Reinforcement learning based joint self-optimisation scheme for fuzzy logic handover algorithm in 5G HetNets
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The heterogeneous networks (HetNets) in 5G can provide higher network coverage and system capacity to the user by deploying massive small base stations (BSs) within the 4G macro system. However, the large-scale deployment of small BSs significantly increases the complexity and workload of network maintenance and optimisation. On the other hand, the current handover (HO) triggering mechanism - A3 event was only designed for mobility management in the macro system. To implement A3 even directly in 5G-HetNets may cause degradation on the mobility robustness of user. Motivated by the concept of self-organisation networks (SON), this paper develops a self-optimisation triggering mechanism to enable automated network maintenance and enhance mobility robustness of user in 5G-HetNets. The proposed method integrates both advantages of subtractive clustering and Q-learning framework into the conventional fuzzy logic-based HO algorithm (FLHA). The subtractive clustering is first adopted to generate membership function (MF) for FLHA, which enable FLHA with the self-configuration feature. Subsequently, the Q-learning is utilised to learn the optimal HO policy from the environment as fuzzy rules that empower FLHA with self-optimisation function. The FLHA with SON functionality also overcomes the limitation of conventional FLHA that it must rely heavily on professional experience to design. The simulation results show that the proposed self-optimisation FLHA can effectively generate MF and fuzzy rules for FLHA. By comparing with conventional triggering mechanism, the proposed approach can decease approximately 91 ping-pong HO ratio and HO failure ratio while improving 8 throughput and latency respectively.
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