Performance-Complexity Analysis for MAC ML-based Decoding with User Selection

TL;DR

This paper analyzes the limits of computational complexity for ML-based decoding in multiuser MAC channels, showing how feedback and user selection can optimize performance and reduce decoding complexity.

Contribution

It introduces bounds on decoding complexity for near-optimal and suboptimal performance in MAC channels, highlighting feedback's role in complexity reduction.

Findings

Proper user selection enables near-optimal decoding with manageable complexity.

Feedback can improve performance and reduce decoding complexity.

Bounds on complexity-performance tradeoffs are established for various decoding scenarios.

Abstract

This work explores the rate-reliability-complexity limits of the quasi-static K-user multiple access channel (MAC), with or without feedback. Using high-SNR asymptotics, the work first derives bounds on the computational resources required to achieve near-optimal (ML-based) decoding performance. It then bounds the (reduced) complexity needed to achieve any (including suboptimal) diversity-multiplexing performance tradeoff (DMT) performance, and finally bounds the same complexity, in the presence of feedback-aided user selection. This latter effort reveals the ability of a few bits of feedback not only to improve performance, but also to reduce complexity. In this context, the analysis reveals the interesting finding that proper calibration of user selection can allow for near-optimal ML-based decoding, with complexity that need not scale exponentially in the total number of codeword bits. The derived bounds constitute the best known performance-vs-complexity behavior to date for ML-based MAC decoding, as well as a first exploration of the complexity-feedback-performance interdependencies in multiuser settings.