A Framework for Weighted-Sum Energy Efficiency Maximization in Wireless Networks

TL;DR

This paper introduces a systematic framework for maximizing weighted-sum energy efficiency in heterogeneous wireless networks, effectively handling power and data rate constraints with a guaranteed convergent algorithm.

Contribution

It presents a novel sequential convex optimization approach for WSEE maximization, including extensions to multi-resource systems and non-convex power models.

Findings

Fast convergence and low complexity of the proposed algorithm

Robustness to initial feasible points

Effective handling of power and data rate constraints

Abstract

Weighted-sum energy efficiency (WSEE) is a key performance metric in heterogeneous networks, where the nodes may have different energy efficiency (EE) requirements. Nevertheless, WSEE maximization is a challenging problem due to its nonconvex sum-of-ratios form. Unlike previous work, this paper presents a systematic approach to WSEE maximization under not only power constraints, but also data rate constraints, using a general SINR expression. In particular, the original problem is transformed into an equivalent form, and then a sequential convex optimization (SCO) algorithm is proposed. This algorithm is theoretically guaranteed to converge for any initial feasible point, and, under suitable constraint qualifications, achieves a Karush-Kuhn-Tucker (KKT) solution. Furthermore, we provide remarkable extensions to the proposed methodology, including systems with multiple resource blocks as well as a more general power consumption model which is not necessarily a convex function of the transmit powers. Finally, numerical analysis reveals that the proposed algorithm exhibits fast convergence, low complexity, and robustness (insensitivity to initial points).