Abstract

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. I.

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Abstract: This work explores the rate-reliability-complexity limits of the quasi-staticK-user multiple access channel (MAC), with or without feedback. Using high-SNRasymptotics, the work first derives bounds on the computational resourcesrequired to achieve near-optimal (ML-based) decoding performance. It thenbounds the (reduced) complexity needed to achieve any (including suboptimal)diversity-multiplexing performance tradeoff (DMT) performance, and finallybounds 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 toimprove performance, but also to reduce complexity. In this context, theanalysis reveals the interesting finding that proper calibration of userselection can allow for near-optimal ML-based decoding, with complexity thatneed not scale exponentially in the total number of codeword bits. The derivedbounds constitute the best known performance-vs-complexity behavior to date forML-based MAC decoding, as well as a first exploration of thecomplexity-feedback-performance interdependencies in multiuser settings.

Submission history

From: Hsiao-feng Lu [view email]
[v1] Thu, 28 May 2015 15:23:35 UTC (71 KB)
[v2]Tue, 2 Jun 2015 15:43:05 UTC (81 KB)
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