Massive Random Access With Sporadic Short Packets: Joint Active User Detection and Channel Estimation via Sequential Message Passing

Jiang, Jia-Cheng; Wang, Huiming

doi:10.1109/twc.2021.3060451

Cited by 29 publications

(28 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different from the sparse detection problem [3], [4], the joint AUD and CE requires both the support set and the corresponding amplitudes of the sparse vector, and can be formulated as a compressed sensing (CS) problem. Several CS-based solutions have been reported [5]- [9]. In [5], a dimension reduction method to reduce the pilot sequence length and computational complexity for joint AUD and CE has been proposed, which projects the original device state matrix onto a low-dimensional space by exploiting its sparse and low-rank structure.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Joint Active User Detection and Channel Estimation for Massive Connectivity

Jiang

Wang

Poor

2022

IEEE Trans. Signal Process.

Self Cite

View full text Add to dashboard Cite

This paper considers joint active user detection (AUD) and channel estimation (CE) for massive connectivity scenarios with sporadic traffic. The state-of-art method under a Bayesian framework to perform joint AUD and CE in such scenarios is approximate message passing (AMP). However, the existing theoretical analysis of AMP-based joint AUD and CE can only be performed with a given fixed point of the AMP state evolution function, lacking the analysis of AMP phase transition and Bayes-optimality. In this paper, we propose a novel theoretical framework to analyze the performance of the joint AUD and CE problem by adopting the replica method in the Bayes-optimal condition. Specifically, our analysis is based on a general channel model, which reduces to particular channel models in multiple typical MIMO communication scenarios. Our theoretical framework allows ones to measure the optimality and phase transition of AMP-based joint AUD and CE as well as to predict the corresponding performance metrics under our model. To reify our proposed theoretical framework, we analyze two typical scenarios from the massive random access literature, i.e., the isotropic channel scenario and the spatially correlated channel scenario. Accordingly, our performance analysis produces some novel results for both the isotropic Raleigh channel and spatially correlated channel case.

show abstract

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Joint Active User Detection and Channel Estimation for Massive Connectivity

Jiang

Wang

Poor

2022

IEEE Trans. Signal Process.

Self Cite

View full text Add to dashboard Cite

show abstract

“…for all devices. As in [7]- [9], the device activity evolution is modeled by a first-order steady Markov chain, which can be fully described by two transition probabilities p 01 = Pr(λ t+1 n = 1|λ t n = 0) and p 11 = Pr(λ t+1 n = 1|λ t n = 1). Then the active probability of each device, denoted as p a , in each frame can be derived by solving the eigenvalue problem as p a = p01 1−p11+p01 .…”

Section: A Temporal-correlated Activity Modelmentioning

confidence: 99%

“…This suggests that the device activity is correlated in the time domain. By accounting such temporal correlation, the performance of activity detection and channel estimation can be improved by formulating the problem from the dynamic CS (DCS) perspective [7]- [9]. Specifically, the work [7] proposes a sequential AMP (S-AMP) algorithm for device activity detection by using the historical knowledge.…”

Section: Introductionmentioning

confidence: 99%

“…By accounting such temporal correlation, the performance of activity detection and channel estimation can be improved by formulating the problem from the dynamic CS (DCS) perspective [7]- [9]. Specifically, the work [7] proposes a sequential AMP (S-AMP) algorithm for device activity detection by using the historical knowledge. In [8], a hybrid generalized AMP (HyGAMP) algorithm is employed to account the device activity information from both the previous frame and the next frame to improve performance.…”

Section: Introductionmentioning

confidence: 99%

“…In [8], a hybrid generalized AMP (HyGAMP) algorithm is employed to account the device activity information from both the previous frame and the next frame to improve performance. Note that both [7] and [8] focus on the scenario where the base station (BS) has single antenna. In [9], a side information aided multiple measurement vector based AMP (SI-aided MMV-AMP) algorithm is proposed for both single-antenna and multiple-antenna scenarios.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Double-Sided Information Aided Temporal-Correlated Massive Access

Zhu¹,

Tao²,

Guan³

2022

Preprint

View full text Add to dashboard Cite

This letter considers temporal-correlated massive access, where each device, once activated, is likely to transmit continuously over several consecutive frames. Motivated by that the device activity at each frame is correlated to not only its previous frame but also its next frame, we propose a doublesided information (DSI) aided joint activity detection and channel estimation algorithm based on the approximate message passing (AMP) framework. The DSI is extracted from the estimation results in a sliding window that contains the target detection frame and its previous and next frames. The proposed algorithm demonstrates superior performance over the state-of-the-art methods.

show abstract

A Channel Estimation Scheme of Short Packets in Frequency Selective Channels

Zhang

Wang

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Massive Random Access With Sporadic Short Packets: Joint Active User Detection and Channel Estimation via Sequential Message Passing

Cited by 29 publications

References 28 publications

Performance Analysis of Joint Active User Detection and Channel Estimation for Massive Connectivity

Performance Analysis of Joint Active User Detection and Channel Estimation for Massive Connectivity

Double-Sided Information Aided Temporal-Correlated Massive Access

A Channel Estimation Scheme of Short Packets in Frequency Selective Channels

Contact Info

Product

Resources

About