Joint alpha-fairness based DSM and user encoding ordering for zero-forcing nonlinear precoding in G. fast downstream transmission
Wouter Lanneer, Paschalis Tsiaflakis, Jochen Maes, Marc Moonen
Abstract
In the G.fast frequency range with strong levels of crosstalk, nonlinear precoding (NLP) is proposed as a near-optimal technique for crosstalk precompensation in downstream transmission. While existing methods for multi-tone NLP user encoding ordering (UEO) are rather heuristic in how they approach fairness and suffer from substantial suboptimality, we develop a novel algorithm for joint dynamic spectrum management (DSM) and UEO that enforces a generalized alpha-fairness policy. Since finding the optimal UEO is a combinatorial optimization problem with excessive computational complexity, the proposed algorithm uses a low-complexity iterative method which provides near-optimal approximate solutions. Simulations demonstrate that the novel algorithm achieves a trade-off between fairness and performance that outperforms current UEO methods.
BibTeX
@inproceedings{icassp2017_jointalphafairne,
title = {Joint alpha-fairness based DSM and user encoding ordering for zero-forcing nonlinear precoding in G. fast downstream transmission},
author = {Wouter Lanneer and Paschalis Tsiaflakis and Jochen Maes and Marc Moonen},
booktitle = {ICASSP 2017},
year = {2017}
}