Deep Learning-Based Improved Cascaded Channel Estimation and Signal Detection for Reconfigurable Intelligent Surfaces-Assisted MU-MISO Systems

Rahman, Md Habibur; Sejan, Mohammad Abrar Shakil; Aziz, Md Abdul; Baik, Jung-In; Kim, Dong–Sun; Song, Hyoung‐Kyu

doi:10.1109/tgcn.2023.3237132

Cited by 18 publications

(6 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. , KG r G t , where (34c) is due to p(y, h|r; α(ℓ) ) ≈ q (ℓ) (y, h) = q (ℓ) (y)q (ℓ) (h); [µ h for the variational posterior q (ℓ) (h), which are calculated via (31) with α in A = Diag(α) replaced by α(ℓ) . Therefore, the n-th element of the new estimate α(ℓ+1) in ( 25) is approximately updated as…”

Section: A Variational Em Approach To (25b)mentioning

confidence: 99%

See 1 more Smart Citation

Spatial Channel Covariance Estimation and Two-Timescale Beamforming for IRS-Assisted Millimeter Wave Systems

Wang

Fang

Huang

et al. 2023

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

Recently, intelligent reflecting surface (IRS)-assisted communication has gained considerable attention due to its advantage in extending the coverage and compensating the path loss with low-cost passive metasurface. This paper considers the uplink channel estimation for IRS-aided multiuser massive multiinput single-output (MISO) communications with one-bit ADCs at the base station (BS). The use of one-bit ADC is impelled by the low-cost and power efficient implementation of massive antennas techniques. However, the passiveness of IRS and the lack of signal level information after one-bit quantization make the IRS channel estimation challenging. To tackle this problem, we exploit the structured sparsity of the user-IRS-BS cascaded channels and develop three channel estimators, each of which utilizes the structured sparsity at different levels. Specifically, the first estimator exploits the elementwise sparsity of the cascaded channel and employs the sparse Bayesian learning (SBL) to infer the channel responses via the type-II maximum likelihood (ML) estimation. However, due to the one-bit quantization, the type-II ML in general is intractable. As such, a variational expectation-maximization (EM) algorithm is custom-derived to iteratively compute an ML solution. The second estimator utilizes the common row-structured sparsity induced by the IRS-to-BS channel shared among the users, and develops another type-II ML solution via the block SBL (BSBL) and the variational EM. To further improve the performance of BSBL, a third two-stage estimator is proposed, which can utilize both the common rowstructured sparsity and the column-structured sparsity arising from the limited scattering around the users. Simulation results show that the more diverse structured sparsity is exploited, the better estimation performance is achieved, and that the proposed estimators are superior to state-of-the-art one-bit estimators.

show abstract

Section: A Variational Em Approach To (25b)mentioning

confidence: 99%

“…In [24]- [28], the authors proposed to estimate the cascaded channel from a sparse signal recovery perspective by exploiting the channel sparsity in the angular domain for millimeter wave (mmWave) communications. More recently, the deep learningbased channel estimation approach has also gained much attentions [29]- [31].…”

Section: Introductionmentioning

confidence: 99%

Spatial Channel Covariance Estimation and Two-Timescale Beamforming for IRS-Assisted Millimeter Wave Systems

Wang

Fang

Huang

et al. 2023

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

show abstract

“…The authors used the vehicular metaverse as a case study of UOC to examine semantic communications and semantic information transfer for IRS-aided systems. Moreover, IRS-assisted wireless communication systems based on deep machine learning [16,20] could be a possible applicable emerging field in metaverse scenarios.…”

Section: Conceptual Motivationmentioning

confidence: 99%

Spectral Efficiency Analysis for IRS-Assisted MISO Wireless Communication: A Metaverse Scenario Proposal

et al. 2023

Self Cite

View full text Add to dashboard Cite

The metaverse is emerging as a next-generation internet paradigm that will enhance human interaction and connectivity. Digital twinning, a fundamental strategy used in the metaverse, allows for the virtualization of real-world items, people, actions, and settings. A virtual world called the metaverse is built on a variety of technologies. Wireless communication is an important part of these technologies. In particular, wireless 6G communication can be essential for the growth of the metaverse. In line with the goal of achieving higher rates in the next-generation wireless network for the metaverse, in this paper, a novel conceptualization of intelligent reflecting surface (IRS)-assisted multiple-input single output-based wireless communication in physical world environments is proposed. More specifically, this paper proposes that in the physical world, the IRS-assisted communication between a communication network and users can be reflected in the metaverse through the virtual world (such as digital avatars and the virtual environment). In the simulations, the bit-error rate and spectral efficiency of the receiver terminal were performed and calculated in the metaverse engine for future consideration.

show abstract

“…In recent years, machine learning (ML) has been extensively used in the area of IRS-assisted wireless communication [ 13 , 14 , 15 , 16 , 17 ]. In IRS-assisted communication, channel state information (CSI) acquisition is a big challenge and can be easily overcome by using deep learning (DL)-based models.…”

Section: Introductionmentioning

confidence: 99%

“…In summary, Bi-LSTM can be an important candidate for IRS-based SE improvement as it can extract more features from data in two directions. The aforementioned application and benefits of Bi-LSTM in the wireless communication system are important specially for IRS-based communication [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Spectral Efficiency Improvement Using Bi-Deep Learning Model for IRS-Assisted MU-MISO Communication System

Aziz,

Rahman,

Sejan

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

The intelligent reflecting surface (IRS) is a two-dimensional (2D) surface with a programmable structure and is composed of many arrays. The arrays are used to supervise electromagnetic wave propagation by altering the electric and magnetic properties of the 2D surface. IRS can influentially convert wireless channels to very effectively enhance spectral efficiency (SE) and communication performance in wireless systems. However, proper channel information is necessary to realize the IRS anticipated gains. The conventional technique has been taken into consideration in recent attempts to fix this issue, which is straightforward but not ideal. A deep learning model which is called the long short-term memory (Bi-LSTM) model can tackle this issue due to its good learning capability and it plays a vital role in enhancing SE. Bi-LSTM can collect data from both forward and backward directions simultaneously to provide improved prediction accuracy. Because of the tremendous benefits of the Bi-LSTM model, in this paper, an IRS-assisted Bi-LSTM model-based multi-user multiple input single output downlink system is proposed for SE improvement. A Wiener filter is used to determine the optimal phase of each IRS element. In the simulation results, the proposed system is compared with other DL models and methods for the SE performance evaluation. The model exhibits satisfactory SE performance with a different signal-to-noise ratio compared to other schemes in the online phase.

show abstract

Deep Learning-Based Improved Cascaded Channel Estimation and Signal Detection for Reconfigurable Intelligent Surfaces-Assisted MU-MISO Systems

Cited by 18 publications

References 43 publications

Spatial Channel Covariance Estimation and Two-Timescale Beamforming for IRS-Assisted Millimeter Wave Systems

Spatial Channel Covariance Estimation and Two-Timescale Beamforming for IRS-Assisted Millimeter Wave Systems

Spectral Efficiency Analysis for IRS-Assisted MISO Wireless Communication: A Metaverse Scenario Proposal

Spectral Efficiency Improvement Using Bi-Deep Learning Model for IRS-Assisted MU-MISO Communication System

Contact Info

Product

Resources

About