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• W2950272787 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." @default.
• W2950272787 created "2019-06-27" @default.
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• W2950272787 date "2015-05-28" @default.
• W2950272787 modified "2023-09-28" @default.
• W2950272787 title "Performance-Complexity Analysis for MAC ML-based Decoding with User Selection" @default.
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• W2950272787 doi "https://doi.org/10.48550/arxiv.1505.07725" @default.
• W2950272787 hasPublicationYear "2015" @default.
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