Multiuser Detection via Compressive Sensing

Shim, Byonghyo; Song, Byung-Kwen

doi:10.1109/lcomm.2012.050112.111980

Cited by 130 publications

(70 citation statements)

References 5 publications

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“…complex Gaussian variables with zero mean and unit variance, i.e, Rayleigh fading channels are considered in this paper, and such typical channel model has been widely used in wireless communications [6] [7]. v n is the Gaussian noise on subcarrier n with zero mean and variance σ 2 .…”

Section: System Modelmentioning

confidence: 99%

“…Particularly, we consider that the transmitted symbols of active users are taken from the complex-constellation set X, while the transmitted signals from inactive users are regarded as zero. Due to the naturally existing sparsity of user activity, the transmitted symbol vector x is sparse [6]. Furthermore, in this paper, we consider a system where users are synchronized in a frame structure and are active or inactive in the entire frame [10].…”

Section: Joint User Activity and Data Detectionmentioning

confidence: 99%

“…Specifically, a blind detection of sparse code multiple access (SCMA) was proposed in [4], which could jointly realize user activity detection and channel estimation by sending pilots. A multi-user detection (MUD) method realized by switching between one of the typical compressive sensing (CS) reconstruction algorithms, i.e., orthogonal matching pursuit (OMP) algorithm [5], and one of the classical linear signal detection algorithms, i.e., linear minimum mean square error (LMMSE), was proposed in [6]. Such CS-based user activity detection could achieve substantial performance gain over conventional solutions by exploiting user activity sparsity.…”

Section: Introductionmentioning

confidence: 99%

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Joint User Activity and Data Detection Based on Structured Compressive Sensing for NOMA

et al. 2016

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Non-orthogonal multiple access (NOMA) has been regarded as one of the promising key technologies for future 5G systems. In the uplink grant-free NOMA schemes, dynamic scheduling is not required, which can significantly reduce the signaling overhead and transmission latency. However, user activity has to be detected in grant-free NOMA systems, which is challenging in practice. In this paper, by exploiting the inherent structured sparsity of user activity naturally existing in NOMA systems, we propose a low-complexity multi-user detector based on structured compressive sensing (SCS) to realize joint user activity and data detection. Particularly, we propose a structured iterative support detection (SISD) algorithm by exploiting such structured sparsity, which is able to jointly detect user activity and transmitted data in several continuous time slots. Simulation results show that the proposed scheme can achieve better performance than conventional solutions.Index Terms-5G, non-orthogonal multiple access (NOMA), multi-user detection (MUD), structured compressive sensing (SC-S).

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Section: System Modelmentioning

confidence: 99%

Section: Joint User Activity and Data Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Joint User Activity and Data Detection Based on Structured Compressive Sensing for NOMA

et al. 2016

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“…In order to perform a reliable MUD in the case of different sparsity, the authors of [13] adopted a switching MUD scheme between linear minimum mean square error (LMMSE) and orthogonal matching pursuit (OMP), which is the most famous CS reconstruction algorithm. The innovative attempt, bringing CS to MUD, prompted many researchers to develop MUD schemes based on CS.…”

Section: Introductionmentioning

confidence: 99%

Multi-User Detection for Sporadic IDMA Transmission Based on Compressed Sensing

Kang

et al. 2017

Entropy

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Abstract:The Internet of Things (IoT) is placing new demands on existing communication systems. The limited orthogonal resources do not meet the demands of massive connectivity of future IoT systems that require efficient multiple access. Interleave-division multiple access (IDMA) is a promising method of improving spectral efficiency and supporting massive connectivity for IoT networks. In a given time, not all sensors signal information to an aggregation node, but each node transmits a short frame on occasion, e.g., time-controlled or event-driven. The sporadic nature of the uplink transmission, low data rates, and massive connectivity in IoT scenarios necessitates minimal control overhead communication schemes. Therefore, sensor activity and data detection should be implemented on the receiver side. However, the current chip-by-chip (CBC) iterative multi-user detection (MUD) assumes that sensor activity is precisely known at the receiver. In this paper, we propose three schemes to solve the MUD problem in a sporadic IDMA uplink transmission system. Firstly, inspired by the observation of sensor sparsity, we incorporate compressed sensing (CS) to MUD in order to jointly perform activity and data detection. Secondly, as CS detection could provide reliable activity detection, we combine CS and CBC and propose a CS-CBC detector. In addition, a CBC-based MUD named CBC-AD is proposed to provide a comparable baseline scheme.

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“…그러나 ML 최근 공간변조 시스템에서 압축센싱기법을 이용하 여 활성화된 안테나 인덱스를 검출하는 기법이 제안 되었다 [8] . [8] [9] . [9]에서는 압축센싱 알고리 즘과 기존의 다중사용자 검출기법을 결합한 알고리즘 이 제시되었고 압축센싱기법으로는 OMP알고리즘이 고려되었다.…”

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A Compressed Sensing-Based Signal Recovery Technique for Multi-User Spatial Modulation Systems

Park

Ban

Jung

2014

The Journal of Korean Institute of Communications and Informati

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In this paper, we propose a compressed sensing-based signal recovery technique for an uplink multi-user spatial modulation (MU-SM) system. In the MU-SM system, only one antenna among   antennas of each user becomes active by nature. Thus, this characteristics is exploited for signal recovery at a base station. We modify the conventional orthogonal matching pursuit (OMP) algorithm which has been widely used for sparse signal recovery in literature for the MU-SM system, which is called MU-OMP. We also propose a parallel OMP algorithm for the MU-SM system, which is called MU-POMP. Specifically, in the proposed algorithms, antenna indices of a specific user who was selected in the previous iteration are excluded in the next iteration of the OMP algorithm. Simulation results show that the proposed algorithms outperform the conventional OMP algorithm in the MU-SM system.

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Multiuser Detection via Compressive Sensing

Cited by 130 publications

References 5 publications

Joint User Activity and Data Detection Based on Structured Compressive Sensing for NOMA

Joint User Activity and Data Detection Based on Structured Compressive Sensing for NOMA

Multi-User Detection for Sporadic IDMA Transmission Based on Compressed Sensing

A Compressed Sensing-Based Signal Recovery Technique for Multi-User Spatial Modulation Systems

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