Bao Xiuwen, Jiang Ming, Fang Wenhao, Zhao Chunming
National Mobile Communications Research Laboratory, Southeast University, Nanjing 210096, China.
Purple Mountain Laboratories, Nanjing 211100, China.
Entropy (Basel). 2022 Aug 2;24(8):1066. doi: 10.3390/e24081066.
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.
多用户多输入多输出（MU-MIMO）技术可以显著提高无线网络的频谱效率和能量效率。在上行链路MU-MIMO系统中，基站的最优预编码器设计利用拉格朗日乘数法和集中式迭代算法，在功率约束下最小化所有用户的均方误差（MSE）。预编码矩阵需要反馈给用户设备，以探索联合收发器设计的潜在益处。我们提出了一种基于卷积神经网络（CNN）的压缩网络，名为PCQNet，以最小化反馈开销。我们首先说明了可训练压缩率和反馈比特对原始预编码矩阵与恢复后的预编码矩阵之间MSE的影响。然后，我们将误块率作为具有最优最小均方误差（MMSE）收发器的集中式实现的性能指标进行评估。数值结果表明，与其他量化反馈方案相比，所提出的PCQNet实现了接近最优的性能，并且在性能下降可忽略不计的情况下显著降低了反馈开销。
