NOMA-based intelligent resource allocation and trajectory optimization for multi-UAVs assisted semantic communication networks

The limited spectrum resources have a particular impact on UAV-assisted semantic communication networks, which undoubtedly leads to poorer quality of service for users and inefficient communication. Therefore, a NOMA-based multi-UAVs assisted semantic cellular network framework is proposed in this paper, in which each UAV transmits semantic information to multiple users in the shared spectrum resource with different power using non-orthogonal multiple access transmission protocol, thereby achieving higher spectrum utilization. We optimize the quantity of semantic symbols, UAV trajectories, and power allocation concurrently to increase communication efficiency by maximizing the sum rate of semantic information transmission for all users. However, conventional convex optimization approaches have difficulty solving it due to the bi-directional mobility of UAVs and users. Therefore, an enhanced K-means algorithm is employed to create the relationship between UAVs and users periodically. Additionally, a deep reinforcement learning technique based on shared dueling double deep Q networks (SD3QN) is also presented to maximize the quantity of semantic symbols, 3D trajectories, and power allocation. Experimental results show that the proposed semantic cellular network achieves higher spectral efficiency. Meanwhile, the proposed algorithm can effectively reduce the training time and avoid the overestimation problem in Deep Q Networks (DQN). Furthermore, the suggested optimization strategy outperforms the benchmark schemes in terms of semantic sum rate.