Multi-agent deep deterministic policy gradient algorithm for predictive UAV deployment in CF-mMIMO for identifying coverage holes

Unmanned aerial vehicles, due to their adaptable mobility and various applications, including supporting communication infrastructure, monitoring, and rescue, are becoming increasingly valuable, making them a valuable addition to emergency communication networks. Even though cell-free massive multiple input multiple outputs (CF-mMIMO) networks provide high communication data rates, their immobility makes it difficult to maintain quality network continuity in emergency, unpredictable, and congested areas where users’ equipment is located. To mitigate this challenge, the integration of aerial access points (AAPs) into CF-mMIMO networks is proposed by using the multi-agent deep deterministic policy gradient (MADDPG) framework, which teaches several unmanned aerial vehicles (UAVs) to jointly learn the best deployment plans by estimating user distributions and traffic demand trends on invitations to provide tremendous dynamic coverage, increased spectral efficiency, and throughput maximization. The predictive component framework utilizes a long short-term memory (LSTM) network model incorporating concepts of learning, association, movement, and service provision for temporal traffic forecasting, thereby ensuring proactive UAV positioning before coverage holes emerge. Our extensive simulation results demonstrate that the MADDPG-based throughput deployment strategy achieves approximately 45 Gb/s for 50 UAVs, spectral efficiency for downlink and uplink of 10.2 bps/Hz, 15.2 bps/Hz, respectively, and minimal transmit power of 3.5 kJ as compared with the multi-agent soft actor-critic (MASAC) method, traditional heuristic-LSTM, and single-agent reinforcement learning approaches.