Throughput Optimization in Cooperative Communications using Evolutionary Algorithms

Abstract

This paper illustrates the results of the throughput of Secondary User (SU) in cognitive radios under cooperative scenario with the use of evolutionary algorithms in the presence of additive white Gaussian (AWGN) noise. In this work, the performance of half-voting and OR fusion rule is studied in terms of P d Vs P f curves. OR fusion rule is found to be most suitable in cooperative scenario. Then, Particle Swarm Optimization (PSO) and Biogeography Based Optimization (BBO) algorithms are implemented using OR fusion rule to enhance the throughput of cognitive users by taking in to account the protection of primary (licensed) users. The probability of detection is set to 0.9 for protection purpose. Simulations results in terms of co-operations shows that PSO performs better initially but as numbers of co-operations increases PSO performance degrades. KEYWORDS: Throughput, Evolutionary Algorithms, Fusion rules, Optimization I.INTRODUCTIONIn recent years, cognitive radio (CR) has emerged as a promising paradigm for exploiting the spectrum opportunity, which is restricted by the current rigid spectrum allocation scheme, to solve the spectrum scarcity problem [1][2]. Different spectrum sensing techniques are available for sensing primary users (PUs) signals. They can be broadly classified in to three categories: matched filter detection, cyclostationary detection and energy detection [19]. Energy detection is widely adopted because of lower complexity than other two schemes and priori knowledge of signal is also not required. Spectrum sensing depends upon two probabilities namely detection probability (P d ) and false alarm probability (P f ) [5]. Detection probability is related to probability of correctly detecting the presence of PUs, whereas false alarm probability is related to incorrect detection of PUs when it is absent [4]. Many research works are proposed to improve the performance metrics of detection probability and false alarm probability. However, PSO is addressed for sensing-throughput trade-offs problem under cooperative scenarios using various hard combination fusion schemes [3]. Optimization performance of cooperative sensing in terms of number of cooperative users is performed using constant detection rate (CDR) and constant false alarm rate [4]. But, there is no consideration for throughput of CUs. The work in [5] focuses on the optimization of sensing-throughput tradeoffs using iterative algorithms. In this paper, PSO and BBO algorithms are implemented to achieve the same objective. Both the algorithms are performed over OR fusion schemes.The Paper is organized as follows: Section-II denotes the system model and other performance metrics. A BBO and PSO algorithms are discussed in section III and IV respectively. Section 5 covers the methodology used by proposed work. However, the section VI and section VII include results and conclusions respectively. II.SYSTEM MODELThis study focuses on PUs signals with AWGN at CUs. We assume that both noise and PUs signal are independent. The basic model...