Spectrum Sharing in Cognitive Radio Networks

As the complexities of wireless technologies increase, novel multidisciplinary approaches for spectrum sharing and management are required, with inputs from technology, economics, and regulations. The important characteristics of spectrum sharing methods include identifying the available spectrum resource, deciding on the optimal sensing and transmission time and proper coordination among the users for spectrum access. Recently, cognitive radio technology has come into action to handle the spectrum scarcity problem. In this chapter, we technically overview the state of the art of various spectrum sharing techniques and discuss their potential issues with emerging applications of the communication system, especially to enhance the spectral efficiency. The potential advantages, limiting factors, and characteristic features of the existing cognitive radio spectrum sharing domains are thoroughly discussed, and an overview of spectrum sharing is provided as it ensures channel access without interference or collision with the licensed users in the spectrum. Emerging trends in cognitive radio research and open research challenges related to the cost-effective and large-scale deployment of cognitive radio systems are outlined.

Due to the huge number of diverse wireless devices and technologies, spectacular increases in the number of wireless subscribers, advent of new applications and continuous demand for higher data-rates, the radio frequency (RF) spectrum is becoming more and more crowded. This development calls for systems and devices that are aware of their surrounding RF environment, so they can facilitate flexible, efficient, and reliable operation and utilization of the available spectral resources. Thus, the spectrum sensing is becoming progressively more important to recent and future wireless communication systems for identifying underutilized spectrum and characterizing interference, with the goal of achieving reliable and efficient operation. Cognitive radio is an intelligent radio that is aware of its surrounding environment, capable of learning and adapting its behavior and operation to provide a good match to its surrounding environment and to the user’s needs. Spectrum sensing is the key requirement and one of the most challenging issues for the cognitive radio system. This chapter presents a comprehensive survey of the physical layer spectrum sensing techniques for cognitive radios. The major challenges in spectrum sensing are outlined and several techniques for improving spectrum sensing performance are discussed. Further, a hybrid model for non-cooperative spectrum sensing is presented; with this terminology, the proper channelization of the three techniques is introduced, with relevant discussion. This approach helps in detecting the idle spectrum opportunistically, with better spectrum utilization under non-cooperative sensing, resulting in enhanced spectrum efficiency. We also explore sensing under a cooperative environment. The approach presented aids in opportunistically detecting idle spectrum bands (spectrum holes that are the underutilized sub-bands of the radio spectrum), with better utilization of the spectrum than under non-cooperative sensing, and increased overall spectrum efficiency.

A key challenge in a cognitive radio network is to have an efficient sensing and non-interfering spectrum access decision protocol that enables cognitive users to reserve chunks of the spectrum for certain periods of time. However, the modeling of variable bandwidths for communication in cognitive radio is very complicated, and channel accessing policies must be defined for the cognitive radio users. In this chapter, we compare various medium access control (MAC) protocols for distributed cognitive radio networks and propose a novel multichannel cooperative MAC protocol for distributed cognitive radio networks which has a backoff algorithm for contention solving among the competing cognitive users. The proposed MAC protocol consists of a control channel on which the cognitive users cooperate with each other. The control channel cooperation among the cognitive users is performed by presenting the sensing results of all cognitive users on the control channel, then the idle channels from the pool of total available idle channels, whose information is available on the control channel, are selected by the cognitive users. Each channel is divided into cycle time, which is further divided into four intervals: idle interval, sensing–sharing interval, contention interval, and data transmission interval. The backoff algorithm for solving collisions among the competing users allows the collided cognitive users to succeed by selecting another contention slot from the expanded contention window. The increased number of successful users enhances the throughput of the cognitive radio network by transmitting their data over the detected idle licensed channels. An optimal number of contention slots is identified that maximizes the number of successful cognitive users as well as throughput. The proposed MAC protocol optimizes the number of contention slots depending on the number of cognitive users in comparison to the fixed number of slots in the self-scheduled multichannel-MAC (SMC-MAC) protocol.

In this chapter, the cognitive radio MAC protocol is considered in a practical scenario, and we examine the effect of perfect and imperfect sensing on the performance of throughput and energy efficiency of the cognitive radio network. Idle channel detection in the cognitive radio MAC protocol is affected by false alarm probability that occurs due to imperfect sensing. A false alarm occurs when the cognitive user falsely (imperfectly) detects a licensed channel as busy which is actually idle, so in this situation the cognitive user cannot utilize the opportunity of data transmission. Imperfect sensing resulted from a false alarm affects the system performance of the cognitive radio network by missing opportunities for spectrum use in comparison to perfect sensing, as demonstrated in the simulation results. The miss-detection also results in the imperfect sensing of licensed channels, due to which the cognitive user transmits its data on a licensed channel already occupied by the primary user and hence causes interference to the primary user. In this chapter, a potential scheme is proposed to depict the effects of perfect and imperfect sensing on the performance of the proposed distributed cognitive radio MAC protocol. In addition, the optimal number of contention slots is achieved for the proposed MAC protocol, which has avoided the contention slots-throughput trade-off problem. The performance of the MAC protocol for different licensed channels utilization probability is simulated. The simulation results illustrate that throughput and energy efficiency of the MAC protocol for an imperfectly sensed environment are lower than those of the perfect sensing scenario, and interference to the primary user is lower in the proposed protocol for smaller values of missed detection probability.

In this chapter, the scheme for maximizing the bandwidth efficiency by utilizing the wasted bandwidth of the licensed channels in the distributed cognitive radio MAC protocol is proposed. In addition, the contention resolving algorithm is applied in this proposed bandwidth maximization scheme as discussed in Chap. 3. It is clear that the idle channel detected by the cognitive user in a sensing-sharing slot is utilized only in the data transmission interval, therefore all the remaining sensing-sharing slots after idle channel detection and contention interval of that licensed channel remain unutilized, causing waste of bandwidth in the proposed cooperative MAC protocol. Since bandwidth is one of the scarce resources of wireless communication, this chapter deals with the potential issue of bandwidth wastage arising in the proposed distributed MAC protocol. Further, the bandwidth wastage in the cooperative distributed MAC protocol is minimized by transmitting data of the cognitive users over the idle licensed channels, which are unutilized in the sensing-sharing and contention interval. The proposed technique significantly enhances the throughput of the cooperative distributed network. A comparison of the proposed scheme in this chapter is performed with the SMC-MAC protocol.

Energy consumption is a major concern in the present wireless communication scenario. Wireless devices run different services—for example web browsing, gaming, social media, and multimedia downloads—which quickly drain the battery of the user terminal; therefore we need to design an energy-efficient user terminal that provides more battery life. Primary users recurrence rate return to the licensed band also impacts the energy efficiency of the cognitive radio network because it may require a restart of spectrum sensing, channel selection and communication over the control and data channels, consuming additional energy. Further, cognitive users consume a great deal of energy for exchange of control information, and during retransmission if the primary user resumes its transmission. A cognitive user with multiple transceivers achieves higher sensing accuracy, avoids hidden terminal problems, maximizes throughput, and is more spectrum-efficient than a single transceiver user, but it utilizes higher energy. Therefore, there is trade-off in cognitive radio MAC protocol design between the number of transceivers and energy efficiency. This chapter is concerned about the energy efficiency of cognitive radio terminals, and obtains the optimal transmit power for the cognitive terminal at which energy efficiency is at its maximum. We further show that the complexity of the proposed algorithm for computing optimal transmit power is greatly reduced. We consider different scenarios of channel conditions at different channel gains and maximize the energy efficiency of the cognitive radio terminal.

In this chapter, we propose a technique to eliminate the sensing-throughput trade-off of the conventional approach in order to increase the throughput of the cognitive radio user and simultaneously reduce interference with the primary users. We look at a cognitive user that employs a conventional frame, then perform spectrum sensing and transmission, finding that the cognitive user ceases data transmission at the beginning of each frame. Spectrum sensing is performed first for particular units of time, and data is then transmitted for the remaining frame duration. However, there is a potential problem in this scheme. It is well known from classical detection theory that an increase in sensing time results in a higher probability of detection and lower probability of false alarm; however, it also results in less data transmission time and hence limits the throughput of the cognitive radio user, causing a sensing-throughput trade-off problem. In addition, there is the problem of unpredictable primary user (PU) transmission during the transmission time of the cognitive user, resulting in data loss. In order to avoid the sensing-throughput trade-off and to maximize the throughput of spectrum sharing cognitive radio networks, we propose a technique that reduces the data loss rate by reducing collisions of frames of primary and secondary users. Finally, simulation results are provided and compared with the conventional approach. From these simulation results, it is demonstrated that the throughput is better for the proposed approach as compared to that of the conventional approach.

The access strategy to provide efficient spectrum allocation to the cognitive user is an important issue in cognitive radio communication network research. The channel state information (CSI) between cognitive user (CU) transmitter and the primary user (PU) receiver is employed to compute the maximum CU transmit power allowable to limit the interference. Channel capacity is the best performance metric to analyze any cognitive radio network model, and several capacity notions are expressed for different fading channels, such as ergodic capacity for the fast-fading channel and outage capacity for the slow-fading channel. Various researchers have analyzed the capacity limits of the CU link over different fading channels with perfect and imperfect CSI. In this chapter, we explore an optimal power allocation scheme for spectrum sharing with imperfect channel state information between the CU and PU over a Rayleigh fading environment. We analyze the ergodic capacity of the CU link under the combination of peak transmit power and peak/average interference power constraints with and without primary user interference. In addition, we analyze the outage capacity with multiple primary user interference with the error variance under the joint peak transmit power and peak interference power constraint, as well as individual peak interference power constraint. Moreover, we investigate the power expenditure to achieve the lower limit of ergodic and outage capacity. The minimum mean square channel estimation technique is used for the channel estimation between the CU and PU. However, the convex optimization method is employed for the optimal power allocation.

Channel capacity is used as a basic performance measurement tool for analysis and design of more efficient techniques to improve the spectral efficiency of wireless communication systems. In spectrum sharing systems, channel state information (CSI) is used at the cognitive/secondary transmitter to adaptively adjust the transmission. The knowledge of secondary link CSI and information at the secondary transmitter/cognitive radio transmitter regarding the channel between the secondary transmitter and the primary receiver (PR) are used to obtain the optimal power transmission policy of the secondary user (SU) under the constraints on the peak and average received power at the primary receiver. In this chapter, we numerically compute the channel capacity in the fading environment under average interference power constraints with two different adaptation policies for spectrum sharing in cognitive radio communication systems: power adaptation and rate and power adaptation for multilevel quadrature amplitude modulation format. We explore the small-scale fading effect on the transmit power of the secondary transmitter. The rate and power of the secondary transmitter is varied based upon the sensing information and channel state information of the secondary link. The channel capacity is maximized for these two policies by considering the Lagrange optimization problem for the average interference power constraint.

This chapter presents a cross-layer optimized design framework for cognitive radios in a dynamic spectrum access environment. Generally speaking, layered architectures such as Open Systems Interconnections (OSI) and Transmission Control Protocol (TCP) models forbid direct communication between non-adjacent layers, and communication between adjacent layers is also limited in such a way that the higher-layer protocol makes use only of the services at the lower layers and is not concerned about the details of how the service is being provided. This in turn becomes a bottleneck for new emerging wireless services. Therefore, the cross-layer optimization framework work related to wireless and cognitive radio network is reviewed in this chapter. In addition, the MAC layer parameters optimization with the help of cross-layer interaction is explored and various potential challenges in this interaction are presented.