Zhang Rui, Tan Weiqiang, Nie Wenliang, Wu Xianda, Liu Ting
School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China.
School of Electronic and Information Engineering, Chongqing Three Gorges University, Chongqing 404000, China.
Sensors (Basel). 2022 May 23;22(10):3938. doi: 10.3390/s22103938.
Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems can significantly reduce the number of radio frequency (RF) chains by using lens antenna arrays, because it is usually the case that the number of RF chains is often much smaller than the number of antennas, so channel estimation becomes very challenging in practical wireless communication. In this paper, we investigated channel estimation for mmWave massive MIMO system with lens antenna array, in which we use a mixed (low/high) resolution analog-to-digital converter (ADC) architecture to trade-off the power consumption and performance of the system. Specifically, most antennas are equipped with low-resolution ADC and the rest of the antennas use high-resolution ADC. By utilizing the sparsity of the mmWave channel, the beamspace channel estimation can be expressed as a sparse signal recovery problem, and the channel can be recovered by the algorithm based on compressed sensing. We compare the traditional channel estimation scheme with the deep learning channel-estimation scheme, which has a better advantage, such as that the estimation scheme based on deep neural network is significantly better than the traditional channel-estimation algorithm.
毫米波（mmWave）大规模多输入多输出（MIMO）系统可以通过使用透镜天线阵列显著减少射频（RF）链路的数量，因为通常情况下，RF链路的数量往往远小于天线的数量，所以在实际无线通信中，信道估计变得非常具有挑战性。在本文中，我们研究了具有透镜天线阵列的毫米波大规模MIMO系统的信道估计，其中我们使用混合（低/高）分辨率模数转换器（ADC）架构来权衡系统的功耗和性能。具体而言，大多数天线配备低分辨率ADC，其余天线使用高分辨率ADC。通过利用毫米波信道的稀疏性，波束空间信道估计可以表示为一个稀疏信号恢复问题，并且可以通过基于压缩感知的算法来恢复信道。我们将传统信道估计方案与深度学习信道估计方案进行比较，深度学习信道估计方案具有更好的优势，例如基于深度神经网络的估计方案明显优于传统信道估计算法。
