Averaged Iterative Water-Filling Algorithm: Robustness and Convergence

TL;DR

This paper introduces a robust iterative water-filling algorithm that guarantees convergence despite time-varying interference estimation errors, outperforming traditional methods especially in high interference scenarios.

Contribution

It proposes a new algorithm with proven convergence under estimation errors, addressing a key limitation of existing iterative water-filling algorithms.

Findings

The proposed algorithm converges with time-varying errors.

Traditional IWF diverges under strong interference.

Simulation confirms robustness in practical scenarios.

Abstract

The convergence properties of the Iterative water-filling (IWF) based algorithms have been derived in the ideal situation where the transmitters in the network are able to obtain the exact value of the interference plus noise (IPN) experienced at the corresponding receivers in each iteration of the algorithm. However, these algorithms are not robust because they diverge when there is it time-varying estimation error of the IPN, a situation that arises in real communication system. In this correspondence, we propose an algorithm that possesses convergence guarantees in the presence of various forms of such time-varying error. Moreover, we also show by simulation that in scenarios where the interference is strong, the conventional IWF diverges while our proposed algorithm still converges.