In multi-UAV networks, user equipment (UE) needs to connect to UAVs for both uplink (UL) and downlink (DL). Traditionally, UL and DL associations are coupled means a UE connects to the same UAV for both. However, this approach is inefficient due to the mobility of UAVs and network heterogeneity. Full-duplex (FD) communication in UAV networks complicates this problem. This paper introduces a novel decoupled UL-DL association (DUDe) framework. This allows each UE to associate with different UAVs for UL and DL transmissions. Such decoupling improves flexibility and enhances communication performance. However, UE association depends on UAV trajectories, making the prob-lem more complex. However, the dependence of UE association on UAV trajectories increases the complexity. This work formulates a joint optimization problem to maximize the total sum-rate of UEs in both UL and DL. To handle this uncertainty, a robust Partially Observable Markov Decision Process (POMDP) model is used. This helps model the uncertain environment where UAVs do not have complete information about the system state. A Multi-Agent Deep Reinforcement Learning (MADRL) ap-approach is proposed to solve this problem. Each UAV selects its policy in a decentralized manner. The training process is improved using a modified Proximal Policy Optimization (PPO) algorithm. It uses deep reinforcement learning to efficiently manage UAV mobility and UE connectivity. The findings suggest that DUDe-based associations outperform traditional coupled associations. It leads to better spectral efficiency and higher network throughput. The proposed framework and algorithms are validated through simulations and real-world scenarios.
Burra et al. (Sat,) studied this question.