On Low-complexity Lattice Reduction Algorithms for Large-scale MIMO Detection: the Blessing of Sequential Reduction

TL;DR

This paper introduces a low-complexity sequential reduction framework for lattice reduction in large-scale MIMO detection, enabling efficient hash-based algorithms that maintain performance with reduced computational cost.

Contribution

The paper proposes a novel sequential reduction framework and a hash-based lattice reduction algorithm tailored for large-scale MIMO detection, reducing complexity while preserving accuracy.

Findings

Hash-based SR algorithm has the lowest complexity among compared methods.

The proposed algorithm maintains error performance comparable to existing methods.

Performance bounds are established for lattice dimensions up to 4.

Abstract

Lattice reduction is a popular preprocessing strategy in multiple-input multiple-output (MIMO) detection. In a quest for developing a low-complexity reduction algorithm for large-scale problems, this paper investigates a new framework called sequential reduction (SR), which aims to reduce the lengths of all basis vectors. The performance upper bounds of the strongest reduction in SR are given when the lattice dimension is no larger than 4. The proposed new framework enables the implementation of a hash-based low-complexity lattice reduction algorithm, which becomes especially tempting when applied to large-scale MIMO detection. Simulation results show that, compared to other reduction algorithms, the hash-based SR algorithm exhibits the lowest complexity while maintaining comparable error performance.