Zhijiat Chong

Framework for Link-Level Energy Efficiency Optimization with Informed Transmitter

The dramatic increase of network infrastructure comes at the cost of rapidly increasing energy consumption, which makes optimization of energy efficiency (EE) an important topic. Since EE is often modeled as the ratio of rate to power, we present a mathematical framework called fractional programming that provides insight into this class of optimization problems, as well as algorithms for computing the solution. The main idea is that the objective function is transformed to a weighted sum of rate and power. A generic problem formulation for systems dissipating transmit-independent circuit power in addition to transmit-dependent power is presented. We show that a broad class of EE maximization problems can be solved efficiently, provided the rate is a concave function of the transmit power. We elaborate examples of various system models including time-varying parallel channels. Rate functions with an arbitrary discrete modulation scheme are also treated. The examples considered lead to water-filling solutions, but these are different from the dual problems of power minimization under rate constraints and rate maximization under power constraints, respectively, because the constraints need not be active. We also demonstrate that if the solution to a rate maximization problem is known, it can be utilized to reduce the EE problem into a one-dimensional convex problem.

Energy-efficient power control for MIMO time-varying channels

Fast rising data traffic in mobile communication entails an increase in its energy consumption, which may result in climate effects and high operation costs, unless measures are taken to enhance its energy efficiency (EE). As part of the endeavour towards higher EE, we present a power control policy that adapts to a time-varying channel for multiple antenna communication. We provide a power budget model that reflects the power expenditure of a base station, that even accounts for the power used for training which enables channel information at the transmitter. We apply the results to Rayleigh fading channels and compare them with a suboptimal power policy where a constant power allocation is applied at all times. The relationship between the EE and the antenna configuration is discussed.

Analytical Foundation for Energy Efficiency Optimisation in Cellular Networks with Elastic Traffic

Lately, energy efficiency (EE) in mobile communications is receiving growing attention as its increasing energy consumption raises concern over climate effects. As a stepping stone to improve the situation, we provide some analytical tools for optimising the EE of a base station through power control, focusing on elastic traffic in the downlink scenario. Under certain assumptions, the problem is formulated such that any rate maximisation algorithm can be incorporated to achieve the optimum EE. Furthermore, when formulated as a function of one sum power variable, optimality is illustrated graphically and Pareto optimality is discussed. Finally, an EE function for a base station in a cellular network is proposed that captures essential factors of power consumption.

Energy Efficiency in Random Opportunistic Beamforming

As a step towards decreasing power consumption in mobile communication networks, we investigate the energy efficiency (EE) of a scheduler that can be utilized at the transmitter in a broadcast channel: the random opportunistic beamforming algorithm. Using the ratio of rate to power as the EE metric and considering the constant circuit power, the EE at the transmitter decreases with the number of transmit antennas

Energy-efficient power allocation for amplify-and-forward MIMO relay channel

M

Energy-efficient power control for MIMO channels with partial and full CSI

. Two approaches of optimizing the power allocation when

Green resource allocation in relay-assisted multicarrier IC networks considering circuit dissipated power

M=1

Pricing in noncooperative interference channels for improved energy-efficiency

are introduced. We show that the optimal EE scales double-logarithmically with the number of users, which has one antenna each. The appropriate expression for EE in communications is also discussed.

Energy-efficient modulation selection and feedback quantization

We consider a two-hop amplify-and-forward (AF) MIMO relay channel consisting one source, one AF relay and one destination, where each node is equipped with multi-antenna. For this scenario, most effort was made to maximize the rate or to minimize the sum power, while these schemes may not be energy-efficient when the ratio of rate to the sum power is used as the cost function. This motivates us to study a more meaningful and challenging problem: energy efficiency (EE) maximization under a rate requirement and individual node transmit power constraints. This work focuses on linear transceiver design: joint optimization of the source and the relay strategies when the minimum-mean-square-error (MMSE) receiver is employed. In order to tackle this non-convex optimization problem, the alternating optimization algorithm combined with fractional programming is applied to optimize each variable separately. In addition, we derive that under some conditions, the conventional schemes of rate maximization and sum-power minimization are also energy-efficient.

Energy-efficient non-cooperative power control in relay-assisted interference channels considering circuit dissipated power

In a multiple-input-multiple-output (MIMO) down-link channel, the impact of line-of-sight, out-of-cell interferers and of antenna correlation on the energy efficiency (EE) of the communication is discussed. With reference to a power budget model reflecting the power expenditure of a base station (BS) together with the power employed for training, we provide analytical insights for power control policies either designed in the presence of full channel state information (CSI) availability at the transmitter or under the assumption that power allocation does not adapt to channel state dynamically.