Exploiting Spatial Correlation for Pilot Reuse in Single-Cell mMTC

TL;DR

This paper proposes novel features derived from channel covariance matrices to improve pilot reuse in multi-sector mMTC systems, reducing pilot collisions and enhancing spectral efficiency.

Contribution

It introduces covariance matrix-based features, including CMD and CC, for better pilot assignment in correlated fading channels, outperforming existing algorithms.

Findings

CC-based feature outperforms CMD-based in error rate and rate.

Proposed features reduce pilot collisions in multi-sector mMTC.

Enhanced spectral efficiency with feature-based pilot reuse.

Abstract

As a key enabler for massive machine-type communications (mMTC), spatial multiplexing relies on massive multiple-input multiple-output (mMIMO) technology to serve the massive number of user equipments (UEs). To exploit spatial multiplexing, accurate channel estimation through pilot signals is needed. In mMTC systems, it is impractical to allocate a unique orthogonal pilot sequence to each UE as it would require too long pilot sequences, degrading the spectral efficiency. This work addresses the design of channel features from correlated fading channels to assist the pilot assignment in multi-sector mMTC systems under pilot reuse of orthogonal sequences. In order to reduce pilot collisions and to enable pilot reuse, we propose to extract features from the channel covariance matrices that reflect the level of orthogonality between the UEs channels. Two features are investigated: covariance matrix distance (CMD) feature and CMD-aided channel charting (CC) feature. In terms of symbol error rate and achievable rate, the CC-based feature shows superior performance than the CMD-based feature and baseline pilot assignment algorithms.