Edge-Caching Wireless Networks: Performance Analysis and Optimization

Edge-caching has received much attention as an efficient technique to reduce delivery latency and network congestion during peak-traffic times by bringing data closer to end users. Existing works usually design caching algorithms separately from physical layer design. In this paper, we analyse edge-caching wireless networks by taking into account the caching capability when designing the signal transmission. Particularly, we investigate multi-layer caching where both base station (BS) and users are capable of storing content data in their local cache and analyse the performance of edge-caching wireless networks under two notable uncoded and coded caching strategies. Firstly, we propose a coded caching strategy that is applied to arbitrary values of cache size. The required backhaul and access rates are derived as a function of the BS and user cache size. Secondly, closed-form expressions for the system energy efficiency (EE) corresponding to the two caching methods are derived. Based on the derived formulas, the system EE is maximized via precoding vectors design and optimization while satisfying a predefined user request rate. Thirdly, two optimization problems are proposed to minimize the content delivery time for the two caching strategies. Finally, numerical results are presented to verify the effectiveness of the two caching methods.Comment: to appear in IEEE Trans. Wireless Commu

Drone Mobile Networks: Performance Analysis Under 3D Tractable Mobility Models

Reliable wireless communication networks are a significant but challenging mission for post-disaster areas and hotspots in the era of information. However, with the maturity of unmanned aerial vehicle (UAV) technology, drone mobile networks have attracted considerable attention as a prominent solution for facilitating critical communications. This paper provides a system-level analysis for drone mobile networks on a finite three-dimensional (3D) space. Our aim is to explore the fundamental performance limits of drone mobile networks taking into account practical considerations. Most existing works on mobile drone networks use simplified mobility models (e.g., fixed height), but the movement of the drones in practice is significantly more complicated, which leads to difficulties in analyzing the performance of the drone mobile networks. Hence, to tackle this problem, we propose a stochastic geometry-based framework with a number of different mobility models including a random Brownian motion approach. The proposed framework allows to circumvent the extremely complex reality model and obtain upper and lower performance bounds for drone networks in practice. Also, we explicitly consider certain constraints, such as the small-scale fading characteristics relying on line-of-sight (LOS) and non line-of-sight (NLOS) propagation, and multi-antenna operations. The validity of the mathematical findings is verified via Monte-Carlo (MC) simulations for various network settings. In addition, the results reveal some design guidelines and important trends for the practical deployment of drone networks

Exploiting Caching and Multicast for 5G Wireless Networks

The landscape toward 5G wireless communication is currently unclear, and, despite the efforts of academia and industry in evolving traditional cellular networks, the enabling technology for 5G is still obscure. This paper puts forward a network paradigm toward next-generation cellular networks, targeting to satisfy the explosive demand for mobile data while minimizing energy expenditures. The paradigm builds on two principles; namely caching and multicast. On one hand, caching policies disperse popular content files at the wireless edge, e.g., pico-cells and femto-cells, hence shortening the distance between content and requester. On other hand, due to the broadcast nature of wireless medium, requests for identical files occurring at nearby times are aggregated and served through a common multicast stream. To better exploit the available cache space, caching policies are optimized based on multicast transmissions. We show that the multicast-aware caching problem is NP-hard and develop solutions with performance guarantees using randomized-rounding techniques. Trace-driven numerical results show that in the presence of massive demand for delay tolerant content, combining caching and multicast can indeed reduce energy costs. The gains over existing caching schemes are 19% when users tolerate delay of three minutes, increasing further with the steepness of content access pattern

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Full-Duplex Cloud Radio Access Network: Stochastic Design and Analysis

Full-duplex (FD) has emerged as a disruptive communications paradigm for enhancing the achievable spectral efficiency (SE), thanks to the recent major breakthroughs in self-interference (SI) mitigation. The FD versus half-duplex (HD) SE gain, in cellular networks, is however largely limited by the mutual-interference (MI) between the downlink (DL) and the uplink (UL). A potential remedy for tackling the MI bottleneck is through cooperative communications. This paper provides a stochastic design and analysis of FD enabled cloud radio access network (C-RAN) under the Poisson point process (PPP)-based abstraction model of multi-antenna radio units (RUs) and user equipments (UEs). We consider different disjoint and user-centric approaches towards the formation of finite clusters in the C-RAN. Contrary to most existing studies, we explicitly take into consideration non-isotropic fading channel conditions and finite-capacity fronthaul links. Accordingly, upper-bound expressions for the C-RAN DL and UL SEs, involving the statistics of all intended and interfering signals, are derived. The performance of the FD C-RAN is investigated through the proposed theoretical framework and Monte-Carlo (MC) simulations. The results indicate that significant FD versus HD C-RAN SE gains can be achieved, particularly in the presence of sufficient-capacity fronthaul links and advanced interference cancellation capabilities