Content delivery over multi-antenna wireless networks

The past few decades have witnessed unprecedented advances in information technology, which have significantly shaped the way we acquire and process information in our daily lives. Wireless communications has become the main means of access to data through mobile devices, resulting in a continuous exponential growth in wireless data traffic, mainly driven by the demand for high quality content. Various technologies have been proposed by researchers to tackle this growth in 5G and beyond, including the use of increasing number of antenna elements, integrated point-to-multipoint delivery and caching, which constitute the core of this thesis. In particular, we study non-orthogonal content delivery in multiuser multiple-input-single-output (MISO) systems. First, a joint beamforming strategy for simultaneous delivery of broadcast and unicast services is investigated, based on layered division multiplexing (LDM) as a means of superposition coding. The system performance in terms of minimum required power under prescribed quality-of-service (QoS) requirements is examined in comparison with time division multiplexing (TDM). It is demonstrated through simulations that the non-orthogonal delivery strategy based on LDM significantly outperforms the orthogonal strategy based on TDM in terms of system throughput and reliability. To facilitate efficient implementation of the LDM-based beamforming design, we further propose a dual decomposition-based distributed approach. Next, we study an efficient multicast beamforming design in cache-aided multiuser MISO systems, exploiting proactive content placement and coded delivery. It is observed that the complexity of this problem grows exponentially with the number of subfiles delivered to each user in each time slot, which itself grows exponentially with the number of users in the system. Therefore, we propose a low-complexity alternative through time-sharing that limits the number of subfiles that can be received by a user in each time slot. Moreover, a joint design of content delivery and multicast beamforming is proposed to further enhance the system performance, under the constraint on maximum number of subfiles each user can decode in each time slot. Finally, conclusions are drawn in Chapter 5, followed by an outlook for future works.Open Acces

D 3. 3 Final performance results and consolidated view on the most promising multi -node/multi -antenna transmission technologies

This document provides the most recent updates on the technical contributions and research challenges focused in WP3. Each Technology Component (TeC) has been evaluated under possible uniform assessment framework of WP3 which is based on the simulation guidelines of WP6. The performance assessment is supported by the simulation results which are in their mature and stable state. An update on the Most Promising Technology Approaches (MPTAs) and their associated TeCs is the main focus of this document. Based on the input of all the TeCs in WP3, a consolidated view of WP3 on the role of multinode/multi-antenna transmission technologies in 5G systems has also been provided. This consolidated view is further supported in this document by the presentation of the impact of MPTAs on METIS scenarios and the addressed METIS goals.Aziz, D.; Baracca, P.; De Carvalho, E.; Fantini, R.; Rajatheva, N.; Popovski, P.; Sørensen, JH.... (2015). D 3. 3 Final performance results and consolidated view on the most promising multi -node/multi -antenna transmission technologies. http://hdl.handle.net/10251/7675

D3.2 First performance results for multi -node/multi -antenna transmission technologies

This deliverable describes the current results of the multi-node/multi-antenna technologies investigated within METIS and analyses the interactions within and outside Work Package 3. Furthermore, it identifies the most promising technologies based on the current state of obtained results. This document provides a brief overview of the results in its first part. The second part, namely the Appendix, further details the results, describes the simulation alignment efforts conducted in the Work Package and the interaction of the Test Cases. The results described here show that the investigations conducted in Work Package 3 are maturing resulting in valuable innovative solutions for future 5G systems.Fantini. R.; Santos, A.; De Carvalho, E.; Rajatheva, N.; Popovski, P.; Baracca, P.; Aziz, D.... (2014). D3.2 First performance results for multi -node/multi -antenna transmission technologies. http://hdl.handle.net/10251/7675

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