AbstractIt is envisioned that the fifth generation (5G) networks will support the massive amount of data, reduce energy consumption and satisfy highly demanding quality-of-service (QoS). This can be achieved by integrating the key enabling technologies such as massive multiple-input multiple-output (MIMO) (i.e., the use of a massive number of antennas to provide high spectral efficiency for users), multi-cell massive MIMO (i.e., the ability to serve a larger number of users simultaneously), small cells with low-cost and flexible deployment (i.e.,the provision of better coverage and network performance), and cell-free (i.e., improvement in the freedom of network for improving the performance and reliability links).Resource allocation is very crucial in wireless communication systems. In 5G wireless networks, it is even more important as the new system must be more dynamic, wiser and must satisfy so many network demands simultaneously. Resource allocation in 5G networks now must face with many significant challenges. On one hand, with the scarcity of spectrum resources and the exceedingly increasing numbers of terminal devices in the age of the internet of things (IoT), communication systems encounter a huge obstacle in guaranteeing QoS for a large number of users. On the other hand, the issue of energy efficiency (EE) which is related to the resource allocation in the communication system, e.g, bandwidth allocation, power control, association allocation, and deployment strategies, has become one of the most critical problems in 5G networks. Besides the enhancement of spectral efficiency performance, an emerging trend of 5G wireless networks is the green communication in terms of EE (measured in bits/Joule, bits/Joule/Hz or bps/Hz/W). However, the most challenging in optimising EE merit is the fractional programming in optimisation field, i.e., non convex programming, which inflicts many difficult tasks for improving network EE performance. As a result, this thesis proposes novel approaches for 5G wireless communication techniques such as massive MIMO, multicell strategies, small cells, and cell-free networks, aiming at the maximal use of communication spectrum and resource allocation to maximize energy efficiency (EE) performance and support for a larger number of users.
|Date of Award||08 May 2018|
|Supervisor||Trung Q. Duong (Supervisor) & Hien-Quoc Ngo (Supervisor)|
Resource Management for Wireless Communications: An Energy Efficiency Approach
Nguyen, L. (Author). 08 May 2018
Student thesis: Doctoral Thesis › Doctor of Philosophy