# Non-Iterative Reconstruction and Selection Network-Assisted Channel Estimation for mmWave MIMO Communications

## TL;DR

This paper introduces a new low-complexity channel estimator for mmWave MIMO systems that improves sensing performance while reducing computational costs.

## Contribution

A non-iterative reconstruction network (NIRNet) with a learning-based selection matrix is proposed for efficient mmWave channel estimation.

## Key findings

- NIRNet achieves better normalized mean squared error (NMSE) than existing methods.
- The proposed algorithm reduces computational complexity and training overhead significantly.
- Sensing resolution is improved under low SNR conditions using a denoising network.

## Abstract

Millimeter-wave (mmWave) MIMO systems have emerged as a key enabling technology for next-generation wireless networks, addressing the growing demand for ultra-high data rates through the utilization of wide bandwidths and large-scale antenna configurations. Beyond communication capabilities, these systems offer inherent advantages for integrated sensing applications, particularly in scenarios requiring precise object detection and localization. The sparse mmWave channel in the beamspace domain allows fewer radio-frequency (RF) chains by selecting dominant beams, boosting both communication efficiency and sensing resolution. However, existing channel estimation methods, such as learned approximate message passing (LAMP) networks, rely on computationally intensive iterations. This becomes particularly problematic in large-scale system deployments, where estimation inaccuracies can severely degrade sensing performance. To address these limitations, we propose a low-complexity channel estimator using a non-iterative reconstruction network (NIRNet) with a learning-based selection matrix (LSM). NIRNet employs a convolutional layer for efficient, non-iterative beamspace channel reconstruction, significantly reducing computational overhead compared to LAMP-based methods, which is vital for real-time sensing. The LSM generates a signal-aware Gaussian measurement matrix, outperforming traditional Bernoulli matrices, while a denoising network enhances accuracy under low SNR conditions, improving sensing resolution. Simulations show the NIRNet-based algorithm achieves a superior normalized mean squared error (NMSE) and an achievable sum rate (ASR) with lower complexity and reduced training overhead.