PCQNet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems

Multi-user multiple-input multiple-output (MU-MIMO) technology can significantly improve the spectral and energy efficiencies of wireless networks. In the uplink MU-MIMO systems, the optimal precoder design at the base station utilizes the Lagrange multipliers method and the centralized iterative al...

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Published in:	Entropy (Basel, Switzerland) Vol. 24; no. 8; p. 1066
Main Authors:	Bao, Xiuwen, Jiang, Ming, Fang, Wenhao, Zhao, Chunming
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01.08.2022 MDPI
Subjects:	Compression ratio Control convolutional neural networks (CNNs) Decomposition Deep learning Design and construction Efficiency Error analysis Feedback Feedback control systems Iterative algorithms Iterative methods joint transceiver design Lagrange multiplier limited feedback precoding Machine learning Methods MIMO MIMO communication MIMO communications MMSE receivers Neural networks Optimization Performance degradation Sensors Technology application uplink precoding Uplinking Wireless networks China
ISSN:	1099-4300, 1099-4300
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Abstract	Multi-user multiple-input multiple-output (MU-MIMO) technology can significantly improve the spectral and energy efficiencies of wireless networks. In the uplink MU-MIMO systems, the optimal precoder design at the base station utilizes the Lagrange multipliers method and the centralized iterative algorithm to minimize the mean squared error (MSE) of all users under the power constraint. The precoding matrices need to be fed back to the user equipment to explore the potential benefits of the joint transceiver design. We propose a CNN-based compression network named PCQNet to minimize the feedback overhead. We first illustrate the effect of the trainable compression ratios and feedback bits on the MSE between the original precoding matrices and the recovered ones. We then evaluate the block error rates as the performance measure of the centralized implementation with an optimal minimum mean-squared error (MMSE) transceiver. Numerical results show that the proposed PCQNet achieves near-optimal performance compared with other quantized feedback schemes and significantly reduces the feedback overhead with negligible performance degradation.
AbstractList	Multi-user multiple-input multiple-output (MU-MIMO) technology can significantly improve the spectral and energy efficiencies of wireless networks. In the uplink MU-MIMO systems, the optimal precoder design at the base station utilizes the Lagrange multipliers method and the centralized iterative algorithm to minimize the mean squared error (MSE) of all users under the power constraint. The precoding matrices need to be fed back to the user equipment to explore the potential benefits of the joint transceiver design. We propose a CNN-based compression network named PCQNet to minimize the feedback overhead. We first illustrate the effect of the trainable compression ratios and feedback bits on the MSE between the original precoding matrices and the recovered ones. We then evaluate the block error rates as the performance measure of the centralized implementation with an optimal minimum mean-squared error (MMSE) transceiver. Numerical results show that the proposed PCQNet achieves near-optimal performance compared with other quantized feedback schemes and significantly reduces the feedback overhead with negligible performance degradation. Multi-user multiple-input multiple-output (MU-MIMO) technology can significantly improve the spectral and energy efficiencies of wireless networks. In the uplink MU-MIMO systems, the optimal precoder design at the base station utilizes the Lagrange multipliers method and the centralized iterative algorithm to minimize the mean squared error (MSE) of all users under the power constraint. The precoding matrices need to be fed back to the user equipment to explore the potential benefits of the joint transceiver design. We propose a CNN-based compression network named PCQNet to minimize the feedback overhead. We first illustrate the effect of the trainable compression ratios and feedback bits on the MSE between the original precoding matrices and the recovered ones. We then evaluate the block error rates as the performance measure of the centralized implementation with an optimal minimum mean-squared error (MMSE) transceiver. Numerical results show that the proposed PCQNet achieves near-optimal performance compared with other quantized feedback schemes and significantly reduces the feedback overhead with negligible performance degradation.Multi-user multiple-input multiple-output (MU-MIMO) technology can significantly improve the spectral and energy efficiencies of wireless networks. In the uplink MU-MIMO systems, the optimal precoder design at the base station utilizes the Lagrange multipliers method and the centralized iterative algorithm to minimize the mean squared error (MSE) of all users under the power constraint. The precoding matrices need to be fed back to the user equipment to explore the potential benefits of the joint transceiver design. We propose a CNN-based compression network named PCQNet to minimize the feedback overhead. We first illustrate the effect of the trainable compression ratios and feedback bits on the MSE between the original precoding matrices and the recovered ones. We then evaluate the block error rates as the performance measure of the centralized implementation with an optimal minimum mean-squared error (MMSE) transceiver. Numerical results show that the proposed PCQNet achieves near-optimal performance compared with other quantized feedback schemes and significantly reduces the feedback overhead with negligible performance degradation.
Audience	Academic
Author	Bao, Xiuwen Zhao, Chunming Fang, Wenhao Jiang, Ming
AuthorAffiliation	2 Purple Mountain Laboratories, Nanjing 211100, China 1 National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China; xwbao@seu.edu.cn (X.B.); whfang@seu.edu.cn (W.F.); cmzhao@seu.edu.cn (C.Z.)
AuthorAffiliation_xml	– name: 2 Purple Mountain Laboratories, Nanjing 211100, China – name: 1 National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China; xwbao@seu.edu.cn (X.B.); whfang@seu.edu.cn (W.F.); cmzhao@seu.edu.cn (C.Z.)
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SubjectTerms	Compression ratio Control convolutional neural networks (CNNs) Decomposition Deep learning Design and construction Efficiency Error analysis Feedback Feedback control systems Iterative algorithms Iterative methods joint transceiver design Lagrange multiplier limited feedback precoding Machine learning Methods MIMO MIMO communication MIMO communications MMSE receivers Neural networks Optimization Performance degradation Sensors Technology application uplink precoding Uplinking Wireless networks
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Title	PCQNet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
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