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

A QoE based performance study of mobile peer-to-peer live video streaming

Peer-to-peer (P2P) Mobile Ad Hoc Networks (MANETs) are widely envisioned to be a practical platform to mobile live video streaming applications (e.g., mobile IPTV). However, the performance of such a streaming solution is still largely unknown. As such, in this paper, we aim to quantify the streaming performance using a Quality of Experience (QoE) based approach. Our simulation results indicate that video streaming performance is highly sensitive to the video chunk size. Specifically, if the chunk size is small, performance, in terms of both QoE and QoS, is guaranteed but at the expense of a higher overhead. On the other hand, if chunk size is increased, performance can degrade quite rapidly. Thus, it needs some careful fine tuning of chunk size to obtain satisfactory QoE performance. © 2012 IEEE.published_or_final_versio

Age of Information of a Server with Energy Requirements

We investigate a system with Poisson arrivals to two queues. One queue stores the status updates of the process of interest (or data packets) and the other handles the energy that is required to deliver the updates to the monitor. We consider that the energy is represented by packets of discrete unit. When an update ends service, it is sent to the energy queue and, if the energy queue has one packet, the update is delivered successfully and the energy packet disappears; however, in case the energy queue is empty, the update is lost. Both queues can handle, at most, one packet and the service time of updates is exponentially distributed. Using the Stochastic Hybrid System method, we characterize the average Age of Information of this system. Due to the difficulty of the derived expression, we also explore approximations of the average Age of Information of this systemJosu Doncel has received funding from the Department of Education of the Basque Government through the Consolidated Research Group MATHMODE (IT1294-19), from the Marie Sklodowska-Curie grant agreement No. 777778 and from from the Spanish Ministry of Science and Innovation with reference PID2019-108111RB-I00 (FEDER/AEI). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip