Energy efficiency maximization for buffer-aided multi-UAV relaying communications

This paper studies a multiple unmanned aerial vehicle(UAV)relaying communication system,where multiple UAV re-lays assist the blocked communication between a group of ground users(GUs)and a base station(BS).Since the UAVs only have limited-energy in practice,our design aims to maximize the energy efficiency(EE)through jointly designing the communica-tion scheduling,the transmit power allocation,as well as UAV trajectory under the buffer constraint over a given flight period.Actually,the formulated fractional optimization problem is diffi-cult to be solved in general because of non-convexity.To re-solve this difficulty,an efficient iterative algorithm is proposed based on the block coordinate descent(BCD)and successive convex approximation(SCA)techniques,as well as the Dinkel-bach’s algorithm.Specifically,the optimization variables of the formulated problem are divided into three blocks and we alter-nately optimize each block of the variables over iteration.Numeri-cal results verify the convergence of the proposed iterative al-gorithm and show that the proposed designs achieve significant EE gain,which outperform other benchmark schemes.

User space transformation in deep learning based recommendation

Deep learning based recommendation methods, such as the recurrent neural network based recommendation method(RNNRec) and the gated recurrent unit(GRU) based recommendation method(GRURec), are proposed to solve the problem of time heterogeneous feedback recommendation. These methods out-perform several state-of-the-art methods. However, in RNNRec and GRURec, action vectors and item vectors are shared among users. The different meanings of the same action for different users are not considered. Similarly, different user preference for the same item is also ignored. To address this problem, the models of RNNRec and GRURec are modified in this paper. In the proposed methods, action vectors and item vectors are transformed into the user space for each user firstly, and then the transformed vectors are fed into the original neural networks of RNNRec and GRURec. The transformed action vectors and item vectors represent the user specified meaning of actions and the preference for items, which makes the proposed method obtain more accurate recommendation results. The experimental results on two real-life datasets indicate that the proposed method outperforms RNNRec and GRURec as well as other state-of-the-art approaches in most cases.