message is clear, and that spectrum allocation needs to be more dynamic and adaptive to the environment and applications. However, dynamic spectrum sharing and access is complicated in many ways. Firstly, it requires global knowledge of channel states for all communication links in the entire network and, secondly, the required large-scale optimization would be computationally prohibitive to achieve, not to mention that channel states vary over time as well. Importantly, there is a strong desire that such dynamic spectrum sharing be realized by a large number of uncoordinated mobile radios in a distributed and autonomous fashion. This is the focus of this chapter which discusses game-theoretic methods for self-optimization of cognitive mobile radios in spectrum sharing. The chapter will begin by reviewing the not-so-flexible spectrum management in cellular networks and then covering the topics of using forward-looking games in spectrum allocation. A major result is that autonomous spectrum sharing leading to spectral-efficient solutions is shown possible by well-designed games requiring only local channel knowledge at individual mobile radios and such interactive self-optimization can also be employed under the hierarchical spectrum sharing model in which primary spectrum owners are present and need to be protected.

Jie Ren, Kai-Kit Wong, Muhammad R. A. Khandaker

15. Spectrum Sensing in Multi-antenna Cognitive Radio Systems via Distributed Subspace Tracking Techniques

Among the many different techniques that have been suggested for spectrum sensing, the eigenvalue-based spectrum sensing (EBSS) techniques exhibit some important advantages. Specifically, they can operate in a totally blind manner while they offer remarkably improved performance for specific types of signals, especially when compared to energy-based methods. Until recently, most of the cooperative EBSS techniques that could be found in the literature were batch and centralized ones, thus suffering from limitations that render them impractical in several cases. Practical cooperative adaptive versions of typical EBSS techniques, which could be applied in a completely distributed manner, have been proposed very recently. The aim of this chapter is (a) to briefly review existing cooperative EBSS techniques of the batch and centralized type and (b) to present in more detail adaptive and distributed versions of typical EBSS techniques. Focusing on the latter case, at first, we present adaptive EBSS techniques for the maximum eigenvalue detector (MED), the maximum-minimum eigenvalue detector (MMED), and the generalized likelihood ratio test (GLRT) scheme, respectively, for a single-user (noncooperative) case. Then, a distributed subspace tracking method is presented which enables the cooperating nodes to track the joint subspace of their received signals. Based on this method, cooperative distributed versions of the adaptive EBSS techniques have been developed that overcome the limitations of the previous batch centralized approaches. Numerical results show that the distributed techniques exhibit good performance, even though they require reduced computational complexity compared to their batch and centralized counterparts.

Christos G. Tsinos, Kostas Berberidis

16. Cognitive Management Strategies for Dynamic Spectrum Access

The Cognitive Radio (CR) paradigm represents an innovative solution to mitigate the spectrum scarcity problem. Enabling a Dynamic Spectrum Access (DSA), it conciliates the existing conflict between the ever-increasing spectrum demand and the currently inefficient spectrum utilization. The basic idea of DSA is to provide proper solutions that allow sharing radio spectrum among several radio communication systems and optimize the overall spectrum utilization. The first part of this chapter gives a general overview of the CR concept to enable DSA, whereas the second part of the chapter addresses the problem of modeling a cognitive management framework with innovative strategies for spectrum management in different scenarios. The presented framework is able to characterize the environment dynamicity through long-term predictions based on the so-called belief vector. This demonstrates that a reliable characterization of the radio environment that combines awareness of its surrounding with a statistical evaluation of the system dynamics in terms of traffic generation patterns is able to guarantee an efficient utilization of the available spectrum resources. From a methodological point of view, the development and assessment of the proposed cognitive management framework involves an analytical study and a real-time platform implementation.

A. Raschellà, L. Militano, G. Araniti, A. Orsino, A. Iera

17. Full-Duplex WiFi Networks

The device in conventional half-duplex WiFi networks cannot perform carrier sensing while in data transmission; thus it suffers from long collision duration. To mitigate this problem, this chapter introduces full-duplex (FD) technology into WiFi networks. A novel CSMA/CD protocol design is first presented for single-channel FD-WiFi, which facilitates continuous carrier sensing and transmission suspension. The network throughput performance is comprehensively analyzed by considering possible sensing errors (i.e., false alarm and miss detection) due to self-interference, and simulation results verify the performance analysis and the effectiveness of CSMA/CD protocol. Then the protocol for multi-channel FD-WiFi is provided, where the CSMA/CD protocol for accessing a certain channel is modified by adopting a contention window adjustment rule, and a distributed channel selection strategy is proposed based on the best-response algorithm. Simulation results indicate the performance improvement of multi-channel FD-WiFi protocol design.

Liwei Song, Yun Liao, Lingyang Song

18. Mobile Data Offloading Through Third-Party Wi-Fis: Association Rules and Incentive Mechanisms

WiFi offloading is regarded as one of the most promising techniques to deal with the explosive increasing data usage over the existing 4G cellular network due to its high quality of service, high data transmission rate, and low requirement on devices. In this chapter, we investigate two key issues, i.e., association rules and incentive mechanisms, for data offloading through third-party WiFi access points (APs) in a cellular mobile system. Firstly, by assuming the data offloaded through the third-party WiFi AP is charged based on usage, we formulate the user association problem as an utility maximization problem from the cellular operator’s perspective. By considering whether the successive interference cancelation (SIC) decoders are available at the BS and/or the WiFi AP, different utility functions are considered. Then, the optimal user association rules are derived for each case when the number of users is large. Secondly, incentive mechanisms to motivate WiFi APs to provide data offloading services are studied. In particular, a salary plus bonus-based incentive mechanism is proposed. Under the proposed incentive scheme, WiFi APs are rewarded not only based on the amount of offloaded data but also based on the quality of the offloading service. The interactions between the WiFi APs and the mobile network operator are investigated using Stackelberg game.

Xin Kang, Sumei Sun

19. Resource Allocation in Spectrum-Sharing Cognitive Heterogeneous Networks

Cognitive radio-enabled heterogeneous networks are an emerging technology to address the exponential increase of mobile traffic demand in the next-generation mobile communications. Recently, many technological issues such as resource allocation and interference mitigation pertaining to cognitive heterogeneous networks have been studied, but most studies focus on maximizing spectral efficiency. This chapter introduces the resource allocation problem in cognitive heterogeneous networks, where the cross-tier interference mitigation, imperfect spectrum sensing, and energy efficiency are considered. The optimization of power allocation is formulated as a non-convex optimization problem, which is then transformed to a convex optimization problem. An iterative power control algorithm is developed by considering imperfect spectrum sensing, cross-tier interference mitigation, and energy efficiency.

Haijun Zhang, Theodoros A. Tsiftsis, Julian Cheng, Victor C. M. Leung

20. Dynamic Spectrum Sharing in Secure Cognitive Radio Networks

In this chapter, the physical layer security in cognitive radio networks with dynamic spectrum sharing is discussed. A brief overview on the security threats in cognitive ratio networks is given. Focusing on the eavesdropping attack, a secrecy problem of the communication between a secondary transmitter-receiver pair in the presence of randomly distributed eavesdroppers is specifically investigated. The dynamic transmit power control is adopted at the secondary-user transmitter to ensure that the spectrum sharing does not harm the primary network. Depending on the knowledge of the channel and the eavesdropper locations, four secure transmission schemes with dynamic spectrum sharing are introduced. A comprehensive performance analysis of each scheme is given. Moreover, the optimal design of the transmission scheme that maximizes the secrecy throughput subject to the secrecy constraint and the reliability constraint is derived. Numerical illustrations on the performance comparison between different schemes are also presented.

Biao He, Xiaoming Xu, Vincent K. N. Lau, Weiwei Yang

21. Heterogeneous Statistical QoS Provisioning Over Cognitive-Radio Based 5G Mobile Wireless Networks

As one of the critical techniques to support the multimedia services over mobile wireless networks, the statistical quality of service (QoS) technique has been proved to be effective in statistically guaranteeing delay-bounded video transmissions over the time-varying wireless channels. In the meantime, the full-duplex spectrum sensing (FD-SS) has been widely recognized as the promising candidate technique for maximizing the spectrum efficiency while provisioning the heterogeneous statistical QoS guarantees over cognitive radio-based 5G mobile wireless networks. However, due to the heterogeneity caused by different scenarios and applications of the multimedia traffics over CRNs, supporting diverse delay-bounded QoS guarantees for cognitive radio networks imposes many new challenges not encountered before. To effectively overcome the aforementioned problems, in this book chapter, we propose the heterogeneous statistical QoS provisioning schemes by applying the multiple-input-multiple-output generalized frequency division multiplexing (MIMO-GFDM) techniques to implement the FD-SS-based multimedia services in CRNs. In particular, under the Nakagami-m wireless channels, we derive the MIMO-GFDM-based physical (PHY)-layer model and the self-interference cancelation model. We develop the MIMO-GFDM-based FD-SS schemes and derive the miss-detection and false-alarm probabilities. Under the heterogeneous statistical QoS constraints, we develop the Markov chain model to characterize the aggregate effective capacity under the optimal power allocation policies using our proposed MIMO-GFDM architecture over FD-SS CRNs. Also conducted is a set of simulations which validate our proposed schemes, evaluate their performances, and show that our proposed schemes can outperform the other existing solutions under the heterogeneous statistical delay-bounded QoS constraints over cognitive radio-based 5G mobile wireless networks.

Xi Zhang, Jingqing Wang

22. Spectrum-Aware Mobile Computing Using Cognitive Networks

With the advent of mobile cloud computing, the expectation of the mobile users for anywhere, anytime, content-rich experience will see a significant increase. The users’ expectation on quality of experience for content-rich applications can only be met through offloading computationally intensive application tasks to a remote cloud since mobile devices are still constrained by their battery power. This, however, leads to an increase in mobile web traffic. The success of computation offloading techniques, therefore, depends on being able to effectively trade-off resource usage at the mobile device against efficiently managing the spectrum for mobile computing. Hence it is essential for cloud offloading techniques to take advantage of recent advances in cognitive networking and spectrum-aware scheduling of application components. The convergence of cognitive networking and spectrum-aware mobile computing is propelling research in this area. The current state-of-the-art includes techniques that offload application data using all viable multiple radio interfaces (e.g., WiFi, LTE, etc.) in multi-RAT-enabled devices, while being adaptive to the conditions of the mobile network. This chapter presents a survey of the existing spectrum-aware mobile computing techniques and proposes a vision for the future for a 5G-enabled, cognitive mobile computing platform. Implementation setups using real data measurements from an HTC phone running multicomponent applications and using different cloud servers such as Amazon EC2 and NSFCloud over LTE and WiFi are also discussed.

S. Eman Mahmoodi, K. P. Subbalakshmi, R. N. Uma

23. Cognitive Radio Network Security

Cognitive radio networks (CRNs) emerge as a possible solution to increase spectrum efficiency by allowing cognitive radios (CRs) to access spectrum in an opportunistic manner. Although security in CRNs has received less attention than other areas of CR technology, the need for addressing security issues is evidenced by two facts. First, as for any other type of wireless network, an open channel is used for communications that can easily be accessed by attackers. On the other hand, the particular attributes of CRNs raise new opportunities to malicious users, which can disrupt network operation. In this chapter, we provide an overview of those threats that are specific to CRNs. We classify them according to the layer in which the attacks are performed, give an insight of their impact on the network performance, and describe potential countermeasures that can be used to prevent them or mitigate their effect.

Olga León, K. P. Subbalakshmi

24. Physical Layer Coexistence: WLAN/Radar Case Study

Spectrum sharing of 802.11 wireless local area network (WLAN) and radars operating in co-/adjacent channel scenarios (notably 5 GHz) is a problem of considerable importance that requires new innovations. The spectrum sharing explored in this chapter is based on unilateral action by Wi-Fi networks to prevent unacceptable interference to incumbent radar and also mitigating the interference from radar to Wi-Fi. Specifically, the ability of a single Wi-Fi network inside the exclusion region is to speedily detect radar operation and to subsequently switch to a clear channel as a means of protecting them. The approach is relied on the opportunistic use of naturally occurring random quiet/idle periods in a Wi-Fi network employing distributed coordination function (DCF) to detect the presence of a radar using energy detection. Moreover, the Wi-Fi systems outside the exclusion region are modified to mitigate the interference from a pulsed search radar such that the WLAN continues to operate with no noticeable performance degradation. The radar pulse detection is required to mitigate the radar interference.

Morteza Mehrnoush, Sumit Roy

Cognitive Radio Resource Management

Frontmatter

25. System Power Minimization in Non-contiguous Spectrum Access

Wireless transmission using non-contiguous chunks of spectrum is becoming increasingly important due to a variety of scenarios such as secondary users avoiding incumbent users in TV white space, anticipated spectrum sharing between commercial and military systems, and spectrum sharing among uncoordinated interferers in unlicensed bands. multichannel multi-radio (MC-MR) platforms and non-contiguous orthogonal frequency division multiple access (NC-OFDMA) technology are the two commercially viable transmission choices to access these non-contiguous spectrum chunks. Fixed MC-MRs do not scale with increasing number of non-contiguous spectrum chunks due to their fixed set of supporting radio front ends. NC-OFDMA allows nodes to access these non-contiguous spectrum chunks and put null subcarriers in the remaining chunks. However, nulling subcarriers increases the sampling rate (spectrum span) which, in turn, increases the power consumption of radio front ends. Our work characterizes this trade-off from a cross-layer perspective, specifically by showing how the slope of ADC/DAC’s power consumption versus sampling rate curve influences scheduling decisions in a multi-hop network. Specifically, we provide a branch and bound algorithm-based mixed integer linear programming solution that performs joint power control, spectrum span selection, scheduling, and routing in order to minimize the system power of multi-hop NC-OFDMA networks. We also provide a low-complexity (O(E2M2)) greedy algorithm where M and E denote the number of channels and links, respectively. Numerical simulations suggest that our approach reduces system power by 30% over classical transmit power minimization based cross-layer algorithms.

Muhammad Nazmul Islam, Narayan B. Mandayam, Ivan Seskar, Sastry Kompella

26. Sequential Learning and Decision-Making in Dynamic Channel Access and Transmission Scheduling

Making judicious channel access and transmission scheduling decisions is essential for improving performance (delay, throughput, etc.) as well as energy and spectral efficiency in multichannel wireless systems. This problem has been a subject of extensive study in the past decade, and the resulting dynamic and opportunistic channel access schemes can bring potentially significant improvement over traditional schemes. In this chapter, a couple of classical settings and problems for this decision-making question in cognitive radio networks, namely, multiuser, single-channel model and single-user, multichannel model, as well as their solutions, will be surveyed first. Toward making such studies more practical, we point to a common and severe limitation of these dynamic schemes in that they almost always require some form of a priori knowledge of the channel statistics. On the other hand, what is often available to the decision-maker is a rather rich stream of network data that can jointly describe channel conditions.Then a natural remedy is to consider a learning framework, which has also been extensively studied in the same context, but a typical learning algorithm in this literature seeks only the best static policy (i.e., to stay in the best channel), with performance measured by weak regret, rather than learning a good dynamic channel access policy. There is thus a clear disconnect between what an optimal channel access policy can achieve with known channel statistics that actively exploits temporal, spatial, and spectral diversity and what a typical existing learning algorithm aims for, which is the static use of a single-channel devoid of diversity gain.In this chapter, this gap is bridged by designing learning algorithms that track known optimal or suboptimal dynamic channel access and transmission scheduling policies via using collected observations following the made decisions, thereby yielding performance measured by a form of strong regret, the accumulated difference between the reward returned by an optimal solution when a priori information is available and that by our online algorithm. We do so in the context of two specific algorithms that appeared in [1] and [2], respectively, the former for a multiuser single-channel setting and the latter for a single-user multichannel setting. In both cases we show that our algorithms achieve sublinear regret uniform in time and outperform the standard weak-regret learning algorithms.

Yang Liu, Mingyan Liu

27. Energy-Efficient Design in Cognitive MIMO Systems

The energy-efficient design for TDMA (time-division multiple access) MIMO (multiple-input multiple-output) cognitive radio (CR) networks can be treated as the joint optimization over both the time resource and the transmit precoding matrices to minimize the overall energy consumption. Compared with the traditional MIMO networks, the challenge here is that the secondary users (SUs) may not be able to obtain the channel state information (CSI) to the primary receivers. The corresponding mathematical formulation turns out to be non-convex and thus of high complexity to solve in general. This chapter covers both the transmission choices for each SU: single-data-stream transmission and multiple-data-stream transmission. Fortunately, by applying a proper optimization decomposition, it can be shown that the optimal solution can be found in polynomial time in both cases. In practical wireless system, the time is usually allocated in the unit of slot. Moreover, by exploring the special structure of this particular problem, it can be shown that the optimal time slots allocation can be obtained in polynomial time with a simple greedy algorithm. Simulation results show that the energy-optimal transmission scheme adapts to the traffic load of the secondary system to create a win-win situation where the SUs are able to decrease the energy consumption and the PUs experience less interference from the secondary system. The effect is particularly pronounced when the secondary system is underutilized.

Liqun Fu

28. Collaborative Spectrum Trading and Sharing for Cognitive Radio Networks

Spectrum trading is one of the most promising approaches to enabling dynamic spectrum access (DSA) in cognitive radio networks (CRNs). With this approach, unlicensed users (a.k.a. secondary users) offer licensed users (a.k.a. primary users) with monetary rewards or improved quality of services (QoSs) in exchange for spectrum access rights. In this chapter, we present a comprehensive introduction to spectrum trading. First, we provide a brief introduction to DSA and CRNs as the background and motivation for the spectrum trading. Then, we present various state-of-the-art spectrum trading mechanisms for spectrum sharing. Finally, by analyzing various design issues in these mechanisms, we introduce the concept of service-oriented spectrum trading and offer a novel collaborative network architecture, called a cognitive mesh assisted network, to effectively utilize unused licensed/unlicensed spectrums with high spectral efficiency. We expect that this chapter provides readers with basic understanding on spectrum trading technology and foster future research initiatives.

Xuanheng Li, Haichuan Ding, Yuguang Fang, Miao Pan, Pan Li, Xiaoxia Huang, Yi Sun, Savo Glisic

29. Cognitive Radio Networks for Delay-Sensitive Applications: Games and Learning

We have witnessed an explosion in wireless video traffic in recent years. Video applications are bandwidth intensive and delay sensitive and hence require efficient utilization of spectrum resources. Born to utilize wireless spectrum more efficiently, cognitive radio networks are promising candidates for deployment of wireless video applications. In this chapter, we introduce our recent advances in foresighted resource allocation mechanisms that enable multiuser wireless video applications over cognitive radio networks. The introduced resource allocation mechanisms are foresighted, in the sense that they optimize the long-term video quality of the wireless users. Due to the temporal coupling of delay-sensitive video applications, such foresighted mechanisms outperform mechanisms that maximize the short-term video quality. Moreover, the introduced resource allocation mechanisms allow wireless users to optimize while learning the unknown dynamics in the environment (e.g., incoming traffic, primary user activities). Finally, we introduce variations of the mechanisms that are suitable for networks with self-interested users. These mechanisms ensure efficient video resource allocation even when the users are self-interested and aim to maximize their individual video quality. The foresighted resource allocation mechanisms introduced in this chapter are built upon our theoretical advances in multiuser Markov decision processes, reinforcement learning, and dynamic mechanism design.

Yuanzhang Xiao, Mihaela van der Schaar

30. MIMO-Empowered Secondary Networks for Efficient Spectrum Sharing

Cognitive radio (CR) and multiple-input multiple-output (MIMO) are two independent physical layer technologies that have made significant impact on wireless networks. In particular, CR operates on the channel level to exploit efficiency across spectrum dimension, while MIMO operates within the same channel to exploit efficiency across spatial dimension. In this chapter, we explore MIMO-empowered CR technique to enhance spectrum access in wireless networks. Specially, we study how to apply MIMO-empowered CR for both interweave and underlay paradigms Underlay paradigm in multi-hop network environment. With MIMO interference cancelation (IC) capability, we first show how multiple secondary links achieve simultaneous transmission on the same channel under the interweave paradigm. Next, we show how secondary networks achieve simultaneously transmission with the primary network on same channel to achieve transparent coexistence under the underlay paradigm. Through rigorous mathematical modeling, problem formulation, and extensive simulation results, we find that MIMO-empowered CR can offer significant improvement in terms of spectrum access and throughput performance under both interweave and underlay paradigms.

Xu Yuan, Cunhao Gao, Feng Tian, Yi Shi, Y. Thomas Hou, Wenjing Lou, Wade Trappe, Scott F. Midkiff, Jeffrey H. Reed, Sastry Kompella

31. Coalition Formation Games for Cooperative Spectrum Sensing in Cognitive Radio Networks

Cooperative spectrum sensing is an effective technique to enhance the sensing performance and improve the spectrum efficiency in cognitive radio networks (CRNs). This chapter considers a CRN with multiple primary users (PUs) and multiple secondary users (SUs) and presents two cooperative spectrum sensing and access (CSSA) schemes. The first CSSA scheme allows each SU to sense one channel and is formulated as a hedonic coalition formation game, where each coalition is composed of the SUs that sense on the same channel. The value function of each coalition and the utility function take into account both the sensing accuracy and the energy consumption. The algorithms for decision node selection in each coalition and SU decision-making are proposed to obtain a final network partition, which is proved to be both Nash stable and individually stable. This chapter then focuses on a more general scenario, where each SU can simultaneously sense multiple channels based on its traffic demand. The traffic demand-based CSSA scheme is formulated as a nontransferable utility (NTU) overlapping coalitional game, where each SU implements a cooperation strategy based on its expected payoff. Two algorithms, namely overlapping coalition formation (OCF) and sequential coalition formation (SCF), are proposed to obtain a coalition structure. The OCF algorithm guarantees the stability of the coalition structure, while the SCF algorithm reduces the computational complexity and information exchange. Simulation results show that the proposed algorithms significantly enhance the network throughput.

Yong Zhou, Zhiyu Dai, Xiaolei Hao, Man Hon Cheung, Zehua Wang, Vincent W. S. Wong

32. Contract-Based Secondary Spectrum Trading

Secondary spectrum trading is a promising solution to address the economic incentive issue in dynamic spectrum access, by allowing primary users (PUs) to sell the underutilized spectrum to secondary users (SUs) for the temporary access. This chapter studies a monopoly secondary spectrum market with a single PU (seller) and multiple heterogeneous SUs (buyers), where SUs have different preferences for spectrum access quality. The key focus is to study the optimal trading mechanism for the PU (that maximizes his profit) under information symmetry or asymmetry, depending on whether the preference of each SU is public information (hence can be observed by the PU) or private information (hence cannot be observed by the PU). The key solution idea to this PU-profit maximization problem is a contract-based spectrum trading mechanism, in which the PU offers a list of quality-price combinations (called a contract) for SUs, and each SU chooses the proper quality and price according to his (private or public) preference information. The optimal design for this contract-based trading mechanism consists of two steps: (i) analyze the incentive compatibility (IC) and individual rationality (IR) for feasible contracts, which guarantee the truthful information disclosure of SUs, and (ii) derive the optimal contract that maximizes the PU’s profit under each information scenario, based on the IC and IR analysis in the first step. Simulations show that the optimal contract can increase the PU’s payoff significantly.

Lin Gao, Xinbing Wang, Youyun Xu, Qinyu Zhang

33. Adaptive Learning in Cognitive Radio

Machine learning is a powerful tool for cognitive radio users to learn its sensing and transmission strategy from the experience. This chapter provides a brief introduction to a variety of machine-learning techniques. The basic setup of machine learning, as well as the dichotomy, is explained. Then, the supervised, unsupervised, semi-supervised, and reinforcement learning techniques are briefly discussed. The single-agent learning is then extended to the case of multiagent learning. Then, the machine-learning techniques are applied in various cases of machine learning, such as channel selection and routing.

Husheng Li

34. Spatial Spectrum Access Game

A key feature of wireless communications is the spatial reuse of wireless resources. However, such a spatial aspect is relatively less understood for the purpose of designing efficient spectrum sharing mechanisms. In this chapter, we propose a framework of spatial spectrum access games, where we model fairly general spatial interference relationships among users as directed interference graphs. We show that a pure Nash equilibrium exists for the two classes of games: (1) any spatial spectrum access games on directed acyclic graphs and (2) any games satisfying the congestion property on directed trees and directed forests. We identify the graphical structures under which the spatial spectrum access games have pure Nash equilibria and further show that under mild conditions, the spatial spectrum access games with random backoff and Aloha channel contention mechanisms on undirected graphs are potential games and have pure Nash equilibria as well. We also quantify the price of anarchy of the general spatial spectrum access game. We then propose a distributed learning algorithm, which only utilizes users’ local observations to adaptively adjust the spectrum access strategies. We show that the distributed learning algorithm can converge to an approximate mixed strategy Nash equilibrium for any spatial spectrum access games. We further generalize the spatial spectrum access game framework to accommodate the physical interference model. Numerical results demonstrate that the distributed learning algorithm achieves significant performance improvement over the benchmark algorithms.

Xu Chen, Jianwei Huang

Cognitive Cellular Networks

Frontmatter

35. Coexistence of Heterogeneous Cellular Networks

Many wireless standards for cellular networks (e.g., IEEE 802.11af and IEEE 802.22) have been developed or are currently being developed for enabling opportunistic access to spectrum using cognitive radio (CR) technology. When heterogeneous cellular networks that are based on different wireless standards operate in the same spectrum band, coexistence issues can potentially cause major problems. Enabling coexistence via direct coordination between heterogeneous cellular networks is very challenging due to incompatible MAC/PHY designs of coexisting networks, the conflict of interest issues, as well as customer privacy concerns. This chapter introduces a number of research problems that may arise in the context of coexistence of heterogeneous cellular networks, namely, the hidden terminal problem, the multichannel broadcast problem, the spectrum sharing problem, and the channel contention problem. This chapter also identifies the major challenges for addressing these problems, proposes the guidelines for devising potential solutions, and provides results of performance evaluation on the proposed solutions.

Kaigui Bian, Jung-Min Jerry Park

36. Device-to-Device Communications over Unlicensed Spectrum

Device-to-device (D2D) communication, which enables direct communication between nearby mobile devices, is an attractive add-on component to improve spectrum efficiency and user experience by reusing licensed cellular spectrum. Nowadays, LTE-unlicensed (LTE-U) emerges to extend the cellular network to the unlicensed spectrum to alleviate the spectrum scarcity issue. In this chapter, we propose to enable D2D communication in unlicensed spectrum (D2D-U) as an underlay of the uplink cellular network to further boom the network capacity. A sensing-based protocol is designed to support the unlicensed channel access for both LTE and D2D users, based on which we investigate the subchannel allocation problem to maximize the sum-rate of LTE and D2D users while taking into account their interference to the existing Wi-Fi systems. Specifically, we formulate the subchannel allocation as a many-to-many matching problem with externalities and develop an iterative user-subchannel swap algorithm. Analytical and simulation results show that the proposed D2D-U scheme can significantly improve the network capacity.

Hongliang Zhang, Yun Liao, Lingyang Song

37. RF-Based Energy Harvesting Cognitive Cellular Networks

Recently, fundamental research has demonstrated great potentials of integrating radio frequency (RF) energy harvesting techniques into cognitive cellular networks (CCNs). Such an integration can improve spectrum utilization and energy efficiency of wireless communication services. In CCNs with RF energy harvesting capability, when cellular base stations, i.e., primary transmitters, transmit signals to their mobile devices, secondary users (SUs) can harvest energy from the cellular channel, i.e., the primary channel, and store the energy in their batteries. Then, when the cellular channel becomes idle, the SUs can use the harvested energy to transmit data to their receivers. As such, we can utilize not only the available spectrum when the channel is idle but also energy scavenging when the channel is busy. This chapter first presents an overview of RF-based energy harvesting CCNs. Then, limitations are discussed, and some new solutions using ambient backscattering communication techniques are introduced to overcome the limitations. Finally, the chapter concludes with a discussion on the development of such networks and possible research directions.

Dinh Thai Hoang, Dusit Niyato

38. Spectrum Sharing of Drone Networks

Drone networks are aerial base stations that can be used to support cellular networks. The underlay spectrum sharing between the three-dimensional (3D) drone small cells (DSCs) downlink network modeled by a 3D Poisson point process and traditional cellular networks modeled by a 2D Poisson point process is introduced. To maximize the DSC network throughput while satisfying the cellular network efficiency constraint, the optimal density of DSC aerial base stations is discussed. The maximum throughput of the DSC user increases almost linearly with the increase of the DSC outage constraint. Effects of directional transmission on DSC networks is further discussed. Besides density control, power and beam control can also be applied in the spectrum sharing between unmanned aerial vehicle (UAV) network and ground network. With the mobility pattern information of UAVs, the delay-tolerant transmissions can be constructed and multiple transmission modes are implemented to carry various types of traffic. Exploiting cognition capability on mobility, UAV network can provide high quality of information services in the highly dynamic environment with limited resources.

Chiya Zhang, Zhiqing Wei, Zhiyong Feng, Wei Zhang

39. User-Cognizant Scalable Video Transmission over Heterogeneous Cellular Networks

With the increase of mobile video applications in people’s daily life as well as industrial manufacture, such as video streaming, surveillance, and so on, video has been the main service in cellular networks. Operators and service providers are struggling to enhance the mobile video service, while user requirements for abundant, high-definition, and low-delay video have nearly drained the transmission capacity of current networks. Moreover, the large population of user equipments (UEs) exhibit differentiated video demands and various network transmission environments. Traditional networking, which is static and base station (BS) concentric, can hardly deal with these challenges. Thus, adaptive video transmission schemes are needed by jointly considering the interplay among user demand, video source characteristics, and networking. This work focuses on user-cognizant scalable video transmission over heterogeneous cellular networks. The video source is encoded using scalable video coding, which enables dynamic adaption of source information to the requirements of UEs and is suitable for cellular networks in which the transmission link quality varies substantially over space and time. Three novel transmission schemes are proposed, layered digital transmission, layered hybrid digital-analog transmission, and cooperative digital transmission. Leveraging tools from stochastic geometry, a comprehensive analysis is conducted focusing on three key performance metrics: outage probability, high-definition probability, and average distortion. The associated spectrum allocation and video transmission are chosen based on the user-cognizant information, such as the requirements for video service, wireless channel status, and the connections with the BSs. The results show that the proposed user-cognizant transmission schemes can provide a scalable video experience for UEs.

Liang Wu, Wenyi Zhang

40. Precoding and Power Allocation for Two-Tier Heterogeneous Networks

In two-tier heterogeneous networks, the cross-tier interference and co-tier interference significantly affect the network performance. In this chapter, cascaded precoders in orthogonal frequency-division multiplexing systems are investigated to protect macro-cell user equipments (MUEs) from the cross-tier interference caused by co-located small cells and at the same time to satisfy the quality-of-service (QoS) requirements of small-cell user equipments (SUEs). An outer precoder ensures that the signals intended for the SUEs are orthogonal to the MUEs thus avoids the cross-tier interference from the second tier. Moreover, optimal power allocation through an inner precoder at each small base station (SBS) yields better performance of the SUEs and guarantees their QoS requirements. With consideration of the dense deployment of SBSs, an SBS selection algorithm is studied to further reduce the computational complexity. Simulation results demonstrate that the cascaded precoders are effective in mitigating the interference and enhancing the capacity of small cells.

Shengjie Guo, Xiangwei Zhou

41. Distributed Resource Allocation for Network Virtualization

The explosive development of mobile data service makes our lives convenient and efficient. However, due to the limitation of resources and high flexibility of users’ requirements, the resource management and allocation remain challenging. In this chapter, we first overview the development of mobile network. Based on the increasingly complicated mobile network, we analyze the current trends for service architecture, and show the features of distributive control and network virtualization in the future data services. According to the service architecture, game theory is adopted to discuss the distributed behaviors of each service provider and user. We model the resource allocation problem as a hierarchical game, where the strategies for each service provider and each mobile user is proposed to achieve optimal and stable utilities. Finally, we conclude the chapter and put forwards future directions for distributed resource allocation problem in the virtualized data service network.

Huaqing Zhang, Zhu Han

42. Many-to-Many Matching for Distributed Spectrum Trading

In cognitive radio networks, service providers with spare channels can sell the spectrum to those in need of them. The redistribution of the spectrum among service providers reduces the waste of spare spectrum, therefore enhances the spectrum utilization. It also provides the service provider more revenue from the sail of spectrum. Traditional method of spectrum trading mainly based on double auction, which requires an auctioneer, is a trustworthy third-party authority, to centrally enforce a certain spectrum allocation policy. In this chapter, we take a different and new perspective, proposing to use matching as an alternative tool to realize spectrum trading in a distributed way for a free market, which consists of only buyers and sellers, without a trustworthy third-party authority. In this chapter, we will first give a brief introduction of the whole chapter in the first section and then present the fundamentals of the matching theory in the second section. In the third section, matching theory is leveraged in spectrum trading among service providers to decide the spectrum allocation and trading price, the distinctive challenge of spectrum matching compared with conventional matching is analyzed, and a two-stage distributed algorithm is proposed to solve the spectrum matching problem. In the fourth section, we considered a more general case, where multiple channels can be bought by the same service provider, and the spectrum matching algorithm for combinatorial spectrum trading is proposed to enable the spectrum allocation. For both algorithms, the proposed algorithm can achieve a Nash-stable matching, and the simulation shows that the proposed algorithms achieve good performance compared with centralized schemes.

Jin Zhang, Linshan Jiang, Haofan Cai, Yanjiao Chen

43. Cooperation in Cognitive Cellular Heterogeneous Networks

A recent drive by mobile network operators to mitigate the network capacity crunch and to improve indoor coverage involves the development of cellular heterogeneous networks. Cellular heterogeneous networks consist of the existing macrocells plus shorter range cells referred to as small cells. Coexistence of macrocells and small cells sharing the same spectrum represents a special case of cognitive networking, where small cells and their users can be viewed as secondary users, whereas the macrocell and its users act as the primary legacy users. Unlike the traditional listen-before-talk concept in cognitive radio spectrum sensing, this chapter presents techniques for utilizing inherent Radio Link Control (RLC) messages and feedback information in existing cellular systems. It develops a more-advanced cognitive approach that takes into account actual primary user’s interference tolerance and facilitates more efficient spectrum sharing. The chapter first introduces the idea of implicit cooperation through the use of inherent feedback information in cellular heterogeneous networks. Explicit cooperation is then discussed in the chapter before introducing the concept of cooperation in hybrid-access cellular heterogeneous networks as well as in dense enterprise femtocell deployments. The chapter concludes by summarizing the most recent trend of integrated access between both cellular and wireless local area network (WLAN) interfaces at small cells for traffic offloading and for improving network capacity.

Ahmed R. Elsherif, Hesham M. Elmaghraby, Zhi Ding

44. Cognitive Multihoming System for Enhanced Cellular Experience

Cellular network service providers are facing acute spectrum shortage due to surging mobile data traffic demand. On the contrary, spectrum measurement studies reveal that large part of the licensed spectrum is being underutilized. In this chapter, a cognitive multihoming (CM) framework is presented for the cellular network service providers to meet the escalating data demands and provide enhanced quality of service (QoS) to the users. In CM, the conventional cellular base stations (BS) are enabled with cognitive radio (CR) access functionality. Thus, these CR-enabled BS transmit simultaneously to the users over the licensed cellular bands as well as over the CR bands. Communication over CR incurs lower transmission cost at the expense of higher energy consumption due to frequent channel sensing. On the other hand, communication over licensed cellular bands is expensive due to its licensing premium. Performance of CM is analyzed in two scenarios. Multiple real-time (RT) and non-RT users requesting for unicast downlink content are considered in the first scenario, while the second scenario considers multiple users requesting for scalable video content from the network. For the two scenarios, optimal resource allocation and call admission control algorithm are presented. Through the performance results presented in this chapter, it is inferred that the CM strategy can enable the cellular network providers to serve a higher number of users as well as improve the user’s QoS in terms of reduced service cost.

Satyam Agarwal, Swades De

Spectrum Policy and Cognitive Radio Standards

Frontmatter

45. Spectrum Policy and Cognitive Radio Standards

Standards, fundamental in many areas of technology, are particularly vital in communications as they provide the foundations upon which radio devices, network elements, and other essential parts and functionalities in the communication chain can talk to each other. Moreover, standards are of even further accentuated importance in CR and spectrum sharing scenarios, as such scenarios imply direct or indirect interaction involving a wider range of stakeholders, including regulators – implying different forms of standards (e.g., regulatory conformance standards). Given such observations, this chapter addresses standardization in the context of CR and spectrum sharing, particularly aiming to provide an opening and front matter to the more detailed coverage of some particular standards that are addressed in later chapters of this section of the book. In addition to providing this introductory material, this chapter addresses the broader scope of a number of relevant standards that are not detailed in the dedicated chapters. These include the TVWS regulatory conformance standard ETSI EN 301 598, the IEEE DySPAN-SC and IEEE 1900 series standards on various aspects of spectrum sharing and dynamic spectrum access, the IEEE 802.15.4m wireless personal area networks in TVWS standard, and the ECMA 392 TVWS MAC/PHY standard.

Oliver Holland

46. IEEE 802.11af Wi-Fi in TV White Space

TV White Space is expected to make the radio resource utilization more flexible by adopting spectrum sharing concept to TV band. IEEE 802.11af is the world-first standard of PHY and MAC for operation of wireless LAN in the TV band. As radio regulations, such as spectrum mask and maximum transmission power, are different depending on countries, it is not so simple to implement the standard. Therefore, it is necessary for the standard to conduct investigation on implementation of the standard by prototyping according to the specific regulations, measurements of performances in various use scenarios, and discussion on its feasibility. This chapter covers from the IEEE 802.11af standard to its prototypes and field experiments. It is believed that the chapter helps to understand not only the specific standard but also the concept and practical experiences of TV White Space operations. The prototyping was based on radio regulations of US FCC and UK Ofcom. The field experiments introduced in the chapter were conducted under the TV White Space Pilot organized by UK Ofcom. Measurements were taken through the experiments in indoor and outdoor environments and analyzed to show feasibility and features of the standard and potentials of TV White Space utilization for IP communications. Note that this chapter includes several world-first achievements on IEEE 802.11af related R&D.

Kentaro Ishizu, Keiichi Mizutani, Takeshi Matsumura, Zhou Lan, Hiroshi Harada

47. Cognitive Radio: The Need to Align Regulations with Technology

Cognitive radio holds an interesting promise for improved utilization of the radio spectrum. However, there is a considerable degree of uncertainty regarding the potential application of cognitive radio. One of the reasons for this uncertainty is the need for changes in the regulatory regime to allow for more dynamic forms of spectrum access. In addressing the necessary changes in regulations, the regulator should be well aware of the perspective of the entrepreneur. Eventually it is the entrepreneur who invests in CR technology and thereby realizes the goal of improved utilization of the radio spectrum.This chapter addresses the relationship between the regulations and the CR technology. Both the regulations and the CR technology will pose limitations on the possible business cases. It further proposes a way forward to come to a successful exploitation of CR technology in which the objectives of both the entrepreneur and the regulator can be realized.

Peter Anker

48. Spectrum Sharing Policy at Global Level

Spectrum sharing developments exploiting cognitive radio technology will change the traditional spectrum management models, which calls for discussions and decisions in the policy making domain. Efficient governance of natural resources such as the radio spectrum requires actions in different policy making levels ranging from national level all the way to the international level. This chapter will introduce spectrum sharing related policy making activities in the global level presenting the actions taken at the International Telecommunication Union Radiocommunication (ITU-R) sector. We will introduce the groups within ITU-R and their related activities and introduce cognitive radio and spectrum sharing related terminology developed at the ITU-R. Special emphasis is put to the ITU-R studies on cognitive radio systems (CRS) with a set of capabilities for obtaining knowledge, decision-making and adjustment, and learning, to enhance the efficiency of spectrum use. We will introduce the CRS capabilities and present scenarios and applications where vertical and horizontal spectrum sharing using CRS capabilities could take place. Other sharing related activities at the ITU-R are also presented including spectrum management, spectrum monitoring and spectrum occupancy measurement studies, as well as more general ongoing work on regulatory tools to enable spectrum sharing and CRS from the point view of spectrum management. Finally, a future outlook is given for spectrum sharing policy developments toward the fifth generation (5G) networks.

Marja Matinmikko, Miia Mustonen

49. ETSI-RRS Reconfigurable Radio Systems Standards

The evolution of classical, static Radio Systems toward Reconfigurable Radio Systems is a clear trend in the industry for several reasons – first, the lack of spectral resources forces manufacturer to exploit novel technological trends in order to meet 5G-related promises in terms of quality of service, latency, reliability, etc. Second, the fast evolution and heterogeneous nature of the radio environment combined with an ever-increasing computation power in mobile devices call for new ways of ensuring that the diverse environment is exploited in the best possible way; software reconfigurability is the key to dynamically adapt any target device to the specific needs of its owner through installation of tailored and targeted software components. All this flexibility, however, is useless without access to real-time, reliable context information which feeds decision-making entities in the network and in the mobile device or implemented in a distributed way such that the network and mobile devices participate in the decision-making process. The European Commission has recognized this trend in an early stage and acted correspondingly. ETSI received EC Mandate M/512 “Standardisation Mandate to CEN, CENELEC and ETSI for Reconfigurable Radio Systems” which has led to the development of the Licensed Shared Access Spectrum Sharing solution in ETSI’s Technical Committee Reconfigurable Radio Systems (TC RRS). Furthermore, the new Radio Equipment Directive creates a clear framework for Software Reconfigurable Radio Equipment in Europe. This section details the respective technical solutions and trends as they are currently being developed in ETSI standards.

Markus Mueck

50. Spectrum Sharing Policy in Europe

While the importance of harmonization on spectrum matters in the global scale is well known and the national regulatory authorities possess the power to govern the spectrum use within their territory, the regional level regulatory activities are important to bridge the gap between these two levels. This chapter introduces the spectrum management framework and spectrum sharing initiatives in the regional level in Europe. The roles of major European regulation and standardization authorities are outlined as well as the cooperation framework between these entities. Additionally, the activities promoting spectrum sharing in Europe is discussed. These European spectrum sharing policy developments have addressed the introduction of additional users in a spectrum band considering both individual usage rights and general authorization by developing specifically licensed shared access and TV white space sharing concepts. These sharing concept are introduced on general level as well as on the basis of introducing the underlying regional regulatory and standardization activities that have led to harmonized overall framework, leaving national administrations the freedom to decide on details of the implementation of the concept.

Miia Mustonen, Marja Matinmikko, Jarkko Paavola

51. Novel Regulatory Solutions for Cognitive Radio and Spectrum Sharing in the United States

The regulation of spectrum in the United States is managed by two independent agencies: the National Telecommunications and Information Administration, who is responsible for spectrum used by US government agencies such as the Department of Defense and the National Weather Service, and the Federal Communications Commission, who is responsible for all nonfederal spectrum. Both agencies are mandated by law to maximize the efficient use of spectrum, and toward that end, both agencies are working to share underutilized spectrum to the greatest extent possible. This chapter will explore various initiatives by these two agencies to achieve this objective, both independently and in cooperation.

Lee Pucker

52. Novel Regulatory Solutions for Cognitive Radio and Spectrum Sharing in the UK

More than half (58%) of the spectrum in the UK is shared between different classes of users. Typically, this has been enabled by authorizing individual applications to use specific frequencies at specific locations on a first-come-first-served basis.But technological developments, the focus of this book, have presented Ofcom with promising opportunities to share spectrum more effectively. Ofcom has taken advantage of these technologies within the UHF band (470–790 MHz) in the context of the digitization of terrestrial television. More recently, Ofcom has focused on establishing a systematic way of enabling spectrum sharing in general and has taken preliminary steps in exploring how to share the 3.8–4.2 GHz band.

Toby Youell

53. Spectrum Sharing Policy in the Asia-Pacific Region

In this chapter, we investigate the spectrum measurement results in Asia-Pacific region. Then the spectrum sharing policy in Asia-Pacific region is reviewed in details, where the national projects and strategies on spectrum sharing in China, Japan, Singapore, India, Korea, and Australia are Investigated. Then, we introduce the spectrum sharing test-bed developed in China, which is a cognitive radio-enabled TD-LTE test-bed utilizing TV white space (TVWS). This chapter provides a brief introduction to the spectrum sharing mechanism and policy in Asia-Pacific region.

Zhiyong Feng, Zhiqing Wei

54. IEEE 802.22/802.22.3 Cognitive Radio Standards: Theory to Implementation

In an earlier book chapter published in the Book titled Opportunistic Spectrum Sharing and White Space Access: The Practical Reality, First Edition (Holland et al., 1st edn. Wiley, 2015), we provided an in-depth overview of the IEEE 802.22 standard for cognitive wireless regional area networks. The discussion featured the motivation and the need for that standard, white space regulations around the world, in-depth analysis of the IEEE 802.22-2011 standard along with a brief overview of the new features present in the amendment to the IEEE 802.22 standard.IEEE 802.22 standard for wireless regional area networks (WRANs), also known as Wi-FARⓇ (IEEE Std 802.22-2011) proposes to use the unused television band channels (the so called white spaces) in the VHF and the UHF bands to provide fixed and nomadic, high-throughput, long-range communications. Applications of this standard include remote and rural broadband Internet access, Frugal 5G for e-Education, e-Health, e-Banking, e-Payments, ship to shore communications, homeland security, border protection and surveillance, environment monitoring, smart grid applications such as supervisory control and data acquisition (SCADA), as well as low latency applications such as protective relaying at a future date. The IEEE Std. 802.22-2011 has been approved by ISO, and its interoperability testing is being carried out by the WhiteSpace Alliance under the commercial brand name of Wi-FARⓇ.In this book chapter, we focus on an early implementation of the IEEE 802.22 standard. We also focus on the emerging IEEE 802.22.3 Standard on Spectrum Characterization and Occupancy Sensing and the implementation challenges based on software defined radio platforms. It would be nice if the readers have some basic understanding of the IEEE 802.22 standard, but as such, this book chapter is self-contained for the new readers. The readers wanting to go deeper into the IEEE 802.22 spec may get the IEEE 802.22-2011standard at no cost using the following URL: http://standards.ieee.org/about/get/802/802.22.html

Apurva Mody, Anindya Saha, Ivan Reede, Gianfranco Miele, Gianni Cerro

Cognitive Radio Applications and Practices

Frontmatter

55. Spectrum Database and Smart Spectrum

In this chapter, spectrum management technologies based on a spectrum database are introduced. Statistical spectrum maps and spectrum databases are important components for understanding a spectrum environment that has locality due to geolocation, surrounding structures, frequency, and so on. The typical spectrum database provides spectrum information according to a radio propagation model for estimating the unused spectrum for spectrum sharing. However, the original geolocation spectrum database does not consider the site-specific environment because a statistical radio propagation model is used. Here, in order to improve the accuracy of the spectrum database, the measurement-based spectrum database is considered. The highly accurate spectrum database can improve spectrum-sharing performance and spectrum efficiency. Finally, a future spectrum management concept, called a smart spectrum, is introduced to open up the possibility for a new wireless world.

Takeo Fujii, Kei Inage, Koya Sato

56. Dynamic Spectrum Access for Machine to Machine Communications: Opportunities, Standards, and Open Issues

Cognitive radio can be applied to a multitude of domains, one of which is M2M communication. Specifically, M2M communication refers to communication between devices without human intervention. Hence, devices should be able to organize themselves and run the communication protocol autonomously. If cognitive radio is used, tasks such as dynamic spectrum access (DSA), spectrum sensing, and alike present additional challenges compared to traditional network, as all the decision framework should be implemented and automatized in the devices. In this chapter, we focus on DSA techniques for M2M. The main difference from other kinds of communication is relative both to the energy efficiency and to the low protocol overhead, as devices should run for long periods of time and run without human intervention. At first we present related work from literature, categorizing the different tasks devices which want to leverage DSA on M2M have to perform. At the end of the chapter, we present a proof of concept of a general framework, which can be applied to different scenario concerning M2M, encompassing all the spectrum management and measurement tasks M2M devices should generally perform. Finally, we derive open challenges and future research directions concerning this scenario.

Luca Bedogni, Marco Di Felice, Luciano Bononi

57. Reinforcement Learning-Based Spectrum Management for Cognitive Radio Networks: A Literature Review and Case Study

In cognitive radio (CR) networks, the cognition cycle, i.e., the ability of wireless transceivers to learn the optimal configuration meeting environmental and application requirements, is considered as important as the hardware components which enable the dynamic spectrum access (DSA) capabilities. To this purpose, several machine learning (ML) techniques have been applied on CR spectrum and network management issues, including spectrum sensing, spectrum selection, and routing. In this paper, we focus on reinforcement learning (RL), an online ML paradigm where an agent discovers the optimal sequence of actions required to perform a task via trial-end-error interactions with the environment. Our study provides both a survey and a proof of concept of RL applications in CR networking. As a survey, we discuss pros and cons of the RL framework compared to other ML techniques, and we provide an exhaustive review of the RL-CR literature, by considering a twofold perspective, i.e., an application-driven taxonomy and a learning methodology-driven taxonomy. As a proof of concept, we investigate the application of RL techniques on joint spectrum sensing and decision problems, by comparing different algorithms and learning strategies and by further analyzing the impact of information sharing techniques in purely cooperative or mixed cooperative/competitive tasks.

Marco Di Felice, Luca Bedogni, Luciano Bononi

58. Overview of Recent Applications of Cognitive Radio in Wireless Communication Systems

Cognitive radio (CR) is one of the most intensively researched paradigms in recent wireless communication systems. The great deal of attention that CR has attracted can be ascribed to its demonstrated capability to increase spectrum efficiency and overall network capacity through interference-free spectrum sharing among several wireless communication systems. CR provides intelligence to wireless networks, enabling users to access multiple air interfaces and select the most appropriate alternative under varying communication needs and operation conditions. The potential benefits of CR have not gone unnoticed to many wireless communication systems, which nowadays have effectively benefited from the adoption of CR techniques and operating principles. This chapter provides an overview on the introduction of CR principles into two prominent wireless communication systems, namely, mobile and satellite communication networks. A detailed discussion is provided on the background and motivation for the adoption of the CR technology and how CR techniques have been introduced in these two systems. A brief discussion is also provided on the adoption of the CR technology in other wireless communication systems, including military communications, public safety and emergency networks, aeronautical communications, and wireless-based Internet of Things. This chapter is aimed at illustrating the practical implementation of the theoretical CR principles widely discussed in the literature.

Miguel López-Benítez

59. TVWS: From Trial to Commercial Operation in the UK

The UK has long held a reputation for innovation in the TV bands. This chapter explains how the UK regulator’s (Ofcom) openness to innovation, together with long established TV industry collaboration, enabled a robust regulatory framework for TV white spaces to be put in place. This chapter considers how the UK broadcasting heritage led up to this point and shapes the TV white spaces capacity availability. It also considers how industry cooperation in substantive trials produced results that encouraged the regulator to finalise its framework development and enact the required regulation, by the start of 2016.

Andrew Stirling, Jim Beveridge

60. Cognitive Radio and TV White Space (TVWS) Applications

As more user applications emerge for wireless devices, the corresponding amount of traffic is rapidly expanding, with the corollary that ever-greater spectrum capacity is required. Service providers are experiencing deployment blockages due to insufficient bandwidth being available to accommodate such devices. TV White Space (TVWS) represents an opportunity to supplement existing licensed spectrum by exploiting unlicensed resources. TVWS spectrum has materialized from the unused TV channels in the switchover from analogue to digital platforms. The main obstacles to TVWS adoption are reliable detection of primary users (PU) i.e., TV operators and consumers, allied with specifically, the hidden node problem. This chapter presents a new generalized enhanced detection algorithm (GEDA) that exploits the unique way digital terrestrial TV (DTT) channels are deployed in different geographical areas. GEDA effectively transforms an energy detector into a feature sensor to achieve significant improvements in detection probability of a DTT PU. Furthermore, by framing a novel margin strategy utilizing a keep-out contour, the hidden node issue is resolved and a viable secondary user sensing solution formulated. Experimental results for a cognitive radio TVWS model have formalized both the bandwidth and throughput gains secured by TVWS users with this new paradigm.

J. H. Martin, L. S. Dooley, K. C. P. Wong

61. Opportunities and Enabling Technologies for 5G and Beyond-5G Spectrum Sharing

In this paper an overview is given of the current status of 5G industry standards, spectrum allocation, and use cases, followed by initial investigations of new opportunities for spectrum sharing in 5G and the underlying technologies to enable efficient sharing, considering both licensed and unlicensed scenarios and spectrum both below 6 GHz and in the millimeter-wave frequency range.

Maziar Nekovee

62. Learning Dynamic Jamming Models in Cognitive Radios

Cognitive radio (CR) integrates results from software-defined radio (SDR), machine learning (ML), and neuroscience for smart radio transmission devices. SDR enables devices to be digitally and dynamically configured in online applications; methodologies and techniques developed to introduce self-awareness in existing systems can be based on ML. Specifically, CR can adaptively regulate its internal parameters in response to the changes in the surrounding environment. New physical layer security issues are also emerging, for example, smart jamming attacks aim to reduce the quality of service or to disrupt legitimate communications. In this context, the electromagnetic spectrum represents the environment, while signals inside it are the individual entities. A CR-to-spectrum interaction consists of a dynamic process that can be driven by a CR device. Learning dynamic and measurement models from spectrum data is the main objective in CR applications.To learn a model, statistical signal processing techniques can be used. Such models can be considered as parametric Bayesian filters that allow a CR to estimate current state of observed entities (including CR itself) and to predict their actions in the near future. Adaptive hierarchical Bayesian filters able to cover nonstationary entity behaviors can be described through probabilistic graphical models (PGM). Interacting entities can be modelled by coupling multiple PGMs related to different entities.In this chapter, state-of-the-art on representation and learning of dynamic models for physical layer security is introduced along with some future directions. An experimental framework is then presented with two currently investigated applications: Spectrum Intelligence and TV White Spaces (TVWS).

Andrea Toma, Carlo Regazzoni, Lucio Marcenaro, Yue Gao

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