Joint Channel Estimation and Mixed-ADCs Allocation for Massive MIMO via Deep Learning

TL;DR

This paper introduces a deep learning framework for joint pilot design, channel estimation, and mixed-ADCs allocation in mmWave massive MIMO systems, reducing hardware costs and improving estimation accuracy.

Contribution

It proposes a novel end-to-end deep learning architecture that jointly optimizes pilot signals, channel estimation, and ADC allocation for mixed-ADC massive MIMO systems.

Findings

Deep learning-based methods outperform traditional estimators.

Joint optimization improves channel estimation accuracy.

The approach reduces hardware costs while maintaining performance.

Abstract

Millimeter wave (mmWave) multi-user massive multi-input multi-output (MIMO) is a promising technique for the next generation communication systems. However, the hardware cost and power consumption grow significantly as the number of radio frequency (RF) components increases, which hampers the deployment of practical massive MIMO systems. To address this issue and further facilitate the commercialization of massive MIMO, mixed analog-to-digital converters (ADCs) architecture has been considered, where parts of conventionally assumed full-resolution ADCs are replaced by one-bit ADCs. In this paper, we first propose a deep learning-based (DL) joint pilot design and channel estimation method for mixed-ADCs mmWave massive MIMO. Specifically, we devise a pilot design neural network whose weights directly represent the optimized pilots, and develop a Runge-Kutta model-driven densely connected network as the channel estimator. Instead of randomly assigning the mixed-ADCs, we then design a novel antenna selection network for mixed-ADCs allocation to further improve the channel estimation accuracy. Moreover, we adopt an autoencoder-inspired end-to-end architecture to jointly optimize the pilot design, channel estimation and mixed-ADCs allocation networks. Simulation results show that the proposed DL-based methods have advantages over the traditional channel estimators as well as the state-of-the-art networks.