Symbol-Level Noise-Guessing Decoding with Antenna Sorting for URLLC Massive MIMO

TL;DR

This paper introduces a novel symbol-level GRAND decoding method for massive MIMO systems that leverages antenna sorting and channel state information to significantly reduce decoding complexity while supporting URLLC requirements.

Contribution

It generalizes symbol-level GRAND to spatial multiplexing in massive MIMO and proposes antenna sorting to enhance decoding efficiency and reliability.

Findings

Over 80% reduction in decoding complexity with antenna sorting.

Effective support for URLLC with short codewords and reduced latency.

Leveraging channel-state information improves decoding performance.

Abstract

Supporting ultra-reliable and low-latency communication (URLLC) is a challenge in current wireless systems. Channel codes that generate large codewords improve reliability but necessitate the use of interleavers, which introduce undesirable latency. Only short codewords can eliminate the requirement for interleaving and reduce decoding latency. This paper suggests a coding and decoding method which, when combined with the high spectral efficiency of spatial multiplexing, can provide URLLC over a fading channel. Random linear coding and high-order modulation are used to transmit information over a massive multiple-input multiple-output (mMIMO) channel, followed by zero-forcing detection and guessing random additive noise decoding (GRAND) at a receiver. A variant of GRAND, called symbol-level GRAND, originally proposed for single-antenna systems that employ high-order modulation schemes, is generalized to spatial multiplexing. The paper studies the impact of the orthogonality defect of the underlying mMIMO lattice on symbol-level GRAND, and proposes to leverage side-information that comes from the mMIMO channel-state information and relates to the reliability of each receive antenna. This induces an antenna sorting step, which further reduces decoding complexity by over 80\% when compared to bit-level GRAND.