Pdf Performance Complexity Analysis For Mac

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[Submitted on 28 May 2015 (v1), last revised 2 Jun 2015 (this version, v2)]
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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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Hsiao-feng Lu
Petros Elia
Arun Kumar Singh
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