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Novel Codebook Design for Channel State Information Quantization in MIMO Rician Fading Channels With Limited Feedback | IEEE Journals & Magazine | IEEE Xplore

Novel Codebook Design for Channel State Information Quantization in MIMO Rician Fading Channels With Limited Feedback


Abstract:

When a channel consists of a line-of-sight (LoS) path as well as non-LoS components, codebook design for channel state information (CSI) quantization is required to take ...Show More

Abstract:

When a channel consists of a line-of-sight (LoS) path as well as non-LoS components, codebook design for channel state information (CSI) quantization is required to take account of both of them. However, the conventional codebook design requires infinitely many optimal codebooks corresponding to all possible Rician factors, which is impossible in practice. In this regard, we propose an effective codebook adaptive to any Rician factors, while guaranteeing comparable performance to the optimal codebook. Contrary to the conventional approaches, the adaptation to Rician factors suffices by sharing only a single common codebook between the transmitter and receiver. We first investigate the distribution of the angle between the channel vector and the LoS component, where the distribution depends on Rician factors that reflect the power ratio of LoS and non-LoS components. Driven by the analysis, we devise a band-structured non-homogeneous codebook and derive the upper bound of the quantization error of the proposed codebook. The design parameters of the proposed codebook are optimized to minimize the quantization error bound. Using an approximation, we also derive a tractable near-optimal solution of the parameters determining the proposed codebook. Numerical results exhibit that the proposed codebook substantially outperforms conventional methods and achieves near-optimal performance in terms of the average quantization distortion and average sum rate.
Published in: IEEE Transactions on Signal Processing ( Volume: 69)
Page(s): 2858 - 2872
Date of Publication: 07 May 2021

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Citations are not available for this document.

I. Introduction

To Fully exploit the advantages of multiple-input multiple-output (MIMO) communications, the accurate knowledge of channel state information at transmitter (CSIT) is crucial. In time-division duplex (TDD) systems, CSIT is obtained by uplink channel estimation based on the uplink and downlink channel reciprocity. On the other hand, in frequency division duplexing (FDD) systems, uplink and downlink channels are usually independent of each other [1]–[4] and thus feedback on CSI from receivers is required. A popular feedback technique is codebook based approach where an index of a quantized CSI in a pre-designed codebook is fed back to a transmitter. In the Long Term Evolution (LTE) of the Third Generation Partnership Project (3GPP) standard, each receiver reports rank indicator (RI) which corresponds to the number of independent data streams, precoding matrix indicator (PMI) from the pre-designed codebook, and channel quality indicator (CQI) which represents the channel quality correponding to the modulation and coding scheme [5], [6]. On the other hand, explicit feedback schemes which directly reports a quantized CSI allow more flexibility of transmission and the reception methods and achieve a higher scheduling gain but they require larger overhead than implicit feedback [2], [6]. With the codebook based feedback, the accuracy of CSI depends on the codebook structure and the amount of allowed feedback bits. On this account, one of the main research thrusts of MIMO communications has been on efficient codebook design with limited feedback for various channels.

Cites in Papers - |

Cites in Papers - IEEE (9)

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1.
Huayan Guo, Vincent K. N. Lau, "Bayesian Hierarchical Sparse Autoencoder for Massive MIMO CSI Feedback", IEEE Transactions on Signal Processing, vol.72, pp.3213-3227, 2024.
2.
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3.
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4.
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5.
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6.
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7.
Zhiyao Tang, Liang Sun, Xinyu Tian, Dusit Niyato, Yang Zhang, "Artificial-Noise-Aided Coordinated Secure Transmission Design in Multi-Cell Multi-Antenna Networks With Limited Feedback", IEEE Transactions on Vehicular Technology, vol.71, no.2, pp.1750-1765, 2022.
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Jinho Kang, Gyeongrae Im, Sooyeob Jung, Joon Gyu Ryu, Woo Jin Byun, "Partial CSI based Regularized Zero-Forcing Precoder for Multibeam Satellite Communications toward 6G Networks", 2021 International Conference on Information and Communication Technology Convergence (ICTC), pp.1579-1581, 2021.
9.
Jinho Kang, Wan Choi, "Multi-Stage Precoder Design for Cooperative Massive MIMO Networks with Limited Feedback", 2021 International Conference on Information and Communication Technology Convergence (ICTC), pp.1588-1593, 2021.

Cites in Papers - Other Publishers (7)

1.
Jinho Kang, "Joint Design of Altitude and Channel Statistics Based Energy Beamforming for UAV-Enabled Wireless Energy Transfer", Drones, vol.8, no.11, pp.668, 2024.
2.
Jinho Kang, "Joint Design of Transmit Waveform and Altitude for Unmanned Aerial Vehicle-Enabled Integrated Sensing and Wireless Power Transfer Systems", Electronics, vol.13, no.21, pp.4237, 2024.
3.
Fei Pan, Xiaoyu Zhao, Boda Zhang, Pengjun Xiang, Mengdie Hu, Xuesong Gao, "CSI Feedback Model Based on Multi-Source Characterization in FDD Systems", Sensors, vol.23, no.19, pp.8139, 2023.
4.
Davi da Silva Brilhante, Joanna Carolina Manjarres, Rodrigo Moreira, Lucas de Oliveira Veiga, Jose F. de Rezende, Francisco Muller, Aldebaro Klautau, Luciano Leonel Mendes, Felipe A. P. de Figueiredo, "A Literature Survey on AI-Aided Beamforming and Beam Management for 5G and 6G Systems", Sensors, vol.23, no.9, pp.4359, 2023.
5.
Jiajia Guo, Tong Chen, Shi Jin, Geoffrey Ye Li, Xin Wang, Xiaolin Hou, "Deep learning for joint channel estimation and feedback in massive MIMO systems", Digital Communications and Networks, 2023.
6.
Long Suo, Fei Liu, "Closed-Form Sum-Rate Analysis of Interference Alignment with Limited Feedback Based on Scalar Quantization and Random Vector Quantization", Applied Sciences, vol.12, no.12, pp.6117, 2022.
7.
Seunghui Hong, Sanguk Jo, Jaewoo So, "Machine learning-based adaptive CSI feedback interval", ICT Express, 2021.

References

References is not available for this document.