GfcLLL: A Greedy Selection Based Approach for Fixed-Complexity LLL Reduction

TL;DR

This paper introduces two greedy selection based fixed-complexity LLL reduction algorithms that improve the bit error rate of Babai points while maintaining or reducing computational time.

Contribution

The paper proposes novel greedy selection based fixed-complexity LLL algorithms, GfcLLL(1) and GfcLLL(2), enhancing efficiency and error performance over existing methods.

Findings

GfcLLL algorithms achieve lower BER in Babai points.

They operate with similar or reduced CPU time.

Simulations validate their improved performance.

Abstract

The LLL lattice reduction has been widely used to decrease the bit error rate (BER) of the Babai point, but its running time varies much from matrix to matrix. To address this problem, some fixed-complexity LLL reductions (FCLLL) have been proposed. In this paper, we propose two greedy selection based FCLLL algorithms: GfcLLL(1) and GfcLLL(2). Simulations show that both of them give Babai points with lower BER in similar or much shorter CPU time than existing ones.