Integrating Sensing, Communication, and Computation in the Sky

Unmanned Aerial Vehicle (UAV)-mounted edge devices are particularly advantageous for federated edge learning (FEEL) due to their flexibility and mobility in efficient data collection. In UAV-assisted FEEL, sensing, computation, and communication are coupled and compete for limited onboard resources, and UAV deployment also affects sensing and communication performance. Therefore, the joint design of UAV deployment and resource allocation is crucial to achieving the optimal training performance. In this paper, we address the problem of joint UAV deployment design and resource allocation for FEEL via a concrete case study of human motion recognition based on wireless sensing. Due to the nonideal sensing channels, we consider the probabilistic sensing model. Then, we derive the upper bound of the FEEL training loss as a function of the sensing probability. We formulate a training time minimization problem by jointly optimizing UAV deployment, integrated sensing, computation, and communication (ISCC) resources under a desirable optimality gap constraint. To solve this challenging mixed-integer non-convex problem, we propose our algorithm based on the alternating optimization technique. Simulation results demonstrate that our algorithm outperforms other baselines regarding convergence rate and testing accuracy.