Blind Channel and Data Estimation Using Fuzzy Logic-Empowered Opposite Learning-Based Mutant Particle Swarm Optimization

Abstract

Multiple-input and multiple-output (MIMO) technology is one of the latest technologies to enhance the capacity of the channel as well as the service quality of the communication system. By using the MIMO technology at the physical layer, the estimation of the data and the channel is performed based on the principle of maximum likelihood. For this purpose, the continuous and discrete fuzzy logic-empowered opposite learning-based mutant particle swarm optimization (FL-OLMPSO) algorithm is used over the Rayleigh fading channel in three levels. The data and the channel populations are prepared during the first level of the algorithm, while the channel parameters are estimated in the second level of the algorithm by using the continuous FL-OLMPSO. After determining the channel parameters, the transmitted symbols are evaluated in the 3rd level of the algorithm by using the channel parameters along with the discrete FL-OLMPSO. To enhance the convergence rate of the FL-OLMPSO algorithm, the velocity factor is updated using fuzzy logic. In this article, two variants, FL-total OLMPSO (FL-TOLMPSO) and FL-partial OLMPSO (FL-POLMPSO) of FL-OLMPSO, are proposed. The simulation results of proposed techniques show desirable results regarding MMCE, MMSE, and BER as compared to conventional opposite learning mutant PSO (TOLMPSO and POLMPSO) techniques.

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

“…The performance and comparison of Proposed FLeCSI based on both variants with previous algorithms are represented in Tables 3 and 4 at 117th, 110th NoCs, respectively. It clearly observed Proposed FLeCSI based on both variations give attractive results as compared to previously published approaches in [13,18]. 3.001*10 −5.1 MMCE and 1.523*10 −6.5 , 1.5017*10 −6.6 Miss Rate, respectively.…”

“…The procedure above mentioned is included in any MUD and CE for any multi-user MIMO system. MMSE-based batch processing receiver, cost function and derivation is given below [18].…”

“…(c) Proposed FLeCSI method compared with various state of art algorithms like TOMPSO and POMPSO [13,18] and FL-OLMPSO [18]. Simulation results have shown that FLeCSI gives far better results as compared to other algorithms.…”

Multi user detection using fuzzy logic empowered adaptive back propagation neural network

“…As a result, the computational complexity of the system is also increased [10]. The adaptive implementation of the MIMO system is better to approach in order to overcome the computation complexity problem [10,36,37].…”

“…There are numerous strategies like Neural Network [25][26][27]41], Genetic Algorithm (GA) [31][32][33] Differential Equation (DE), Cooperative Co-Evolutionary (CC) Algorithms [34], Particle Swarm Optimization (PSO) [40], Maximum Likelihood (ML) [5,6], Partial Opposite Mutant Particle Swarm Optimization (POMPSO), Total Opposite Mutant Particle Swarm Optimization (TOMPSO) [35][36][37], Island GA, Differential Equation (DE) and Island DE has been proposed which further enhance the performance of the 5-th generation communication network [20,38,39,41,42].…”