The Role of Parked Cars in Content Downloading for Vehicular Networks

When it comes to content access using Inter-Vehicle Communication (IVC), data will mostly flow through Road Side Units (RSUs), deployed in our cities. Unfortunately, the RSU coverage is expected to be rather scattered. Instead of relying on RSUs only, the paper investigate the possibility of exploiting parked vehicles to extend the RSU service coverage. Our approach leverages optimization models aiming at maximizing the freshness of content that downloaders retrieve, the efficiency in the utilization of radio resources, and the fairness in exploiting the energy resources of parked vehicles. The latter is constrained so as not to excessively drain parked vehicle batteries. Our approach provides an estimate of the system performance, even in those cases where users may only be willing to lease a limited amount of their battery capacity to extend RSU coverage. Our optimization-based results are validated by comparing them against ns-3 simulations. Performance evaluation highlights that the use of parked vehicles enhances the efficiency of the content downloading process by 25%-35% and can offload more than half the data traffic from RSUs, with respect to the case where only moving cars are used as relays. Such gains in performance come at a small cost in terms of battery utilization for the parked vehicles, and they are magnified when a backbone of parked vehicles can be formed

Cooperative video transmission strategies via caching in small-cell networks

Small-cell network is a promising solution to the high video traffic. However, it has some fundamental problems, i.e., high backhaul cost, quality of experience (QoE) and interference. To address these issues, we propose a cooperative transmission strategy for video transmission in small-cell networks with caching. In the scheme, each video file is encoded into segments using a maximum distance separable rateless code. Then, a portion of each segment is cached at a certain small-cell base station (SBS), so that the SBSs can cooperatively transmit these segments to users without incurring high backhaul cost. When there is only one active user in the network, a greedy algorithm is utilized to deliver the video-file segment from the SBS with good channel state to the user watching videos in real time. This reduces video freezes and improves the QoE. When there exist several active users, interference will appear among them. To deal with interference, interference alignment (IA) is adopted. Based on the scheme for a single user, the greedy algorithm and IA are combined to transmit video-file segments to these users, and the performance of the system can be significantly improved. Simulation results are presented to show the effectiveness of the proposed scheme

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