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Structured Sparsity Learning Based Multiuser Detection in Massive-Device Multiple Access

In this work, we study the non-time-slotted massive-device multiple access (MaDMA) problem where massive user devices transmit sporadic data to a multi-antenna base station (BS). We develop a structured sparsity learning based multiuser detection (SSL-MUD) scheme. By exploiting the structured sparsi...

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Main Authors:	Tian Ding, Xiaojun Yuan, Soung Chang Liew
Format:	Conference Proceeding
Language:	English
Subjects:	Antennas Approximation algorithms Channel estimation Decoding Message passing Multiuser detection Random variables
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creator	Tian Ding Xiaojun Yuan Soung Chang Liew
description	In this work, we study the non-time-slotted massive-device multiple access (MaDMA) problem where massive user devices transmit sporadic data to a multi-antenna base station (BS). We develop a structured sparsity learning based multiuser detection (SSL-MUD) scheme. By exploiting the structured sparsity naturally embedded in user signals, our SSL-MUD scheme is able to blindly detect the user packets without any prior knowledge of the user activity state (UAS) and the channel state information (CSI), and hence significantly reduces the transmission overhead. For the blind signal detection at the BS, we put forth the turbo bilinear generalized approximate message passing (Turbo-BiG-AMP) algorithm. Simulation results demonstrate that the Turbo-BiG- AMP algorithm significantly outperforms the existing compressed sensing based approach and achieves a performance comparable to that of the oracle linear minimum mean-square error (Oracle- LMMSE) algorithm (which assumes perfect knowledge of UAS and CSI at the BS).
doi_str_mv	10.1109/GLOCOM.2018.8648017
format	conference_proceeding
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language	eng
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source	IEEE Xplore All Conference Series
subjects	Antennas Approximation algorithms Channel estimation Decoding Message passing Multiuser detection Random variables
title	Structured Sparsity Learning Based Multiuser Detection in Massive-Device Multiple Access
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