Distributed RAN Slicing Based on MATD3 Joint With Evolutionary Game Assisted User Association for MEC-Enabled HetNets

To cope with the dramatic growth of future network traffic and the diversity of services, beyond fifth-generation (B5G) and sixth-generation (6G) wireless communication need to balance the network load while being service-oriented. In this paper, we consider a multi-access edge computing (MEC) driven radio access network (RAN) slicing scenario in a heterogeneous cellular network (HetNet) with local traffic overload. First, since users are generally non absolutely rational when selecting base stations (BSs), an evolutionary game (EG) based user association (UA) scheme is proposed to solve the problem of overload. Specifically, we innovatively define the load function of the base stations (BSs) and combine resource capacity of BSs to form the payoff function to dynamically adjust the load. Second, we model the network slicing (NS) problem using the transmission rate, average latency and quality of service (QoS). The problem is further relaxed and an NS algorithm based on distributed successive convex approximation (DSCA) is presented to derive a theoretical upper bound reference value as a static offline criterion. Finally, considering the high randomness of user task arrival in the real scenario, we formulate the multi-base station slicing problem as a stochastic game (SG) and a multi-agent twin delayed deep deterministic policy gradient (MATD3)-based distributed network slicing algorithm is proposed to obtain excellent slicing strategies. Simulation results show that our proposed UA algorithm has a unique evolutionary equilibrium (EE) solution and is highly scalable. The proposed MATD3-based NS algorithm has better performance compared to other baseline algorithms and converges to a utility value that best approximates the theoretical upper bound.