Framework for Link-Level Energy Efficiency Optimization with Informed Transmitter

TL;DR

This paper introduces a fractional programming framework for optimizing link-level energy efficiency in communication systems, providing efficient algorithms and solutions for various models including time-varying channels and discrete modulation schemes.

Contribution

It develops a mathematical framework using fractional programming for energy efficiency optimization, with algorithms applicable to diverse system models and modulation schemes.

Findings

Efficient solutions for a broad class of EE maximization problems.

Water-filling solutions for various system models.

Reduction of EE problems to one-dimensional convex problems.

Abstract

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.