Deterministic Sort-Free Candidate Pruning for Scalable MIMO Box Decoding

TL;DR

This paper introduces two deterministic, low-complexity pruning strategies for MIMO box decoding that significantly reduce complexity while maintaining performance, enabling scalable and hardware-efficient large-scale MIMO detection.

Contribution

The paper proposes novel, geometry-based pruning methods that control node growth in sort-free MIMO detection, enhancing scalability and hardware efficiency.

Findings

Substantial complexity reduction with negligible error-rate impact

Enables fully parallel, hardware-efficient implementations

Maintains QAM-order independence in detection

Abstract

Box Decoding is a sort-free tree-search MIMO detector whose complexity is independent of the QAM order, achieved by searching a fixed candidate box around a zero-forcing (ZF) estimate. However, without pruning, the number of visited nodes grows exponentially with the MIMO dimension, limiting scalability. This work proposes two deterministic, low-complexity, sort-free pruning strategies to control node growth. By exploiting the geometric symmetry of the QAM grid and the relative displacement between the ZF estimate and nearby constellation points, the proposed methods eliminate unnecessary metric evaluations while preserving QAM-order independence. The resulting detector achieves substantial complexity reduction with negligible error-rate degradation and enables fully parallel, hardware-efficient implementations for large-scale MIMO and higher-order QAM systems.