Deep Learning-Based Channel Estimation for mmWave Massive MIMO Systems in Mixed-ADC Architecture

Abstract

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.

Metaheuristic hybrid deep learning-based channel estimation and precoding for millimeter-wave massive MIMO system

“…To elucidate this matter, an instance is provided of a beam prediction method that extracts beam information from previous observations-including power and the ideal beam indexusing machine learning and situational awareness [23]. Additionally, an approximate message-passing network employing learned denoising techniques has been proposed for the estimation of mmWave communication systems featuring lens antenna arrays, effectively finding and eliminating noise to refine channel estimation [24]. Hybrid precoding vast MIMO systems operating at the millimetre wave do not incorporate channel estimates into these methods [25].…”

Deep Learning Based One Bit-ADCs Efficient Channel Estimation Using Fewer Pilots Overhead for Massive MIMO System

“…For handling the CE performance in a 1-bit massive MIMO system, DL techniques demonstrate superior effectiveness compared to traditional methods. Recently, there have been significant researches conducted on DL-based CE for 1 and mixed-bits ADCs [37]- [46]. Specifically, to calculate the channel matrix using 1-bit quantization for incoming information, a conditional generative adversarial network (cGAN) was constructed in [39].…”

“…In the first phase, a recovering DNN model is used for coarse CE with fewer-ADC antennas, while in the second phase, a refining DNN is employed for CE with all antennas. In [46], authors proposed a modified DNN-based CE with a mixed-ADC architecture, where the majority of the antennas are fitted with low-resolution ADC, and the remainder are outfitted with high-resolution ADC, respectively. However, recurrent neural network (RNN) is one of the DL models better suited for handling periodic and sequential data than DNN and CNN, as mentioned in [31].…”