MAP-Based Pilot State Detection in Grant-Free Random Access for mMTC

Jiang, Dongdong; Cui, Ying

doi:10.1109/spawc48557.2020.9154246

Cited by 8 publications

(15 citation statements)

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“…On physical layer design, researchers usually focus on collision resolution. In [ 16 ], the multivariate Bernoulli model which exhibited the distributions of pilot states was proposed. With this, general correlation among device activities and relationship among the states of pilots assigned to one device could be specified.…”

Section: Related Workmentioning

confidence: 99%

“…Moreover, an RA scheme was proposed in [ 20 ], which introduced virtual preambles and associated them with RA channel indexes to discern multiple low-cost IoT devices. Works [ 16 , 17 , 18 , 19 , 20 ] all devoted to collision probability reduction. However, there was little attention on preamble collision in the physical layer of IoT.…”

Section: Related Workmentioning

confidence: 99%

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Preamble Design and Collision Resolution in a Massive Access IoT System

Zhong

Wang

et al. 2021

Sensors

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How to support massive access efficiently is one of the challenges in the future Internet of Things (IoT) systems. To address such challenge, this paper proposes an effective preamble collision resolution scheme to sustain massive random access (RA) for an IoT system. Specifically, a new sub-preamble structure is first proposed to reduce the preamble collision probability. To identify different devices that send the same preamble to the gNB on the same physical random access channel (PRACH), a multiple timing advance (TA) capturing scheme is then proposed. Thereafter, an RA scheme is designed to sustain massive access and the performance of the scheme is studied analytically. Finally, the effectiveness of the proposed RA scheme is validated by extensive simulation experiments in terms of preamble detection probability, preamble collision probability, RA success probability, resource efficiency and TA capturing.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Preamble Design and Collision Resolution in a Massive Access IoT System

Zhong

Wang

et al. 2021

Sensors

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“…Besides, classic statistical estimation methods, such as maximum likelihood (ML) estimation [12]- [15] and maximum a posteriori probability (MAP) estimation [16], [17], are applied for device activity detection. It is noteworthy that device activity detection is a more fundamental path problem as channel conditions of the detected active devices can be subsequently estimated using conventional channel estimation methods.…”

Section: Introductionmentioning

confidence: 99%

“…In [13], the authors extend the ML estimation-based device activity detection method in [12] to joint activity and data detection in a single-cell network. On the other hand, in [16], the authors formulate device activity detection with a general prior activity distribution in a single-cell network as an MAP estimation problem and extend the coordinate descent-based algorithm in [12] to obtain a stationary point of the MAP estimation problem. The proposed MAP estimation-based method in [16] can also be applied for joint activity and data detection in a single-cell network.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, in [16], the authors formulate device activity detection with a general prior activity distribution in a single-cell network as an MAP estimation problem and extend the coordinate descent-based algorithm in [12] to obtain a stationary point of the MAP estimation problem. The proposed MAP estimation-based method in [16] can also be applied for joint activity and data detection in a single-cell network. Later, in [14], [15], [17], the authors investigate non-cooperative [17] and cooperative [14], [15], [17] device activity detection in multi-cell networks.…”

Section: Introductionmentioning

confidence: 99%

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Statistical Device Activity Detection for OFDM-based Massive Grant-Free Access

Jia¹,

Cui²,

Jiang³

2021

Preprint

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Existing works on grant-free access, proposed to support massive machine-type communication (mMTC) for the Internet of things (IoT), mainly concentrate on narrow band systems under flat fading. However, little is known about massive grant-free access for wideband systems under frequency-selective fading. This paper investigates massive grant-free access in a wideband system under frequency-selective fading. First, we present an orthogonal frequency division multiplexing (OFDM)-based massive grantfree access scheme. Then, we propose two different but equivalent models for the received pilot signal, which are essential for designing various device activity detection and channel estimation methods for OFDM-based massive grant-free access. One directly models the received signal for actual devices, whereas the other can be interpreted as a signal model for virtual devices. Next, we investigate statistical device activity detection under frequency-selective Rayleigh fading based on the two signal models.We first model device activities as unknown deterministic quantities and propose three maximum likelihood (ML) estimation-based device activity detection methods with different detection accuracies and computation times. We also model device activities as random variables with a known joint distribution and propose three maximum a posterior probability (MAP) estimation-based device activity methods, which further enhance the accuracies of the corresponding ML estimation-based methods.Optimization techniques and matrix analysis are applied in designing and analyzing these methods.Finally, numerical results show that the proposed statistical device activity detection methods outperform existing state-of-the-art device activity detection methods under frequency-selective Rayleigh fading.

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