Adaptive Switched Lattice Reduction-Aided Linear Detection Techniques for MIMO Systems

TL;DR

This paper proposes an adaptive switched lattice reduction technique for MIMO detection that balances near-optimal performance with manageable computational complexity, improving upon traditional methods.

Contribution

It introduces a simple, adaptive algorithm based on complex LLL to approximate optimal lattice reduction in MIMO detection.

Findings

Achieves near-optimal detection performance

Reduces noise enhancement in MIMO systems

Maintains reasonable computational complexity

Abstract

Lattice reduction (LR) aided multiple-input-multiple-out (MIMO) linear detection can achieve the maximum receive diversity of the maximum likelihood detection (MLD). By emloying the most commonly used Lenstra, Lenstra, and L. Lovasz (LLL) algorithm, an equivalent channel matrix which is shorter and nearly orthogonal is obtained. And thus the noise enhancement is greatly reduced by employing the LR-aided detection. One problem is that the LLL algorithm can not guarantee to find the optimal basis. The optimal lattice basis can be found by the Korkin and Zolotarev (KZ) reduction. However, the KZ reduction is infeasible in practice due to its high complexity. In this paper, a simple algorithm is proposed based on the complex LLL (CLLL) algorithm to approach the optimal performance while maintaining a reasonable complexity.