Time complexity analysis of GA-based variants uplink MC-CDMA system

Abstract

Complexity plays a very significant role in real-time problems. A genetic algorithm (GA)-based multiple input multiple output for an uplink multi-carrier code-division multiple-access (MC-CDMA) receiver is being considered as an important pillar in real-time wireless communication problems. Bit error rate (BER) and minimum mean square error (MMSE) are well-known system performance evolution parameters to estimate the real-time system standards. Sometimes, the multiple solutions give the same BER and MMSE, and then, the complexity is one of the core aspects that will outline the suitable candidate solution. In this article, the proposed drift analysis is being used for the complexity analysis among these GA-based variations. It is observed that the time complexity of the proposed GA-based MC-CDMA with weight relation is less as compared to without weight relation, due to empowering of an exceptional relationship to find out the required solution. Keywords Multi-carrier code-division multiple access (MC-CDMA) • Minimum mean square error (MMSE) • Multiuser detection (MUD) • Bit error rate (BER) • Multiple input multiple output (MIMO) • Genetic algorithm (GA) • Channel estimation (CE)

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

“…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].…”

“…This improves the performance of the MIMO based MC-CDMA system as compared to the conventional LMS [28,29] & GA based suboptimum receiver [16,33] in terms of Convergence rate & Minimum Mean Square Error. Computational complexity is another challenging issue in modern communication [38][39][40][41][42]. The proposed FLeABPNN solution gives attractive results with low computational complexity.…”

Two‐stage memetic algorithm for blind equalisation in direct‐sequence/code‐division multiple‐access systems

“…On the other hand, some approaches have studied both bit‐error‐rate (BER) and near–far effect performances. It is worth mentioning recent approaches using GAs such as [28–30, 32, 43, 44]. Earlier references can be found in [45].…”