Blind Channel and Data Estimation Using Fuzzy Logic Empowered Cognitive and Social Information-Based Particle Swarm Optimization (PSO)

Abstract

Multiple Input Multiple Output (MIMO) is a technology used to improve the channel capacity of the wireless communication systems. Rapid increase in the number of users has led to data rate demand increased in growing modern wireless communication systems. To overcome this issue, MIMO is being used with several multicarrier techniques like Orthogonal Frequency Division Multiple Access (OFDMA), Multi-Carrier Code Division Multiple Access (MC-CDMA), etc. Multi-user detection (MUD) with artificial intelligence plays a vital role to enhance network capacity to meet the demands of future networks with an increased number of users and multimedia services. Computational intelligence techniques are used in a multicarrier system to boost the process of MUD. Some of the computational intelligence algorithms like Swarm and Evolutionary are stuck in local minima and due to this issue, the overall performance of the network decreases. For the convergence of Swarm intelligence-based solutions, cognitive and social information (CSI) play a vital role. In this research article, the Fuzzy Logic empowered Cognitive and Social Information (FLeCSI) algorithm using a fuzzy logic and swarm intelligence algorithm is proposed. By using social and cognitive information FLeCSI updated each swarm position. After the simulation, it is observed that FLeCSI provides fast convergence and minimize MMSE and BER as compared to techniques used previously for MUD like Fuzzy Logic empowered Opposite Mutant Particle Swarm Optimization (FLOMPSO), Opposite Learning Mutant Particle Swarm Optimization (OLMPSO), Total Opposite Mutant Particle Swarm Optimization (TOMPSO), Partial Opposite Mutant Particle Swarm Optimization (POMPSO), etc.