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.

This letter considers the multiple-input single-output (MISO) broadcast system for simultaneous wireless information and power transfer (SWIPT) using receiver power splitting and aims to optimize jointly the beamforming vectors and the power splitting ratios for minimizing the transmit power of the base station (BS) subject to the individual signal-to-interference-plusnoise ratio (SINR) and the energy-harvesting constraints at the mobile stations (MSs). However, the CSI is assumed imperfect but has a deterministic uncertainty region. Unlike existing attempts that resort to iterations guided by semidefinite relaxation (SDR), we propose a reverse convex nonsmooth optimization algorithm, which provides the near-optimal rank-one solution.Index Terms-Energy harvesting, power splitting, optimal beamforming, broadcast, MISO.

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We consider high-dimensional multiuser MIMO transmissions in Frequency Division Duplexing systems. For precoding, the frequency selective channel has to be measured, quantized and fed back to the base station by the users. In 5G New Radio (NR), a modular quantization approach has been applied for this, where first a low-dimensional subspace is identified for the whole frequency selective channel, and then subband channels are linearly mapped to this subspace and quantized. We analyze how the components in such a modular scheme contribute to the overall quantization distortion. Based on this analysis we improve the technology components in the modular approach. We compare the improved quantization scheme to the 5G NR standardized version by simulation in a scenario with a realistic spatial channel model. The improvements lead to a more than 25% improvement in spectral efficiency.