Some MIMO applications in cognitive radio networks

In the last decade, the wireless communication technology has witnessed a rapid development, which led to a rapid growth in wireless applications and services. However, the radio spectrum resources scarcity resulting from using the traditional methods of fixed spectrum resources allocation has potential constraints on this wireless services rapid growth. Consequently, cognitive radio has been emerged as a possible solution for alleviating this spectrum scarcity problem by employing dynamic resource allocation strategies in order to utilize the available spectrum in a more efficient way so that finding opportunities for new wireless application services could be achieved. In cognitive radio networks, the radio spectrum resources utilization is improved by allowing unlicensed users, known as secondary users, to share the spectrum with licensed users, known as primary users, as long as this sharing do not induce harmful interference on the primary users, which completely entitled to utilize the spectrum. Motivated by MIMO techniques that have been used in practical systems as a means for high data rate transmission and a source for spatial diversity, and by its ease implementation with OFDM, different issues in multi-user MIMO (MU-MIMO) in both the uplink and downlink in the context of cognitive radio are studied in this thesis. More specifically, in the first thrust of this thesis, the spectrum spatial holes which could exist in an uplink MU-MIMO cell as a result of the possible free spatial dimensions resulted from the sparse activity of the primary users is studied; a modified sensing algorithm for these spectrum spatial holes that exploit both the block structure of the OFDM signals and the correlation of their activity states along time are proposed. The second thrust is concerned with cognitive radio relaying in the physical layer where the cognitive radio base station (CBS) relays the PU signal while transmitting its own signals to its SUs. We define secondary users with different priorities (different quality of service requirements); the different levels of priority for SUs are achieved by a newly proposed simple linear scheme based on zero forcing called Hierarchal Priority Zero Forcing scheme HPZF

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