In this paper, we study an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) architecture, in which a UAV roaming around the area may serve as a computing server to help user equipment (UEs) compute their tasks or act as a relay for further offloading their computation tasks to the access point (AP). We aim to minimize the weighted sum energy consumption of the UAV and UEs subject to the task constraints, the information-causality constraints, the bandwidth allocation constraints and the UAV's trajectory constraints. The required optimization is nonconvex, and an alternating optimization algorithm is proposed to jointly optimize the computation resource scheduling, bandwidth allocation, and the UAV's trajectory in an iterative fashion. Numerical results demonstrate that significant performance gain is obtained over conventional methods. Also, the advantages of the proposed algorithm are more prominent when handling computation-intensive latency-critical tasks.

This paper explores the potential of wireless power transfer (WPT) in massive multiple-input multiple-output (MIMO) aided heterogeneous networks (HetNets), where massive MIMO is applied in the macrocells, and users aim to harvest as much energy as possible and reduce the uplink path loss for enhancing their information transfer. By addressing the impact of massive MIMO on the user association, we compare and analyze user association schemes: 1) downlink received signal power (DRSP) based approach for maximizing the harvested energy; and 2) uplink received signal power (URSP) based approach for minimizing the uplink path loss. We adopt the linear maximalratio transmission (MRT) beamforming for massive MIMO power transfer to recharge users. By deriving new statistical properties, we obtain the exact and asymptotic expressions for the average harvested energy. Then we derive the average uplink achievable rate under the harvested energy constraint. Numerical results demonstrate that the use of massive MIMO antennas can improve both the users' harvested energy and uplink achievable rate in the HetNets, however, it has negligible effect on the ambient RF energy harvesting. Serving more users in the massive MIMO macrocells will deteriorate the uplink information transfer because of less harvested energy and more uplink interference. Moreover, although DRSP-based user association harvests more energy to provide larger uplink transmit power than the URSP-based one in the massive MIMO HetNets, URSPbased user association could achieve better performance in the uplink information transmission. Index TermsEnergy harvesting, heterogeneous network (HetNet), massive MIMO, user association, wireless power transfer. ). by (25) and (26), respectively.Corollary 4. If the number of antennas at the MBS is large for URSP-based user association, then we obtain the asymptotic expression for EURSP o,M by interchanging Ψ DRSP M∞ → Ψ URSP M∞ and r MS → r MS in (32).

This paper considers simultaneous wireless information and power transfer (SWIPT) in a multiple-input single-output (MISO) downlink system consisting of one multi-antenna transmitter, one single-antenna information receiver (IR), multiple multi-antenna eavesdroppers (Eves) and multiple single-antenna energy-harvesting receivers (ERs). The main objective is to keep the probability of the legitimate user's achievable secrecy rate outage as well as the ERs' harvested energy outage caused by channel state information (CSI) uncertainties below some prescribed thresholds. As is well known, the secrecy rate outage constraints present a significant analytical and computational challenge. Incorporating the energy harvesting (EH) outage constraints only intensifies that challenge. In this paper, we address this challenging issue using convex restriction approaches which are then proved to yield rank-one optimal beamforming solutions. Numerical results reveal the effectiveness of the proposed schemes.Comment: This is an open access article accepted for publication as a regular paper in the IEEE Transactions on Information Forensics & Security. Copyright (c) 2016 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubs-permissions@ieee.or

Caching at the wireless edge is a promising approach to dealing with massive content delivery in heterogeneous wireless networks, which have high demands on backhaul. In this paper, a typical cache-enabled small cell network under heterogeneous file and network settings is considered using maximum distance separable (MDS) codes for content restructuring. Unlike those in the literature considering online settings with the assumption of perfect user request information, we estimate the joint user requests using the file popularity information and aim to minimize the long-term average backhaul load for fetching content from external storage subject to the overall cache capacity constraint by optimizing the content placement in all the cells jointly. Both multicast-aware caching and cooperative caching schemes with optimal content placement are proposed. In order to combine the advantages of multicast content delivery and cooperative content sharing, a compound caching technique, which is referred to as multicast-aware cooperative caching, is then developed. For this technique, a greedy approach and a multicast-aware in-cluster cooperative approach are proposed for the small-scale networks and large-scale networks, respectively. Mathematical analysis and simulation results are presented to illustrate the advantages of MDS codes, multicast, and cooperation in terms of reducing the backhaul requirements for cacheenabled small cell networks.

In this letter, we study the optimization for cache content placement to minimize the backhaul load subject to cache capacity constraints for caching enabled small cell networks with heterogeneous file and cache sizes. Multicast content delivery is adopted to reduce the backhaul rate exploiting the independence among maximum distance separable coded packets.Index Terms-Heterogeneous networks, cache storage.