This paper proposes an efficient channel information feedback scheme to reduce the feedback overhead of multi-user multiple-input multiple-output (MU-MIMO) hybrid beamforming systems. As massive machine type communication (mMTC) was considered in the deployments of 5G, a transmitter of the hybrid beamforming system should communicate with multiple devices at the same time. To communicate with multiple devices in the same time and frequency slot, high-dimensional channel information should be used to control interferences between the receivers. Therefore, the feedback overhead for the channels of the devices is impractically high. To reduce the overhead, this paper uses common sparsity of channel and nonlinear quantization. To find a common sparse part of a wide frequency band, the proposed system uses minimum mean squared error orthogonal matching pursuit (MMSE-OMP). After the search of the common sparse basis, sparse vectors of subcarriers are searched by using the basis. The sparse vectors are quantized by a nonlinear codebook that is generated by conditional random vector quantization (RVQ). For the conditional RVQ, the Linde–Buzo–Gray (LBG) algorithm is used in conditional vector space. Typically, elements of sparse vectors are sorted according to magnitude by the OMP algorithm. The proposed quantization scheme considers the property for the conditional RVQ. For feedback, indices of the common sparse basis and the quantized sparse vectors are delivered and the channel is recovered at a transmitter for precoding of MU-MIMO. The simulation results show that the proposed scheme achieves lower MMSE for the recovered channel than that of the linear quantization scheme. Furthermore, the transmitter can adopt analog and digital precoding matrix freely by the recovered channel and achieve higher sum rate than that of conventional codebook-based MU-MIMO precoding schemes.

中文翻译：

---

多用户 MIMO 混合波束赋形系统的有效信道反馈方案

本文提出了一种有效的信道信息反馈方案，以减少多用户多输入多输出 (MU-MIMO) 混合波束成形系统的反馈开销。由于在 5G 的部署中考虑了大规模机器类型通信 (mMTC)，因此混合波束成形系统的发射机应同时与多个设备进行通信。为了在同一时隙内与多个设备通信，应使用高维信道信息来控制接收器之间的干扰。因此，设备通道的反馈开销高得不切实际。为了减少开销，本文使用了信道和非线性量化的共同稀疏性。要找到宽频带的公共稀疏部分，所提出的系统使用最小均方误差正交匹配追踪（MMSE-OMP）。公共稀疏基搜索完成后，利用该基搜索子载波的稀疏向量。稀疏向量由非线性码本量化，该码本由条件随机向量量化 (RVQ) 生成。对于条件 RVQ，在条件向量空间中使用 Linde-Buzo-Gray (LBG) 算法。通常，稀疏向量的元素由 OMP 算法根据大小进行排序。建议的量化方案考虑了条件 RVQ 的特性。对于反馈，公共稀疏基的索引和量化的稀疏向量被传递，并且信道在发射机处被恢复用于 MU-MIMO 的预编码。仿真结果表明，与线性量化方案相比，所提出的方案在恢复信道上实现了更低的MMSE。此外，发射端可以通过恢复信道自由采用模拟和数字预编码矩阵，实现比传统的基于码本的MU-MIMO预编码方案更高的和速率。