Environment-Aware and Training-Free Beam Alignment for mmWave Massive MIMO via Channel Knowledge Map

TL;DR

This paper introduces a novel environment-aware, training-free beam alignment method for mmWave massive MIMO systems using channel knowledge maps, significantly reducing training overhead and improving communication rates.

Contribution

It proposes the use of channel knowledge maps (CKM) for beam alignment, a new approach that eliminates the need for traditional training-based methods in mmWave MIMO systems.

Findings

CKM-based beam alignment drastically reduces training overhead.

The method maintains high performance even with moderate user location errors.

Significantly improves effective communication rate compared to existing schemes.

Abstract

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) communication system is expected to achieve enormous transmission rate, provided that the transmit and receive beams are properly aligned with the MIMO channel. However, existing beam alignment techniques rely on either channel estimation or beam sweeping, which incur prohibitively high training overhead, especially for future wireless systems with further increased antenna dimensions and more stringent requirement on cost-effective hardware architectures. In this paper, we propose a new beam alignment technique, which is environment-aware and training-free, by utilizing the emerging concept of channel knowledge map (CKM), together with the user location information that is readily available in contemporary wireless systems. CKM is a site-specific database, tagged with the transmitter/receiver locations, which contains useful channel information to facilitate or even obviate real-time channel state information (CSI) acquistion. Two instances of CKM are proposed for beam alignment in mmWave massive MIMO systems, namely channel path map (CPM) and beam index map (BIM). It is shown that compared with existing training-based beam alignment schemes, the proposed CKM-enabled environment-aware beam alignment is able to drastically improve the effective communication rate, even with moderate user location errors, thanks to its significant saving of the prohibitive training overhead.