Greedy Data-Aided Active User Detection for Massive Machine Type Communications

Irtaza, Syed Ali; Lim, Sun Hong; Choi, Jun Won

doi:10.1109/lwc.2019.2912372

Cited by 12 publications

(12 citation statements)

References 10 publications

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“…where ÂΛ = diag(x 1,Λ )S Λ . Such support detection from data detection is generally referred as data-aided activity detection [24] or joint activity and data detection [23,27].…”

Section: Background and Motivationmentioning

confidence: 99%

“…We assume that the preamble and data symbols are transmitted within a channel coherence time and hence, the channel estimated from the preamble transmission enables decoding the data symbols transmitted in the data transmission part of the GF slot. Furthermore, DRAFT March 23, 2021 we present the system model in such a way that the conventional single-sequence spreading random access (SSRA)-based schemes [9][10][11][12][13][14][15][16], [23,24] will be a special case of the proposed scheme.…”

Section: Background and Motivationmentioning

confidence: 99%

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Multi-sequence Spreading Random Access (MSRA) for Compressive Sensing-based Grant-free Communication

Abebe¹,

Kang²

2021

Preprint

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The performance of grant-free random access (GF-RA) is limited by the number of accessible random access resources (RRs) due to the absence of collision resolution. Compressive sensing (CS)based RA schemes scale up the RRs at the expense of increased non-orthogonality among transmitted signals. This paper presents the design of multi-sequence spreading random access (MSRA) which employs multiple spreading sequences to spread the different symbols of a user as opposed to the conventional schemes in which a user employs the same spreading sequence for each symbol. We show that MSRA provides code diversity, enabling the multi-user detection (MUD) to be modeled into a wellconditioned multiple measurement vector (MMV) CS problem. The code diversity is quantified by the decrease in the average Babel mutual coherence among the spreading sequences. Moreover, we present a two-stage active user detection (AUD) scheme for both wideband and narrowband implementation.Our theoretical analysis shows that with MSRA activity misdetection falls exponentially while the size of GF-RA frame is increased. Finally, the simulation results show that about 82% increase in utilization of RRs, i.e., more active users, is supported by MSRA than the conventional schemes while achieving the RA failure rate lower bound set by random access collision.

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“…where ÂΛ = diag(x 1,Λ )S Λ . Such support detection from data detection is generally referred as data-aided activity detection [24] or joint activity and data detection [23,27].…”

Section: Background and Motivationmentioning

confidence: 99%

Section: Background and Motivationmentioning

confidence: 99%

Multi-sequence Spreading Random Access (MSRA) for Compressive Sensing-based Grant-free Communication

Abebe¹,

Kang²

2021

Preprint

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show abstract

“…However the CSI has to be estimated before data detection in many practical scenarios. As a further development, various joint user activation, data detection and channel estimation (CE) schemes have been proposed in [23]- [26] where CE phase and data transmission phase share same user activation to enhance system performance.…”

Section: Introductionmentioning

confidence: 99%

Improved Compressed Sensing-Based Joint User and Symbol Detection for Media-Based Modulation-Enabled Massive Machine- Type Communications

Guo

Yuan

2020

IEEE Access

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Media-Based Modulation (MBM) is regarded as a promising technique for future massive machine-type communications (mMTC) due to its high energy/spectral efficiency, good error performance and low-complexity radio frequency hardware implementation. In this paper, we consider both sparsity nature of user activity and sparsity nature of MBM signals in the uplink MBM-enabled mMTC system. According to the static user activation or the dynamic user activation in a coherent time, we classify the transmission schemes into two types and propose corresponding improved compressive sensing (CS)-based joint user identification and data detection with/without prior information of channel state information (CSI). The simulation results demonstrate the performance advantages of our proposed algorithms over the state-of-the-art CS-based user detection methods or CS-based symbol detection methods and evaluate the performance with different system parameters. INDEX TERMS Massive machine-type communications (mMTC), media-based modulation (MBM), compressive sensing.

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“…In this paper, we focus on the contention-free approaches, which use non-orthogonal preambles (NOPs) to enable preamble overload and support contention-free transmission [12][13][14][15][16][17][18]. Exploiting the sporadic nature of device activity in mMTC, compressive sensing (CS)-based sparsity reconstruction algorithms can be adopted to develop efficient AUD and CE algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…Wei et al [17] proposed an expectation propagation (EP) algorithm for the joint CE and data decoding of grant-free SCMA. Irtaza et al [18] proposed an enhanced greedy OMP algorithm for joint AUD, CE, and data decoding. Although a variety of efficient CS-based AUD algorithms have been proposed, most of the aforementioned work only validates the algorithms by simulations.…”

Section: Introductionmentioning

confidence: 99%

Modeling, Analysis, and Optimization of Grant-Free NOMA in Massive MTC via Stochastic Geometry

Liu

Zhang

et al. 2020

Preprint

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Massive machine-type communications (mMTC) is a crucial scenario to support booming Internet of Things (IoTs) applications. In mMTC, although a large number of devices are registered to an access point (AP), very few of them are active with uplink short packet transmission at the same time, which requires novel design of protocols and receivers to enable efficient data transmission and accurate multi-user detection (MUD). Aiming at this problem, grant-free non-orthogonal multiple access (GF-NOMA) protocol is proposed. In GF-NOMA, active devices can directly transmit their preambles and data symbols altogether within one time frame, without grant from the AP. Compressive sensing (CS)-based receivers are adopted for non-orthogonal preambles (NOP)-based MUD, and successive interference cancellation is exploited to decode the superimposed data signals. In this paper, we model, analyze, and optimize the CS-based GF-MONA mMTC system via stochastic geometry (SG), from an aspect of network deployment. Based on the SG network model, we first analyze the success probability as well as the channel estimation error of the CS-based MUD in the preamble phase and then analyze the average aggregate data rate in the data phase. As IoT applications highly demands low energy consumption, low infrastructure cost, and flexible deployment, we optimize the energy efficiency and AP coverage efficiency of GF-NOMA via numerical methods. The validity of our analysis is verified via Monte Carlo simulations. Simulation results also show that CS-based GF-NOMA with NOP yields better MUD and data rate performances than contention-based GF-NOMA with orthogonal preambles and CS-based grant-free orthogonal multiple access.

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Greedy Data-Aided Active User Detection for Massive Machine Type Communications

Cited by 12 publications

References 10 publications

Multi-sequence Spreading Random Access (MSRA) for Compressive Sensing-based Grant-free Communication

Multi-sequence Spreading Random Access (MSRA) for Compressive Sensing-based Grant-free Communication

Improved Compressed Sensing-Based Joint User and Symbol Detection for Media-Based Modulation-Enabled Massive Machine- Type Communications

Modeling, Analysis, and Optimization of Grant-Free NOMA in Massive MTC via Stochastic Geometry

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