Vertical switching algorithm of fuzzy logic heterogeneous networks based on reinforcement learning assistance

In response to the challenge of users lacking access to accurate network attributes, this paper proposes a heterogeneous network vertical handover algorithm that combines a fuzzy logic system with reinforcement learning. It begins by introducing the three modules of the fuzzy logic system and then delves into the Q-learning method in reinforcement learning. The paper proceeds to compare the proposed algorithm with three other methods using five metrics: bandwidth, latency, jitter, packet loss, and cost. Finally, it discusses the current issues and challenges faced by existing vertical handover algorithms. Simulation results indicate that this approach outperforms the other three methods in terms of switching frequency and the specified metrics, including bandwidth, jitter, latency, packet loss, and cost.