Optimization of Weighted Individual Energy Efficiencies in Interference Network

TL;DR

This paper introduces a fast-converging algorithm for maximizing weighted sum energy efficiency in interference networks, demonstrating near-optimal performance with significantly reduced computational effort compared to global optimization methods.

Contribution

The paper proposes a novel first-order optimal algorithm for WSEE maximization and explores global optimization via monotonic and fractional programming techniques.

Findings

The proposed algorithm converges within 10 iterations.

It often achieves the global optimal solution.

Compared to global methods, it requires far fewer iterations.

Abstract

This paper studies the maximization of the weighted sum energy efficiency (WSEE). We derive a first-order optimal algorithm applicable to a wide class of communication scenarios exhibiting very fast convergence. We also discuss how to leverage monotonic optimization and fractional programming to obtain a global optimal solution at the cost of higher computational complexity. The WSEE of interference networks is studied in detail with an application to relay-assisted multi-cell communication. This scenario is modeled as a non-regenerative multi-way relay channel and the achievable rate region is derived. We apply the proposed algorithm to this scenario and compare its performance to the global optimal algorithm. The results indicate that the proposed algorithm often achieves the global optimal solution and is close to it otherwise. Convergence is achieved within 10 iterations, while the global optimal solution may require more than $10^6$ iterations.