A Low-Complexity Joint User Activity, Channel and Data Estimation for Grant-Free Massive MIMO Systems

Zou, Qiuyun; Zhang, Haochuan; Cai, Donghong; Yang, Hongwen

doi:10.1109/lsp.2020.3008550

Cited by 35 publications

(23 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, the Reed-Muller can be utilized for the design of AUD and CE. channel and data estimation in [14]. The obtained results show that the proposed algorithm has better estimation performance with less pilots.…”

Section: Grant-free Non-orthogonal Multiple Accessmentioning

confidence: 89%

Sparse Code Multiple Access for 6G Wireless Communication Networks: Recent Advances and Future Directions

Liu

Wen

et al. 2021

IEEE Comm. Stand. Mag.

Self Cite

View full text Add to dashboard Cite

show abstract

Section: Grant-free Non-orthogonal Multiple Accessmentioning

confidence: 89%

Sparse Code Multiple Access for 6G Wireless Communication Networks: Recent Advances and Future Directions

Liu

Wen

et al. 2021

IEEE Comm. Stand. Mag.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Finally, this work provides a basis for joint detection, channel estimation and data recovery in the presence of correlated sensor activity. Combining our approach with the work in [12] appears to be a promising avenue of future work.…”

Section: Discussionmentioning

confidence: 99%

“…While the correlation between the symbols is considered, the activity of the users is assumed to be statistically independent. In [12]- [14], a systematic approach for the joint identification and channel estimation problem exploited a general framework, known as the group-sparse model, where sensor activity-common to all subcarriers-is treated as a latent variable. A GAMP-type method, known as hybrid GAMP (HGAMP), was then applied by exploiting the group-sparse hybrid GAMP (GS-HGAMP) algorithm in [15] tailored for the group-sparse model.…”

Section: Introductionmentioning

confidence: 99%

Joint Identification and Channel Estimation for Fault Detection in Industrial IoT With Correlated Sensors

2021

View full text Add to dashboard Cite

As industrial plants increase the number of wirelessly connected sensors for fault detection, a key problem is to identify and obtain data from the sensors. Due to the large number of sensors, random access protocols exploiting non-orthogonal multiple access (NOMA) are a natural approach. In this paper, we develop new algorithms based on approximate message passing for sensor identification and channel estimation accounting for correlation in the activity probability of each sensor and observations of physical variables (e.g., temperature) by the access point. These algorithms form the basis for data decoding, while also identifying faulty machines and estimating local values of the temperature, which can lead to a reduction in the amount of data required to be transmitted. Numerical results show that for an expected activity probability of 0.35, our algorithms improve the normalized mean-square error of the channel estimate by up to 5dB and reduce the rate of sensor identification errors by a factor of four.

show abstract

“…In the large-scale IoT networks, the multiuser data detection involves two key components, the active user detection and the channel estimation. In [41], the authors jointly designed the algorithm which can detect the lowcomplexity active user and data under the assumption that the complete channel information is known [42]. To characterize the impact of incomplete channel information on the multiuser data detection, Liu et al [43] first jointly designed the active user detection and channel estimation algorithm, based on the estimated active user and channel information, using the traditional minimum mean square error (MMSE) and least squares (LS) for data detection.…”

Section: Data Detection Algorithms For Large-scale Multi Accessmentioning

confidence: 99%

A Survey on Transmission Schemes on Large‐Scale Internet of Things with Nonorthogonal Multiple Access

Zhou

Zhao

Zhu³

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

This paper performs a comprehensive survey on transmission schemes for the large-scale Internet of things (IoT) networks with nonorthogonal multiple access (NOMA). By solving the interference among users, NOMA can significantly improve the frequency reuse efficiency and support multiple users to use the same frequency resources. It is considered to be one of the most effective technologies for the next-generation wireless communication. However, there are still many challenges on the transmission schemes for the large-scale NOMA system, including the short-data packet transmission, active user detection, channel estimation, and data detection. In order to meet these challenges, this paper first reviews the short-packet transmission in the large-scale NOMA systems and then reviews the active user detection and channel estimation technologies of the considered systems.

show abstract

A Low-Complexity Joint User Activity, Channel and Data Estimation for Grant-Free Massive MIMO Systems

Cited by 35 publications

References 16 publications

Sparse Code Multiple Access for 6G Wireless Communication Networks: Recent Advances and Future Directions

Sparse Code Multiple Access for 6G Wireless Communication Networks: Recent Advances and Future Directions

Joint Identification and Channel Estimation for Fault Detection in Industrial IoT With Correlated Sensors

A Survey on Transmission Schemes on Large‐Scale Internet of Things with Nonorthogonal Multiple Access

Contact Info

Product

Resources

About