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

Non-Orthogonal Multiple Access for 5G: Design and Performance Enhancement

PhDSpectrum scarcity is one of the most important challenges in wireless communications networks due to the sky-rocketing growth of multimedia applications. As the latest member of the multiple access family, non-orthogonal multiple access (NOMA) has been recently proposed for 3GPP Long Term Evolution (LTE) and envisioned to be a key component of the 5th generation (5G) mobile networks for its potential ability on spectrum enhancement. The feature of NOMA is to serve multiple users at the same time/frequency/code, but with di erent power levels, which yields a signi cant spectral e ciency gain over conventional orthogonal multiple access (OMA). This thesis provides a systematic treatment of this newly emerging technology, from the basic principles of NOMA, to its combination with simultaneously information and wireless power transfer (SWIPT) technology, to apply in cognitive radio (CR) networks and Heterogeneous networks (HetNets), as well as enhancing the physical layer security and addressing the fairness issue. First, this thesis examines the application of SWIPT to NOMA networks with spatially randomly located users. A new cooperative SWIPT NOMA protocol is proposed, in which near NOMA users that are close to the source act as energy harvesting relays in the aid of far NOMA users. Three user selection schemes are proposed to investigate the e ect of locations on the performance. Besides the closed-form expressions in terms of outage probability and throughput, the diversity gain of the considered networks is determined. Second, when considering NOMA in CR networks, stochastic geometry tools are used to evaluate the outage performance of the considered network. New closed-form expressions are derived for the outage probability. Diversity order of NOMA users has been analyzed based on the derived outage probability, which reveals important design insights regarding the interplay between two power constraints scenarios. Third, a new promising transmission framework is proposed, in which massive multipleinput multiple-output (MIMO) is employed in macro cells and NOMA is adopted in small cells. For maximizing the biased average received power at mobile users, a massive MIMO and NOMA based user association scheme is developed. Analytical expressions for the spectrum e ciency of each tier are derived using stochastic geometry. It is con rmed that NOMA is capable of enhancing the spectrum e ciency of the network compared to the OMA based HetNets. Fourth, this thesis investigates the physical layer security of NOMA in large-scale networks with invoking stochastic geometry. Both single-antenna and multiple-antenna aided transmission scenarios are considered, where the base station (BS) communicates with randomly distributed NOMA users. In addition to the derived exact analytical expressions for each scenario, some important insights such as secrecy diversity order and large antenna array property are obtained by carrying the asymptotic analysis. Fifth and last, the fundamental issues of fairness surrounding the joint power allocation and dynamic user clustering are addressed in MIMO-NOMA systems in this thesis. A two-step optimization approach is proposed to solve the formulated problem. Three e cient suboptimal algorithms are proposed to reduce the computational complexity. To further improve the performance of the worst user in each cluster, power allocation coe cients are optimized by using bi-section search. Important insights are concluded from the generated simulate results