{"ID":5937004,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-09T15:22:02.48613467Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.05210","arxiv_id":"2607.05210","title":"Optimal Base Station Placement for Beyond 5G Networks with Non-Convex Topology","abstract":"This paper investigates the optimal placement of a millimeter-wave (mmWave) base station (BS) within a realistic U-shaped environment with non-convex topology. The problem is challenging and NP-hard due to the non-convex topology and the non-convex objective functions which are the sum-rate maximization and max-min fairness, the latter being additionally non-smooth. To address this challenge, the BS placement is formulated as a Markov Decision Process (MDP). Then, we propose two deep reinforcement learning (DRL) techniques: First, the deployment area is discretized into a grid and optimized using a Deep Q-Network (DQN). Second, the U-shaped region is partitioned into continuous subspaces, where a Deep Deterministic Policy Gradient (DDPG) agent is dedicated to each subspace then the best BS placement is selected among partitions. Results demonstrate that optimal placement achieves full coverage and yields a Jain index of 0.99. Furthermore, the proposed partitioned multi-space DDPG achieves better solution than DQN with lower complexity.","short_abstract":"This paper investigates the optimal placement of a millimeter-wave (mmWave) base station (BS) within a realistic U-shaped environment with non-convex topology. The problem is challenging and NP-hard due to the non-convex topology and the non-convex objective functions which are the sum-rate maximization and max-min fai...","url_abs":"https://arxiv.org/abs/2607.05210","url_pdf":"https://arxiv.org/pdf/2607.05210v1","authors":"[\"Mohamed Shalma\",\"Amr Mansour\",\"Ahmed El-Mahdy\"]","published":"2026-07-06T15:28:13Z","proceeding":"eess.SP","tasks":"[\"eess.SP\",\"cs.ET\"]","methods":"[\"Reinforcement Learning\"]","has_code":false}