Adaptive subframe allocation for next generation multimedia delivery over hybrid LTE unicast broadcast

The continued global roll-out of long term evolution (LTE) networks is providing mobile users with perpetually increasing ubiquitous access to a rich selection of high quality multimedia. Interactive viewing experiences including 3-D or free-viewpoint video require the synchronous delivery of multiple video streams. This paper presents a novel hybrid unicast broadcast synchronisation (HUBS) framework to synchronously deliver multi-stream content. Previous techniques on hybrid LTE implementations include staggered modulation and coding scheme grouping, adaptive modulation coding or implementing error recover techniques; the work presented here instead focuses on dynamic allocation of resources between unicast and broadcast, improving stream synchronisation as well as overall cell resource usage. Furthermore, the HUBS framework has been developed to work within the limitations imposed by the LTE specification. Performance evaluation of the framework is performed through the simulation of probable future scenarios, where a popular live event is broadcast with stereo 3-D or multi-angle companion views interactively offered to capable users. The proposed framework forms a ``HUBS group'' that monitors the radio bearer queues to establish a time lead or lag between broadcast and unicast streams. Since unicast and broadcast share the same radio resources, the number of subframes allocated to the broadcast transmission are then dynamically increased or decreased to minimise the average lead/lag time offset between the streams. Dynamic allocation showed improvements for all services across the cell, whilst keeping streams synchronised despite increased user loading

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