Channel Reconstruction with Limited Feedback in Intelligent Surface Aided Communications

Prasad, Narayan; Chowdhury, Moin Uddin; Qi, Xiao Feng

doi:10.1109/vtc2021-fall52928.2021.9625462

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Instead, [46] designs a codebookbased limited feedback protocol for RIS using learning methods. In [47], authors aim to reconstruct the channel using the signal strength feedback and exploiting the sparsity and low-rank properties. The sum rate of a multi-cell multi-RIS MIMO system with imperfect CSI is maximized using deep reinforcement learning in [48].…”

Section: Introductionmentioning

confidence: 99%

“…Particularly, the feedback methods in [44]- [46], [51] mainly try to send the normalized channel vectors to perform beamforming at the transmitter. Further, [47] determines the channel direction while the channel gains are estimated based on the distance and not precisely. The goal of the feedback method in [50] is to only send RIS phase information.…”

Section: Introductionmentioning

confidence: 99%

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Rate Loss Analysis of Reconfigurable Intelligent Surface-Aided NOMA With Limited Feedback

Almasi,

Jafarkhani

2024

IEEE Open J. Commun. Soc.

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The design of feedback channels in frequency division duplex (FDD) systems is a major challenge because of the limited available feedback bits. We consider non-orthogonal multiple access (NOMA) systems that incorporate reconfigurable intelligent surfaces (RISs). In limited feedback RIS-aided NOMA systems, the RIS-aided channel and the direct channel gains should be quantized and fed back to the transmitter. We investigate the rate loss of the overall RIS-aided NOMA systems suffering from quantization errors under Rayleigh and Rician fading channel models. We first consider random vector quantization for the overall RIS-aided channel and identical uniform quantizers for the direct channel gains. We then obtain an upper bound for the rate loss in Rayleigh fading, due to the quantization error, as a function of the number of feedback bits and the size of RIS. In addition, we determine the channel gain values for which NOMA is advantageous under full channel state information (CSI) but not with the quantized feedback. We perform a similar analysis assuming Rician models by approximating the extremely complicated probability density function (pdf) of a RIS-aided double-Rician channel. Our numerical results indicate that, for a small RIS size, the sum rate performance of the limited feedback system approaches that of the system with full CSI as the number of feedback bits increases. However, as the size of RIS enlarges, the sum rate improvement slows down. Further, by increasing the number of feedback bits and the transmit power, the probability that NOMA should be chosen over OMA increases.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rate Loss Analysis of Reconfigurable Intelligent Surface-Aided NOMA With Limited Feedback

Almasi,

Jafarkhani

2024

IEEE Open J. Commun. Soc.

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“…In multi-user systems assisted by an RIS, overhead reduction feedback schemes exploiting the single-structured sparsity of BS-RIS-MS cascaded channel and the specific triplestructured sparsity of the beamspace cascaded channel were proposed in [30] and [31], respectively, considering that different MSs share the same sparse BS-RIS channel but have their respective RIS-MS channels. Moreover, [32] proposed to feed back the signal strength of the intended receiver to reconstruct the channel at the BS rather than feeding back the CSI directly. The proposed channel reconstruction formulation was addressed by efficient proximal distance algorithms and simultaneously exploiting the low-rank property and sparse beamspace representation of the unknown effective channel.…”

Section: Introductionmentioning

confidence: 99%

Channel Customization for Limited Feedback in RIS-Assisted FDD Systems

Chen

Wen

et al. 2023

IEEE Trans. Wireless Commun.

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Reconfigurable intelligent surfaces (RISs) represent a pioneering technology to realize smart electromagnetic environments by reshaping the wireless channel. Jointly designing the transceiver and RIS relies on the channel state information (CSI), whose feedback has not been investigated in multi-RIS-assisted frequency division duplexing systems. In this study, the limited feedback of the RIS-assisted wireless channel is examined by capitalizing on the ability of the RIS in channel customization. By configuring the phase shifters of the surfaces using statistical CSI, we customize a sparse channel in rich-scattering environments, which significantly reduces the feedback overhead in designing the transceiver and RISs. Since the channel is customized in terms of singular value decomposition (SVD) with full-rank, the optimal SVD transceiver can be approached without a matrix decomposition and feeding back the complete channel parameters. The theoretical spectral efficiency (SE) loss of the proposed transceiver and RIS design is derived by considering the limited CSI quantization. To minimize the SE loss, a bit partitioning algorithm that splits the limited number of bits to quantize the CSI is developed. Extensive numerical results show that the channel customization-based transceiver with reduced CSI can achieve satisfactory performance compared with the optimal transceiver with full CSI. Given the limited number of feedback bits, the bit partitioning algorithm can minimize the SE loss by adaptively allocating bits to quantize the channel parameters.

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Reconfigurable Intelligent Surface-Aided NOMA with Limited Feedback

Almasi,

Jafarkhani

2023

ICC 2023 - IEEE International Conference on Communications

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Channel Reconstruction with Limited Feedback in Intelligent Surface Aided Communications

Cited by 4 publications

References 15 publications

Rate Loss Analysis of Reconfigurable Intelligent Surface-Aided NOMA With Limited Feedback

Rate Loss Analysis of Reconfigurable Intelligent Surface-Aided NOMA With Limited Feedback

Channel Customization for Limited Feedback in RIS-Assisted FDD Systems

Reconfigurable Intelligent Surface-Aided NOMA with Limited Feedback

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