MAP-Based Active User and Data Detection for Massive Machine-Type Communications

Jeong, Byeong Kook; Shim, Byonghyo; Lee, Kwang Bok

doi:10.1109/tvt.2018.2849621

Cited by 68 publications

(60 citation statements)

References 28 publications

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“…Thus, the transmit vector consisting of data symbols of both active and inactive devices can be readily modeled as a sparse vector. By capitalizing on the sparsity of this multi-user vector, the multi-user detection (MUD) problem can be formulated as a sparse signal recovery problem [8]- [12]. This type of detection scheme, called compressed sensing based multi-user detection (CS-MUD), has been a key ingredient in the grant-free uplink NOMA schemes.…”

Section: Introductionmentioning

confidence: 99%

EP-Based Joint Active User Detection and Channel Estimation for Massive Machine-Type Communications

Ahn

Shim

Lee

2019

IEEE Trans. Commun.

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102

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Massive machine-type communication (mMTC) is a newly introduced service category in 5G wireless communication systems to support a variety of Internet-of-Things (IoT) applications. In recovering sparsely represented multi-user vectors, compressed sensing based multi-user detection (CS-MUD) can be used. CS-MUD is a feasible solution to the grant-free uplink non-orthogonal multiple access (NOMA) environments. In CS-MUD, active user detection (AUD) and channel estimation (CE) should be performed before data detection. In this paper, we propose the expectation propagation based joint AUD and CE (EP-AUD/CE) technique for mMTC networks. The expectation propagation (EP) algorithm is a Bayesian framework that approximates a computationally intractable probability distribution to an easily tractable distribution. The proposed technique finds the best approximation of the posterior distribution of the sparse channel vector. Using the approximate distribution, AUD and CE are jointly performed.We show by numerical simulations that the proposed technique substantially enhances AUD and CE performances over competing algorithms.

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Section: Introductionmentioning

confidence: 99%

EP-Based Joint Active User Detection and Channel Estimation for Massive Machine-Type Communications

Ahn

Shim

Lee

2019

IEEE Trans. Commun.

Self Cite

102

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“…Hence, it makes sense to exploit the originally harmful co-channel interference to enhance the secrecy performance according to the characteristics of massive access in the cellular IoT network. Commonly, B5G cellular IoT network is suggested to adopt the grant-free random access scheme for avoiding high overhead [29][30][31]. To be specific, the IoT devices can access B5G cellular network without a grant to transmit or a prior scheduling assignment.…”

Section: Introductionmentioning

confidence: 99%

Physical layer security for massive access in cellular Internet of Things

Chen

Zhong

et al. 2020

Sci. China Inf. Sci.

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The upcoming fifth generation (5G) cellular network is required to provide seamless access for a massive number of Internet of Things (IoT) devices over the limited radio spectrum. In the context of massive spectrum sharing among heterogeneous IoT devices, wireless security becomes a critical issue owing to the broadcast nature of wireless channels. According to the characteristics of the cellular IoT network, physical layer security (PHY-security) is a feasible and effective way of realizing secure massive access. This article reviews the security issues in the cellular IoT network with an emphasis on revealing the corresponding challenges and opportunities for the design of secure massive access. Furthermore, we provide a survey on PHY-security techniques to improve the secrecy performance. Especially, we propose a secure massive access framework for the cellular IoT network by exploiting the inherent co-channel interferences. Finally, we discuss several potential research directions to further enhance the security of massive IoT.

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“…In this case, how to optimize JADD is a challenging issue. By exploiting the inherent sparsity of sporadic traffic in mMTC, the JADD can be formulated as a compressive sensing (CS) problem and solved by typical sparse signal recovery algorithms [5]- [10]. The prior work [4] has demonstrated that the CS-based multi-user detector can outperform the traditional linear minimum mean square error (LMMSE) detector when the level of user activity is low.…”

Section: Introductionmentioning

confidence: 99%

“…The joint-expectation maximization-approximate message passing (joint-EM-AMP) algorithm in [9] can effectively reduce the computational complexity and learn the activity parameters by EM algorithm, but the noise variance was required to be known. Similarly, the maximum a posteriori probability (MAP)-based iteration detection scheme presented in [10] also required the noise variance and user activity probability as prior knowledge.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sensing Based Grant-Free Random Access for Massive MTC

Mei

Gao

et al. 2020

2020 International Conference on UK-China Emerging Technologies (UCET)

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Massive machine-type communications (mMTC) are expected to be one of the most primary scenarios in the next-generation wireless communications and provide massive connectivity for Internet of Things (IoT). To meet the demanding technical requirements for mMTC, random access scheme with efficient joint activity and data detection (JADD) is vital. In this paper, we propose a compressive sensing (CS)-based grant-free random access scheme for mMTC, where JADD is formulated as a multiple measurement vectors (MMV) CS problem. By leveraging the prior knowledge of the discrete constellation symbols, we develop an orthogonal approximate message passing (OAMP)-MMV algorithm for JADD, where the structured sparsity is fully exploited for enhanced performance. Moreover, expectation maximization (EM) algorithm is employed to learn the unknown sparsity ratio of the a priori distribution and the noise variance. Simulation results show that the proposed scheme achieves superior performance over other state-of-the-art CS schemes.

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MAP-Based Active User and Data Detection for Massive Machine-Type Communications

Cited by 68 publications

References 28 publications

EP-Based Joint Active User Detection and Channel Estimation for Massive Machine-Type Communications

EP-Based Joint Active User Detection and Channel Estimation for Massive Machine-Type Communications

Physical layer security for massive access in cellular Internet of Things

Compressive Sensing Based Grant-Free Random Access for Massive MTC

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