Compressed sensing channel estimation in massive MIMO

Pramanik, Ankita; Maity, Santi P.; Farheen, Zeba

doi:10.1049/iet-com.2019.0126

Cited by 6 publications

(3 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…which corresponds to the expected value of q b when it is beta distributed according to (5). However, such a machine-based model is limiting when sensors on the same machine may not all be active at the same time, with correlated activity probabilities.…”

Section: Related Work On Group Sparse Modelingmentioning

confidence: 99%

See 1 more Smart Citation

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

Section: Related Work On Group Sparse Modelingmentioning

confidence: 99%

“…Due to the nature of random access, active sensors typically form a sparse subset of all sensors. As a conse-quence, the problem of channel estimation has recently been attacked using compressed sensing, with algorithms based on least absolute shrinkage and selection operator (LASSO) and orthogonal matching pursuit [5]- [7].…”

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

show abstract

“…Concerning active research of applicability of CS in wireless communications, Choi et al 5 cover these extensive applications in their review. Relative to 5G communications, channel estimation in massive multiple‐input‐multiple‐output (MIMO) environment 6 is conducted taking channel sparsity into account and solving via CS algorithm. Moreover, Taylor precoding of correlated massive MIMO is investigated 7 considering low complexity and convergence.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Wireless fading channels performance based on Taylor expansion and compressed sensing: A comparative approach

Xifilidis

Psannis

2021

Int J Communication

View full text Add to dashboard Cite

Summary This paper addresses the performance of wireless fading channels, namely, Rayleigh, Rician, and Nakagami‐m fading channels, taking both noiseless and additive noisy cases into account. The performance metrics investigated are channel capacity, channel model variance estimation, required average description length based on Shannon entropy, and relative entropy and symbol error probability assessment. Taylor expansion is considered as an approximation for the aforementioned metrics together with the compressed sensing (CS) compressibility rule. Technical comments are provided for supporting simulation results. Mathematical interpretations are provided to support the simulation results along with feasible applications in 5G wireless systems. Conclusions and future research directions finalize the paper.

show abstract

Performance Analysis of Multi User Mimo System With Successive Hybrid Information and Energy Transfer Beamformer

Ramisetty

Chennupati

2021

Wireless Pers Commun

View full text Add to dashboard Cite

Compressed sensing channel estimation in massive MIMO

Cited by 6 publications

References 27 publications

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

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

Wireless fading channels performance based on Taylor expansion and compressed sensing: A comparative approach

Performance Analysis of Multi User Mimo System With Successive Hybrid Information and Energy Transfer Beamformer

Contact Info

Product

Resources

About