A Dynamic TOPSIS-based Handover Decision Algorithm in Ultra-Dense Mobile Heterogeneous Networks

Handover decision-making in ultra-dense heterogeneous networks (HetNets) remains a key challenge in ensuring mobility robustness and minimizing service interruptions in 5 G systems. In this study, we propose a novel Dynamic TOPSISbased handover decision algorithm that adaptively adjusts the weight distribution of multiple decision criteria-such as RSRP, SINR, RSRQ, load, and distance-based on user equipment (UE) velocity. Through extensive simulations in a multi-tier 5G HetNet environment, the proposed method significantly outperforms both conventional conditional handover and static TOPSIS approaches in terms of reducing the number of handovers, ping-pong rates, and maintaining higher signal quality. Results further demonstrate the algorithm’s ability to dynamically adapt handover behavior across varying mobility scenarios. As future work, we aim to integrate Long Short-Term Memory (LSTM) networks with TOPSIS to develop a hybrid predictive handover model capable of learning temporal signal and mobility patterns for more intelligent and anticipatory decision-making.