Spread Spectrum Code Estimation by Particle Swarm Algorithm

In the context of spectrum surveillance, a new method to recover the code of spread spectrum signal is presented, while the receiver has no knowledge of the transmitter's spreading sequence. In our previous paper, we used Genetic algorithm (GA), to recover spreading code. Although genetic algorithms (GAs) are well known for their robustness in solving complex optimization problems, but nonetheless, by increasing the length of the code, we will often lead to an unacceptable slow convergence speed. To solve this problem we introduce Particle Swarm Optimization (PSO) into code estimation in spread spectrum communication system. In searching process for code estimation, the PSO algorithm has the merits of rapid convergence to the global optimum, without being trapped in local suboptimum, and good robustness to noise. In this paper we describe how to implement PSO as a component of a searching algorithm in code estimation. Swarm intelligence boasts a number of advantages due to the use of mobile agents. Some of them are: Scalability, Fault tolerance, Adaptation, Speed, Modularity, Autonomy, and Parallelism. These properties make swarm intelligence very attractive for spread spectrum code estimation. They also make swarm intelligence suitable for a variety of other kinds of channels. Our results compare between swarm-based algorithms and Genetic algorithms, and also show PSO algorithm performance in code estimation process. Keywords—Code estimation, Particle Swarm Optimization (PSO), Spread spectrum. used to learn complex behaviors characterized by sets of sequential decision rules and we used them for their robustness in solving complex optimization problem, nonetheless, by increasing the length of the code, we will often lead to an unacceptable slow convergence speed. Hence, we have introduced a new method, which is Particle Swarm Optimization (PSO), into code estimation in spread spectrum communication system. In searching process for code estimation, the PSO algorithm has the merits of rapid convergence to the global optimum results, and good robustness to noise. In this paper, we describe how to implement PSO as a component of a searching algorithm in code estimation. Swarm intelligence boasts a number of advantages due to the use of mobile agents. The code estimation performance of the proposed algorithm is examined by computer simulations. The performance measure of interest in this paper is the mean-squared error (MSE) for the code estimation. The paper is organized as follows. Section II describes the technique of direct sequence spread spectrum (DS-SS). Section III describes the system model used in this paper. Sections IV and V describe the PSO used to implement our proposed code estimator. Our simulation results are presented in section VI. Section VII concludes the paper.