On Efficient Resource Allocation for Cognitive and Cooperative Communications

Abstract

Abstract-Cooperative communication (CC) can offer high channel capacity and reliability in an efficient and low-cost way by forming a virtual antenna array among single-antenna nodes that cooperatively share their antennas. It has been well recognized that the selection of relay nodes plays a critical role in the performance of multiple source-destination pairs. Unfortunately, all prior work has made an unrealistic assumption that spectrum resources are unlimited and each source-destination pair can communicate over a dedicated channel with no mutual interference. In this paper, we study the problem of maximizing the minimum transmission rate among multiple source-destination pairs using CC in a cognitive radio network (CRN). We jointly consider the relay assignment and channel allocation under a finite set of available channels, where the interference must be considered. In order to improve the spectrum efficiency, we exploit the network coding opportunities existing in CC that can further increase the capacity. Such max-min rate problems for cognitive and cooperative communications are proved to be NPhard and the corresponding MINLP (Mixed-Integer Nonlinear Programming) formulations are developed. Moreover, we apply the reformulation and linearization techniques to the original optimization problems with nonlinear and nonconvex objective functions such that our proposed algorithms can produce high competitive solutions in a timely manner. Extensive simulations are conducted to show that the proposed algorithms can achieve high spectrum efficiency in terms of providing a much improved max-min transmission rate under various network settings.

Solving resource allocation problems in cognitive radio networks: a survey

“…The linearised expressions may indeed be approximates of the original, but if the values obtained are close estimates or within certain acceptable limits or bounds, the solutions provided by the new problem can be useful and meaningful, even though suboptimal. Examples of RA problems in which linearisation has been employed as a useful tool to achieve solutions can be found in [20,57,58]. In [58], a combination of linearisation, relaxation and reformulation techniques were employed in solving their RA problem.…”

“…Examples of RA problems in which linearisation has been employed as a useful tool to achieve solutions can be found in [20,57,58]. In [58], a combination of linearisation, relaxation and reformulation techniques were employed in solving their RA problem. For the linearisation part, a constraint, which was non-linear due to the combination of multiplication and division operations, was transformed into a linear form by the use of the logarithm function.…”

“…The RA problem, as developed, was a MINLP problem but by relaxing the integer constraint, the problem became an LP and was then solved. The work in [58] also employed integer relaxation in developing its solution model. The major issue with the relaxation approach is that only suboptimal solutions can be obtained, and in some instances, the gap between solutions obtained after relaxation and the optimal can be significantly wide apart.…”

A two‐level cooperative game‐based approach for joint relay selection and distributed resource allocation in MIMO‐OFDM‐based cooperative cognitive radio networks

“…Joint problems of relay selection and resource allocation in CRNs have attracted extensive research interests because of its more effective spectrum utilisation [6][7][8][9][10][11]. The authors in [6] consider a CCRN in which the relays are selected among the existing SUs.…”

“…A suboptimal approach for reducing the complexity of joint relay selection and power allocation in CCRN was proposed in [10]. The network coding opportunities existing in cooperative communications that can further increase the capacity was exploited in [11]. Furthermore, the reformulation and linearisation techniques to the original optimisation problems with nonlinear and non-convex objective functions were applied such that the proposed algorithms can produce high competitive solutions in a timely manner.…”