New Methods of Efficient Base Station Control for Green Wireless Communications

Abstract

This dissertation reports a study on developing new methods of efficient base station (BS) control for green wireless communications. The BS control schemes may be classified into three different types depending on the time scale — hours based, minutes based, and milli-seconds based. Specifically, hours basis pertains to determining which BSs to switch on or off

minutes basis pertains to user equipment (UE) association

and milli-seconds basis pertains to UE scheduling and radio resource allocation. For system model, the dissertation considers two different models — heterogeneous networks composed of cellular networks and wireless local area networks (WLANs), and cellular networks adopting orthogonal frequency division multiple access (OFDMA) with carrier aggregation (CA). By combining each system model with a pertinent BS control scheme, the dissertation presents three new methods for green wireless communications: 1) BS switching on/off and UE association in heterogeneous networks, 2) optimal radio resource allocation in heterogeneous networks, and 3) energy efficient UE scheduling for CA in OFDMA based cellular networks. The first part of the dissertation presents an algorithm that performs BS switchingon/off and UE association jointly in heterogeneous networks composed of cellular networks and WLANs. It first formulates a general problem which minimizes the total cost function which is designed to balance the energy consumption of overall network and the revenue of cellular networks. Given that the time scale for determining the set of active BSs is much larger than that for UE association, the problem may be decomposed into a UE association algorithm and a BS switching on/off algorithm, and then an optimal UE association policy may be devised for the UE association problem. Since BS switching-on/off problem is a challenging combinatorial problem, two heuristic algorithms are proposed based on the total cost function and the density of access points of WLANs within the coverage of each BS, respectively. According to simulations, the two heuristic algorithms turn out to considerably reduce energy consumption when compared with the case where all the BSs are always turned on. The second part of the dissertation presents an energy-per-bit minimized radioresource allocation scheme in heterogeneous networks equipped with multi-homing capability which connects to different wireless interfaces simultaneously. Specifically, an optimization problem is formulated for the objective of minimizing the energy-per-bit which takes a form of nonlinear fractional programming. Then, a parametric optimization problem is derived out of that fractional programming and the original problem is solved by using a double-loop iteration method. In each iteration, the optimal resource allocation policy is derived by applying Lagrangian duality and an efficient dual update method. In addition, suboptimal resource allocation algorithms are developed by using the properties of the optimal resource allocation policy. Simulation results reveal that the optimal allocation algorithm improves energy efficiency significantly over the existing resource allocation algorithms designed for homogeneous networks and its performance is superior to suboptimal algorithms in reducing energy consumption as well as in enhancing network energy efficiency. The third part of the dissertation presents an energy efficient scheduling algorithm for CA in OFDMA based wireless networks. In support of this, the energy efficiency is newly defined as the ratio of the time-averaged downlink data rate and the time-averaged power consumption of the UE, which is important especially for battery-constrained UEs. Then, a component carrier and resource block allocation problem is formulated such that the proportional fairness of the energy efficiency is guaranteed. Since it is very complicated to determine the optimal solution, a low complexity energy-efficient scheduling algorithm is developed, which approaches the optimal algorithm. Simulation results demonstrate that the proposed scheduling scheme performs close to the optimal scheme and outperforms the existing scheduling schemes for CA.