ISDNN: A Deep Neural Network for Channel Estimation in Massive MIMO systems

TL;DR

This paper introduces ISDNN, a deep neural network based on projected gradient descent for efficient channel estimation in massive MIMO systems, outperforming existing methods in accuracy and speed.

Contribution

It proposes a novel deep unfolding DNN architecture for channel estimation, incorporating side information for improved performance in massive MIMO systems.

Findings

ISDNN outperforms DetNet in accuracy and speed.

S-ISDNN reduces training time further.

Deep unfolding enhances channel estimation efficiency.

Abstract

Massive Multiple-Input Multiple-Output (massive MIMO) technology stands as a cornerstone in 5G and beyonds. Despite the remarkable advancements offered by massive MIMO technology, the extreme number of antennas introduces challenges during the channel estimation (CE) phase. In this paper, we propose a single-step Deep Neural Network (DNN) for CE, termed Iterative Sequential DNN (ISDNN), inspired by recent developments in data detection algorithms. ISDNN is a DNN based on the projected gradient descent algorithm for CE problems, with the iterative iterations transforming into a DNN using the deep unfolding method. Furthermore, we introduce the structured channel ISDNN (S-ISDNN), extending ISDNN to incorporate side information such as directions of signals and antenna array configurations for enhanced CE. Simulation results highlight that ISDNN significantly outperforms another DNN-based CE (DetNet), in terms of training time (13%), running time (4.6%), and accuracy (0.43 dB). Furthermore, the S-ISDNN demonstrates even faster than ISDNN in terms of training time, though its overall performance still requires further improvement.