Modeling multiuser spectrum allocation for cognitive radio networks

https://doi.org/10.1016/j.compeleceng.2015.10.006Get rights and content

Highlights

  • A scheme is proposed to allocate bands among multiple cognitive radio users.

  • Suitability-Throughput Gain defines the suitability of a band for contending users.

  • The scheme takes into account the impact of allocating a band to a user on others.

  • It offers higher user satisfaction, less collision and better spectrum utilization.

Abstract

Spectrum allocation scheme in cognitive radio networks (CRNs) becomes complex when multiple CR users concomitantly need to be allocated new and suitable bands once the primary user returns. Most existing schemes focus on the gain of individual users, ignoring the effect of an allocation on other users and rely on the ‘periodic sensing and transmission’ cycle which reduces spectrum utilization. This paper introduces a scheme that exploits collaboration among users to detect PU’s return which relieves active CR users from the sensing task, and thereby improves spectrum utilization. It defines a Capacity of Service (CoS) metric based on the optimal sensing parameters which measures the suitability of a band for each contending user and takes into consideration the impact of allocating a particular band on other band seeking users. The proposed scheme significantly improves capacity of service, reduces interference loss and collision, and hence, enhances dynamic spectrum access capabilities.

Introduction

The use of cognitive radio (CR) technology allows radio devices to opportunistically access the radio frequency (RF) spectrum in an unlicensed fashion. The RF operation of a CR (i.e., secondary/unlicensed user) involves three major mechanisms: spectrum sensing, spectrum use, and spectrum switching. Spectrum sensing is used to find white spaces (vacant band) in licensed bands and does not interfere with the licensed networks’ activities. The most widely accepted method for detecting white spaces is energy detection based cooperative spectrum sensing [1]. After finding a suitable white space, the secondary user starts using that band and continues until it detects the return of the primary user (PU). At that point, it quickly evacuates the band to inflict minimum interference on the primary system [2]. Finally, the secondary user (SU) has to find and switch to another vacant band to resume transmission. However, finding a vacant band, assessing its suitability to SU’s need and switching smoothly to that band to continue uninterrupted transmission are the most critical factors affecting this mechanism.

Early works on spectrum allocation primarily focused on a simple scenario where a single SU requires finding and switching to the most appropriate band from a number of available vacant bands. Recent works have focused on a more complex but general scenario where multiple SUs require multiple bands for seamless transmission. The critical task in this scenario is to find currently unused licensed bands and allocate them among these users efficiently. In such band allocation, candidate bands’ suitability in meeting respective SU’s requirement as well as throughput achievable should be taken in consideration. Existing researches often use ‘periodic sensing’ (i.e., an SU senses and transmits in alternate cycle) and assume a priori knowledge on PU usage pattern. But ‘periodic sensing’ reduces spectrum utilization due to the time wasted in sensing and the usage pattern of PUs is most likely to vary over time. Another issue is the possible conflict arising from the selection of the same band at the same time by two or more users and has also generally been ignored in literature. Therefore, the following issues need to be addressed for real world implementation of CR networks: (i) improving sensing scheme and spectrum utilization removing the drawbacks of ‘periodic sensing’, such scheme should not depend on the known PU usage pattern; (ii) allocation of vacant bands among multiple users concomitantly in a way that meets individual SU’s requirement while maximizing utility gain; and (iii) resolution of any conflict when the same band is equally suitable for multiple SUs.

In this paper, we introduce a collaborative spectrum band allocation framework for cognitive radio networks where multiple users simultaneously seek spectrum bands for transmission. Our framework allocates spectrum bands to both types of users - new incoming users and ongoing users who are already using some other bands and need to change bands quickly due to the return of the corresponding licensed users. To develop this framework, we first propose a sensing model based on energy detection where non-active users (i.e., users that are not using any licensed band currently) are split into two groups, sensing and idle, based on some appropriate criteria. By selectively keeping the idle group silent and engaging the sensing group into cooperative spectrum sensing, the scheme achieves accurate sensing results and allows continuous spectrum utilization by active users, eliminating the necessity and essentially the drawbacks of periodic sensing [2]. We then define two parameters to determine suitability of a band once it is found vacant via spectrum sensing. One is named suitability throughput gain (STG) which is a measure of throughput of a utilized spectrum band and its suitability to a specific user, and the other is interference loss (IL) which we formulate to reflect the loss due to inflicted interference on the primary user. In order to maximize STG keeping IL below a certain level, we formulate a maximization problem to optimize the sensing time-bandwidth product and decision threshold of a set of energy detectors working in parallel for spectrum sensing over multiple spectrum bands. Based on STG and IL, a combined decision is taken to allocate unique spectrum bands to all the secondary users currently requesting for bands. In this respect, our proposed framework offers: (i) devising a collaborative spectrum sensing model that offers accurate sensing and better spectrum utilization, (ii) allocation of spectrum bands among multiple users aiming to maximize the spectrum efficiency and gain, and (iii) a solution to mitigate the collisions among users in selecting spectrum bands.

This paper is organized as follows. Section 2 presents the related background literature along with their limitations and the necessity of the proposed framework. Section 3 describes the network structure and the sensing model incorporated in our proposed framework. The parameters influencing the formulation of the optimization problem and their impact on the allocation scheme are discussed in Section 4. Section 5 formulates the optimization problem to maximize the suitability throughput gain and the solution is analyzed. The proposed spectrum allocation techniques based on the solution is presented in Section 6 and their performances are analyzed in Section 7. Finally, the paper is concluded with some remarks in Section 8.

Section snippets

Related works

In spectrum allocation, the most common practice in the literature is selecting a band from a set based on its service properties. The spectrum pooling technique is considered to be the first approach for selection of a band. In this approach, spectrum bands from different owners (military, trunk radio, etc.) are merged into a common pool. From this pool, an SU may temporarily use a licensed band during its idle periods. To select the most appropriate band, a covariance matrix based cooperative

Network and sensing model

In the following, we describe the CR network and the sensing model to be used to meet the requirements of the spectrum allocation framework.

Spectrum selection factors

In addition to the above metrics, factors that should be considered in allocating a band to a user include the properties of the spectrum bands, the requirements of the SUs and the impacts that PUs have on band usage. In the following, we define and formulate these factors and analyze their impacts on spectrum selection.

Optimization of r and Λ

As both STG and IL are functions of the sensing parameters rb and Λb (all other parameters are constant given a particular RF scenario), we search for the uniform optimal values (r*, Λ*) applicable to all concurrent energy detectors, to maximize the cumulative STG over all B bands. We formulate the maximization problem as maximizef=b=1Bfb=b=1Bδcbgb(1Ψfbc)subjecttob=1Bκb(1Ψdbc)<ϕ.

To determine whether a feasible solution exists to the optimization problem, we test its convergence and the

Spectrum allocation scheme

We propose two approaches for accomplishing band allocation scheme based on the values of r* and Λ*, namely selfish and compromising. In the selfish approach, only the individual gain of a user is taken into account, ignoring any effect on others, whereas the compromising approach considers the individual gain as well as any loss incurred to other users due to the allocation.

Performance analysis

In this section, we evaluate the performance of our proposed spectrum allocation scheme by both selfish and compromising approaches. Our schemes are compared with the approach where the B vacant bands are randomly allocated among the L contending users [18]. We analyze the results in both the cases when LB and L>B. We randomly generated different parameters for the proposed scheme over 100 trials to cover a wide range of scenarios and the results presented in this section are averaged over all

Conclusion

In allocating bands in cognitive radio networks, a scheme should consider a number of factors like band utilization and user’s past experience of using a particular band, interference cost, possibility of PT’s return in future on the band once allocated and the impact of allocating a particular band on other band seeking users. The existing schemes in literature do not consider all these aspects when multiple secondary users need to be simultaneously accommodated into multiple available

Mohammad Iqbal Bin Shahid received his Ph.D. degree from the Faculty of Information Technology of Monash University, Victoria, Australia in 2011. Currently, he is a Research Associate in the Faculty of Science and Technology of Federation University, Victoria, Australia. His research interests include Cognitive Radio Networks, Artificial Intelligence and Neural Networks.

References (25)

  • YangX. et al.

    Adaptive spectrum selection for cognitive radio networks

    Proceedings of international conference on computer science and software engineering

    (2008)
  • XuD. et al.

    Optimal bandwidth selection in multi-channel cognitive radio networks: How much is too much?

    Proceedings of the 3rd IEEE symposium on new frontiers in dynamic spectrum access networks

    (2008)
  • Cited by (0)

    Mohammad Iqbal Bin Shahid received his Ph.D. degree from the Faculty of Information Technology of Monash University, Victoria, Australia in 2011. Currently, he is a Research Associate in the Faculty of Science and Technology of Federation University, Victoria, Australia. His research interests include Cognitive Radio Networks, Artificial Intelligence and Neural Networks.

    Joarder Kamruzzaman received the B.Sc. and M.Sc. degrees in Electronic Engineering from Bangladesh University of Engineering and Technology, Bangladesh, and the PhD in Information Systems Engineering from Muroran Institute of Technology, Hokkaido, Japan. Currently, he is an Associate Professor in the Faculty of Science and Technology, Federation University Australia. His research interest includes wireless communications, sensor networks and cognitive radios.

    Md. Rafiul Hassan received his Ph.D. in Computer Science and Software Engineering from University of Melbourne in 2007. He joined King Fahd University of Petroleum and Minerals in 2010, where he works as an assistant professor. His current fields of interests are machine learning which includes neural networks, fuzzy logic, evolutionary algorithms, hidden Markov model, and support vector machine.

    Reviews processed and recommended for publication to the Editor-in-Chief by Associate Editor Dr. S. Thampi.

    View full text