A Systematic Review of Optimization Paradigms for Enhancing Capacity and Reliability in MIMO-OFDM

Communication systems continually evolve with the primary goal of improving Quality of Service (QoS) for multiple simultaneous users. Traditional multiplexing techniques, which share channel resources in time or frequency, can restrict bandwidth and disrupt communication, motivating the use of OFDM to exploit user orthogonality and achieve full-band, full-time utilization. However, OFDM-based MIMO systems are highly sensitive to noise, making accurate modeling and optimization of the Signal-to-Noise Ratio (SNR) essential for enhancing channel capacity. This paper investigates MIMO-OFDM channel optimization using a capacity-based fitness function and applies four heuristic optimization algorithms—Greedy, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA)—to select and allocate sub-channels. The proposed framework aims to maximize system capacity while balancing computational complexity and solution quality. Among the evaluated methods, the Greedy algorithm achieves a channel capacity of 90 Mbps with a convergence time of 0.1 seconds, demonstrating its effectiveness for fast, near-optimal channel optimization in MIMO-OFDM systems.