Joint Trajectory and Transmit Power Design for Cellular-Connected UAVs via Differentiable Channel Knowledge Map

Channel knowledge map (CKM) has become a potential technique to enhance communication performance by exploiting actual radio propagation information, especially in communication between uncrewed aerial vehicles (UAVs) and ground base stations (GBSs). However, CKM constructed by existing methods cannot obtain differentiable expressions from locations to the channel information, rendering it unsuitable for the traditional communication design. This work proposes a site-specific differentiable CKM to jointly design UAV trajectories and transmit power. First, assuming sufficient channel samples collected by a GBS, the CKM is constructed for this specific site as a differentiable back propagation neural network (BPNN). To enable CKM migration towards nearby GBSs, we adopt the transfer learning mechanism to set up new CKMs that require significantly less training samples. Next, leveraging CKM-stored channel knowledge, we investigate the multi-UAV trajectory design and power control strategy, while the UAVs are traversing the network coverage area with designated starting and destination points. Specifically, the minimal average rate between UAVs and associated GBSs is maximized along the designed trajectories, which is solved by continuous convex optimization based on the differentiable CKMs. Numerical results show that the BPNN and transfer learning can effectively construct high-accuracy CKMs, while reducing the overall training cost. It is also shown that the proposed joint trajectory and power optimization based on the CKM-assisted architecture achieves improved minimal average rate compared to the alternating optimization method based on distance-dependent path-loss models and existing CKM-based methods with fixed power configurations, since both site-specific environmental information and power optimization are exploited.