Particle-Swarm-Optimised Neural Interference Management for MIMO Architectures in Next-Generation Wireless Network

The interlinked nature of mobile users and terminals has been significantly impacted by Wireless Communication System (WCS). A hybrid Particle Swarm Optimized Artificial Neural Network (PSO ANN) framework is presented in this paper for proactive downlink interference mitigation in multi cell Orthogonal Frequency Division Multiple Access (OFDMA) systems. Large scale route loss, instantaneous Signal to Interference Plus Noise Ratio (SINR), user mobility vectors, and neighboring cell power budgets are all included in the compact feature vector that is taken from live radio measurements and fed into a multilayer artificial neural network. PSO globally adjusts the network’s initial weights and biases prior to traditional back propagation refinements in order to avoid poor local minima and speed up convergence. Simulations are conducted and the proposed PSO ANN outperforms standalone neural networks and traditional reuse schemes in terms of signal quality, network throughput, and interference suppression. The method is resilient across a broad range of user mobility and traffic volumes while maintaining inference delays that are consistent with ultra reliable low latency communication needs. According to the results, PSO ANN is a scalable and effective option for managing radio resources in future wireless infrastructures while taking interference into account.