Whale Optimization Algorithm Aided Markov Chain for Mobility Prediction

Trajectory prediction remains a significant operation in mobile communications. In 5G and Beyond (B5G) networks, next-cell prediction for User Equipment (UE) becomes increasingly critical amid the exponential network complexity driven by unprecedented subscriber growth. Markov Chains are selected for their simplicity, interpretability, low computational demands, and proven effectiveness in modeling sequential mobility patterns, which makes them ideal for real-time predictions, despite the existence of more complex alternatives. Paralleling the growing interest in metaheuristic (MH) algorithms for parameter optimization, this paper employs the Whale Optimization Algorithm (WOA) to select the optimal Markov Chain order for each UE trajectory, thereby enhancing next-location prediction. Compared to the traditional fixed-order Markov Chain, the proposed method boosts average prediction accuracy by 20%.