Max-Min Fair Mobility Management with Minimum Resource Reservation in 5G

Contemporary network applications require ultra-reliable and low-latency connectivity. However, existing 5G mobility procedures, including the newly introduced 3GPP Release 18 mobility procedure, called L1/L2-Triggered Mobility (LTM), still face challenges in providing fairness, wasting reserved resources, and high signaling overhead, especially in dense networks. In this work, we investigate max-min fair mobility management that jointly optimizes user-to-Base Station (BS) assignment, resource allocation, and cell preparation decisions. We formulate a multi-objective optimization problem with the goal of providing max-min fairness in user data rates while minimizing resource reservation overhead due to the unnecessary cell preparations. We derive an upper bound through a problem transformation and decision variable relaxation. Then, we propose a context-aware Deep Reinforcement Learning (DRL)-based solution, with decisions guided via action masking. Our approach uses a two-phase decision process that separates BS selection and resource allocation from cell preparation decisions for a better performance. Extensive 3GPP-compliant simulations in dynamic macro/micro scenarios demonstrate that our DLR-based algorithm significantly improves fairness and resource efficiency, reducing resource reservation by ≈ 60% compared to the baselines, and achieves an optimality gap of 15%.