Resource allocation for delay constrained wireless communications

The ultimate goal of future generation wireless communications is to provide ubiquitous seamless connections between mobile terminals such as mobile phones and computers so that users can enjoy high-quality services at anytime anywhere without wires. The feature to provide a wide range of delay constrained applications with diverse quality of service (QoS) requirements, such as delay and data rate requirements, will require QoS-driven wireless resource allocation mechanisms to efficiently allocate wireless resources, such as transmission power, time slots and spectrum, for accommodating heterogeneous mobile data. In addition, multiple-input-multiple-output (MIMO) antenna technique, which uses multiple antennas at the transmitter and receiver, can improve the transmission data rate significantly and is of particular interests for future high speed wireless communications. In the thesis, we develop smart energy efficient scheduling algorithms for delay constrained communications for single user and multi-user single-input-single-output (SISO) and MIMO transmission systems. Specifically, the algorithms are designed to minimize the total transmission power while satisfying individual user’s QoS constraints, such as rate, delay and rate or delay violation. Statistical channel information (SCI) and instantaneous channel state information (CSI) at the transmitter side are considered respectively, and the proposed design can be applied for either uplink or downlink. We propose to jointly deal with scheduling of the users that access to the channel for each frame time (or available spectrum) and how much power is allocated when accessing to the channel. In addition, the algorithms are applied with modifications for uplink scheduling in IEEE 802.16 Worldwide Interoperability for Microwave Access (WiMAX). The success of the proposed research will significantly improve the ways to design wireless resource allocation for delay constrained communications

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