This paper studies a federated edge learning system, in which an edge server coordinates a set of edge devices to train a shared machine learning (ML) model based on their locally distributed data samples. During the distributed training, we exploit the joint communication and computation design for improving the system energy efficiency, in which both the communication resource allocation for global ML-parameters aggregation and the computation resource allocation for locally updating MLparameters are jointly optimized. In particular, we consider two transmission protocols for edge devices to upload ML-parameters to edge server, based on the non-orthogonal multiple access (NOMA) and time division multiple access (TDMA), respectively. Under both protocols, we minimize the total energy consumption at all edge devices over a particular finite training duration subject to a given training accuracy, by jointly optimizing the transmission power and rates at edge devices for uploading MLparameters and their central processing unit (CPU) frequencies for local update. We propose efficient algorithms to optimally solve the formulated energy minimization problems by using the techniques from convex optimization. Numerical results show that as compared to other benchmark schemes, our proposed joint communication and computation design significantly improves the energy efficiency of the federated edge learning system, by properly balancing the energy tradeoff between communication and computation.

This letter studies an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) system, in which a UAV-mounted energy transmitter (ET) optimizes its positioning locations over time to efficiently charge a set of energy receivers (ERs) distributed on the ground. Different from conventional designs based on deterministic (e.g., line-of-sight (LoS)) or stochastic (e.g., probabilistic LoS) channel models, we consider a new radio-map-based design approach, in which the UAV exploits the information of channel propagation environments for efficient positioning optimization. By practically assuming that the UAV only partially knows the ERs' locations, our objective is to maximize the minimum energy transferred to all ERs over a particular charging duration that is sufficiently long. By applying the robust optimization and Lagrange duality method, we obtain an efficient solution to the minimum energy maximization problem, which has an interesting multi-locationpositioning structure. Numerical results show that our proposed radio-map-based robust design significantly improves the WPT performance, as compared to conventional designs based on LoS and probabilistic LoS channel models.Index Terms-Unmanned aerial vehicle (UAV), wireless power transfer (WPT), positioning optimization, radio map, robust optimization.

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