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Energy and Spectral Efficiency in Wireless Heterogeneous Networks

  • Author(s): Coskun, Cemil Can
  • Advisor(s): Ayanoglu, Ender
  • et al.
Creative Commons Attribution 4.0 International Public License
Abstract

The focus of this dissertation is investigating energy and spectral efficiency in wireless heterogeneous networks (HetNets). Our goal is to improve the energy efficiency and spectral efficiency of the HetNets while satisfying the minimum rate requirements of the users. The contributions of this dissertations are (i) to develop an energy-efficient base station deployment framework for HetNets, (ii) to increase energy efficiency of the HetNets while satisfying minimum rate requirements of users, and (iii) to investigate energy efficiency-spectral efficiency tradeoff in HetNets. First, we address the micro base station deployment problem in HetNets. Although micro base station deployment increases the total capacity of the network, increasing the number of micro base stations excessively may reduce the energy efficiency of the network. Therefore, we examine the energy efficiency aspect of the micro base station deployment problem. We propose a greedy deployment algorithm which is a constant-factor approximation of the optimal solution. Second, we investigate the energy efficiency of downlink transmission in multi-cell HetNets. Our objective is to satisfy the rate requirement of users while maximizing energy efficiency of the network. We divide the problem into three subproblems: cell-center region boundary selection for fractional frequency reuse (FFR), scheduling, and power allocation subproblems. We propose a three-stage algorithm, and apply it iteratively until convergence. We demonstrate that significant gains can be achieved in terms of energy efficiency and outage probability using the proposed algorithm. Third, we investigate the energy efficiency-spectral efficiency tradeoff in multi-cell HetNets. Our objective is to maximize both energy efficiency and spectral efficiency of the network while satisfying the rate requirements of users. We define our objective function as the weighted summation of energy efficiency and spectral efficiency. We derive the Pareto optimal solution that strikes a balance between the spectral efficiency and energy efficiency.

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