Energy Efficiency Optimization in Adaptive Transmit Antenna Selection Systems With Limited Feedback

Wu, Tong; Zou, Yulong

doi:10.1109/jiot.2022.3206460

Cited by 5 publications

(1 citation statement)

References 35 publications

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“…To handle the quantization error, an MMSE precoding was proposed in [36] based on the rate distortion theory, where the estimation error variance is replaced by the quantization error variance. In [37] and [38], the correlation between the quantized CSI and its quantization error was utilized to conduct robust precoding designs. Nevertheless, the above methods work only for multiuser multiple-input single-output (MISO) systems, and cannot be employed in MU-MIMO scenarios.…”

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confidence: 99%

Robust Precoding Designs for Multiuser MIMO Systems With Limited Feedback

Zhou,

Zhang,

Debbah

et al. 2024

IEEE Trans. Wireless Commun.

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It has been well known that the achievable rate of multiuser multiple-input multiple-output systems with limited feedback is severely degraded by quantization errors when the number of feedback bits is not sufficient. To overcome such a rate degradation, we propose new robust precoding designs which can compensate for the quantization errors. In this paper, we first analyze the achievable rate of traditional precoding designs for limited feedback systems. Then, we obtain an approximation of the second-order statistics of quantized channel state information. With the aid of the derived approximation, we propose robust precoding designs in terms of the mean square error (MSE) with conditional expectation in non-iterative and iterative fashions. For the non-iterative precoding design, we study a robust minimum MSE (MMSE) precoding algorithm by extending a new channel decomposition. Also, in the case of iterative precoding, we investigate a robust weighted MMSE (WMMSE) precoding to further improve the achievable rate. Simulation results show that the proposed precoding schemes yield significant improvements over traditional precoding designs.

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confidence: 99%