Reinforcement Learning-based Joint Interference Mitigation and Resource Allocation in Dense Beyond 5G Heterogeneous Networks

The deployment of small cells (SCs) in heterogeneous beyond 5G (B5G) networks holds immense promise in meeting the ever-growing demand for data rates and ensuring the desired Quality-of-Service (QoS) for cell-edge users equipment (CEUEs) in densely populated B5G networks.In our pursuit of jointly maximizing the sum-rate while minimizing interference for CEUEs, we present an optimization-based approach with a specific focus on fulfilling the minimum QoS requirements of CEUEs.Our innovative two-step algorithm begins with a reinforcement learning (RL)-based matching process among UEs and available resources, followed by optimal power allocation to UEs based on the matched pairs.At the first step, by leveraging a Q-learning-based method, our algorithm identifies the optimal UEs-resources pairing.The learning process utilizes the achieved sum-rate of each pairing among UEs and resources at each step and converges to a sub-optimal pairing among them.In the second step, power allocation for the selected pairing of the first step is solved in optimal manner.Achieving optimal power allocation is facilitated by exploiting the difference of concaves form of the objective function and harnessing the majorization-minimization (MaMi) technique considering the minimum required QoS of CEUEs.Our numerical results showcase the effectiveness of the proposed scheme, demonstrating near-optimal performance.The results show the employed RL approach effectively converges to the near-optimal pairing among UEs and resources in a dense environment.Additionally, it is evident that the optimal power allocation not only maximizes the sum-rate but also minimizes interference for CEUEs.Considering different values of macro cell (MC) transmission power and SC radius, the proposed schemes achieve a sum-rate enhancement of at least 10% and 25% compared to other existing matching and power allocation methods, respectively.