ABSTRACT
CONTENTS 20.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480
20.1.1 Related works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 20.1.2 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 481
20.2 System Model and Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 482 20.3 Problem Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 484
20.3.1 Discrete Power Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 20.3.1.1 Quantization and Power Distributions . . . . . . . . . . . . . . . . . . . . . . . 485 20.3.1.2 Iteration Algorithm (IA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 20.3.1.3 Genetic Algorithm (GA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 20.3.1.4 Complexity Analysis for IA and GA . . . . . . . . . . . . . . . . . . . . . . . . . 487
20.3.2 Continuous Power Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487 20.3.2.1 Optimal Terminal Power Allocation . . . . . . . . . . . . . . . . . . . . . . . . . 488 20.3.2.2 Particle Swarm Optimization (PSO) . . . . . . . . . . . . . . . . . . . . . . . . . 490 20.3.2.3 Complexity Analysis for PSO Algorithm . . . . . . . . . . . . . . . . . . . . 491
20.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491 20.4.1 Performance of the Proposed Algorithms for TWR-CR Networks . . . . . . . . . . 491
20.4.1.1 Simulation Results of Discrete Power Distribution . . . . . . . . . . . 491 20.4.1.2 Simulation Results of Continuous Power Distribution . . . . . . . . 493
20.4.2 TWR Transmission vs. OWR Transmission . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494
20.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 496
Abstract
In this chapter, we consider a multi-input multi-output (MIMO) cooperative cognitive radio (CR) system model under a spectrum sharing set-up, where primary users and secondary users operate on the same frequency band. In the CR underlay mode, secondary users are allowed to exploit the spectrum allocated by primary users in an opportunistic manner by respecting a tolerated temperature limit. The secondary networks employ an amplify-and-forward two-way relaying technique in order to maximize the sum-rate under power budget and interference constraints. Indeed, combined CR, tow-way relaying, and MIMO antennas provide a smart solution for a more efficient usage of the frequency band. Furthermore, we investigate two models of power distributions; discrete power distribution and continuous power distribution. In this context, we formulate an optimization problem that is solved using joint optimization algorithms. For discrete power distribution, we employ heuristic algorithms as iterative and genetic algorithms to find a solution. While for continuous power distribution, first, we derive a closed-form expression of the optimal power allocated to antenna terminals. Then, we employ a heuristic algorithm based on practical swarm optimization algorithm to find the power allocated to secondary relays. In our numerical results, we demonstrate the performance of the proposed schemes for both power distribution types and analyze the impact of several system parameters on the achieved performance. Finally, we compare our proposed scheme with traditional one-way relaying scheme.
