Fountain Code-Inspired Channel Estimation for Multi-user Millimeter Wave MIMO Systems

TL;DR

This paper introduces SWIFT, a fountain code-inspired iterative framework for efficient multi-user mmWave channel estimation that adapts measurement counts to channel conditions, outperforming fixed-beamforming methods.

Contribution

It proposes a novel adaptive channel estimation method for multi-user mmWave systems using fountain code principles, enabling simultaneous estimation without beam adaptation.

Findings

SWIFT significantly outperforms fixed-measurement approaches.

The method adapts measurement numbers to channel conditions.

It effectively estimates multiple user channels simultaneously.

Abstract

This paper develops a novel channel estimation approach for multi-user millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent mmWave channel sparsity, we propose a novel simultaneous-estimation with iterative fountain training (SWIFT) framework, in which the average number of channel measurements is adapted to various channel conditions. To this end, the base station (BS) and each user continue to measure the channel with a random subset of transmit/receive beamforming directions until the channel estimate converges. We formulate the channel estimation process as a compressed sensing problem and apply a sparse estimation approach to recover the virtual channel information. As SWIFT does not adapt the BS's transmitting beams to any single user, we are able to estimate all user channels simultaneously. Simulation results show that SWIFT can significantly outperform existing random-beamforming based approaches that use a fixed number of measurements, over a range of signal-to-noise ratios and channel coherence times.