Harnessing Multimodal Sensing for Multi-user Beamforming in mmWave Systems

TL;DR

This paper introduces a sensor-aided beamforming framework for multi-user mmWave MIMO systems, utilizing deep learning to improve beamspace estimation and enhance spectral efficiency significantly.

Contribution

It proposes a novel deep neural network-based multimodal sensor fusion method with a new loss function for better beamspace estimation in multi-user mmWave systems.

Findings

Over 4x improvement in median sum-spectral efficiency

Effective use of sensor data for channel estimation

Enhanced multi-user beamforming performance

Abstract

Sensor-aided beamforming reduces the overheads associated with beam training in millimeter-wave (mmWave) multi-input-multi-output (MIMO) communication systems. Most prior work, though, neglects the challenges associated with establishing multi-user (MU) communication links in mmWave MIMO systems. In this paper, we propose a new framework for sensor-aided beam training in MU mmWave MIMO system. We leverage the beamspace representation of the channel that contains only the angles-of-departure (AoDs) of the channel's significant multipath components. We show that a deep neural network (DNN)-based multimodal sensor fusion framework can estimate the beamspace representation of the channel using sensor data. To aid the DNN training, we introduce a novel supervised soft-contrastive loss (SSCL) function that leverages the inherent similarity between channels to extract similar features from the sensor data for similar channels. Finally, we design an MU beamforming strategy that uses the estimated beamspaces of the channels to select analog precoders for all users in a way that prevents transmission to multiple users over the same directions. Compared to the baseline, our approach achieves more than 4$\times$ improvement in the median sum-spectral efficiency (SE) at 42 dBm equivalent isotropic radiated power (EIRP) with 4 active users. This demonstrates that sensor data can provide more channel information than previously explored, with significant implications for machine learning (ML)-based communication and sensing systems.