Intelligent Multi-Objective Optimization of 4G LTE Performance Using an AI–WOA Framework

Abstract

We introduce a smart multi-objective optimization framework towards the performance enhancement of the 4G LTE network using an integrated Artificial Intelligence (AI) and Whale Optimization Algorithm (WOA) setup. This approach aims to integrate tested regression-based KPI prediction along with decision-variable-based meta-heuristic optimization in a reproducible experimental environment within MATLAB R2023b. A weighted multi-objective formulation for optimizing the performance of 4 key indicators, i.e., throughput, latency, packet loss ratio, and energy efficiency, is jointly executed. By comparison against existing baseline LTE configuration, we observe a 25.3% throughput improvement, 28.9% reduction in latency, 38.7% reduction in packet loss ratio, and 23.4% improvement in energy efficiency. We validate robustness with weight sensitivity analysis (±10% and ±20%), population scalability testing (N = 20, 30, 50), decay-strategy comparison (linear vs. exponential), and Pareto-front approximation (2,960 non-dominated solutions from 3,000 samples). The above results corroborate that, after approximately 70 iterations, convergence is stable and performance is consistent over the span of 30 independent runs. Thus, the proposed AI–WOA construction offers an organized and replicable process to fine-tune LTE performance optimization, a basis for downstream implementations towards a more distributed and next-generation wireless environment.