Deep Learning-Based Active User Detection for Grant-free SCMA Systems

Sivalingam, Thushan; Ali, Samad; Mahmood, Nurul Huda; Rajatheva, Nandana

doi:10.1109/pimrc50174.2021.9569538

Cited by 11 publications

(21 citation statements)

References 15 publications

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“…After that, it estimates the channel coefficients (h i ). Finally, BS decodes the J data symbols x [1] d,i , . .…”

Section: B Problem Formulationmentioning

confidence: 99%

Deep Learning-Based Blind Multiple User Detection for Grant-free SCMA and MUSA Systems

Sivalingam¹,

Ali²,

Mahmood³

et al. 2022

Preprint

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Grant-free random access and uplink non-orthogonal multiple access (NOMA) are techniques to increase the overload factor and reduce transmission latency with signaling overhead in massive machinetype communications (mMTC). Sparse code multiple access (SCMA) and Multi-user shared access (MUSA) are introduced as advanced code domain NOMA schemes. In grant-free NOMA, machine-type devices (MTD) transmit information to the base station (BS) without a grant, creating a challenging task for the BS to identify the active MTD among all potential active devices. In this paper, a novel deep neural network (DNN)-based multi-user detection (MUD) scheme for the grant-free SCMA and MUSA system in an mMTC uplink framework is proposed to jointly identify the received signal's sparsity and the active MTDs in the absence of channel state information. The proposed scheme learns the correlation between the received signal and the multi-dimensional codebook for SCMA and spreading sequences for MUSA schemes and is able to identify the active MTDs from the received signal without any prior knowledge of the device sparsity level. The application of the proposed MUD scheme is further investigated in an indoor factory setting using four different mmWave channels. Numerical results show that when the number of active MTDs in the system is large, DNN-MUD has a significantly higher probability of detection compared to existing approaches over the signal-to-noise ratio range of interest.

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“…After that, it estimates the channel coefficients (h i ). Finally, BS decodes the J data symbols x [1] d,i , . .…”

Section: B Problem Formulationmentioning

confidence: 99%

Deep Learning-Based Blind Multiple User Detection for Grant-free SCMA and MUSA Systems

Sivalingam¹,

Ali²,

Mahmood³

et al. 2022

Preprint

Self Cite

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show abstract

“…The DL-based AUD schemes have been demonstrated to successfully achieve superior performance over conventional CS-based AUD schemes [5]- [8]. The objective of these DLbased AUDs is to optimize an AUD network (AUDN), denoted as g(•), in terms of its trainable parameters.…”

Section: B Deep Learning-based Active User Detectionmentioning

confidence: 99%

“…where θ (g) is a vector of weight and bias AUDN and • 0 denotes L0-norm. Depending on whether it is preamblebased AUD [5]- [6], [8] or data-aided AUD [7], AUDN is given as g(•;…”

Section: B Deep Learning-based Active User Detectionmentioning

confidence: 99%

“…However, its performance would be severely degraded when the number of contending users and their activity rate increase [5]. To tackle these challenges, deep learning (DL)-based AUD schemes have been proposed to exploit deep neural network's (DNN) ability of approximating a nonlinear function to solve the AUD problem [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

“…Notably, a majority of existing CS-based and DL-based AUD schemes have a limitation in that they only use the preamble for their AUD process. Specifically, CS-based AUD schemes [3] and DL-based AUD schemes in [5]- [6], [8] do not consider exploiting the user data, but only utilize the user-specific preamble sequences. Notably, DL-based AUD scheme in [7] performs data-aided AUD.…”

Section: Introductionmentioning

confidence: 99%

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Data-aided Active User Detection with a User Activity Extraction Network for Grant-free SCMA Systems

Han¹,

Abebe²,

Kang³

2022

Preprint

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In grant-free sparse code multiple access system, joint optimization of contention resources for users and active user detection (AUD) at the receiver is a complex combinatorial problem. To this end, we propose a deep learning-based dataaided AUD scheme which extracts a priori user activity information via a novel user activity extraction network (UAEN). This is enabled by an end-to-end training of an autoencoder (AE), which simultaneously optimizes the contention resources, i.e., preamble sequences, each associated with one of the codebooks, and extraction of user activity information from both preamble and data transmission. Furthermore, we propose self-supervised pretraining scheme for the UAEN, which ensures the convergence of offline end-to-end training. Simulation results demonstrated that the proposed AUD scheme achieved 3 to 5dB gain at a target activity detection error rate of 10 −3 compared to the state-ofthe-art DL-based AUD schemes.

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Optical SCMA coding technique based on fully interleaved 3D codebook

Liu

Ren

et al. 2022

Optics Communications

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Deep Learning-Based Active User Detection for Grant-free SCMA Systems

Cited by 11 publications

References 15 publications

Deep Learning-Based Blind Multiple User Detection for Grant-free SCMA and MUSA Systems

Deep Learning-Based Blind Multiple User Detection for Grant-free SCMA and MUSA Systems

Data-aided Active User Detection with a User Activity Extraction Network for Grant-free SCMA Systems

Optical SCMA coding technique based on fully interleaved 3D codebook

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