Low-Latency Lattice Reduction Based on Dual-Basis Updates for High-Dimensional MIMO Streams

Abstract

Lattice reduction (LR) techniques have been widely adopted to enhance the detection performance with low additional complexity in spatial multiplexed multiple-input multiple-output (MIMO) systems. However, in large MIMO systems, the required number of iterations and the corresponding latency and complexity of the existing LR algorithms increase exponentially. In this brief, we propose a low-latency LR method based on Seysen's algorithm (SA) for high-dimensional MIMO systems. The proposed algorithm introduces dual-index selection and simultaneously updates two basis pairs without the recalculation of metrics to reduce the latency and complexity. Additionally, it introduces a reduced candidate set to decrease the computational complexity and memory usage. The numerical results demonstrate that the proposed LR-aided linear detector outperforms the conventional Lenstra-Lenstra-Lovász algorithm. Furthermore, the performance of the proposed method is comparable to that of SA; it achieves reductions of 40% and 12.5% in the number of iterations and computational complexity, respectively. The proposed 65-nm CMOS implementation achieves an overall latency of 196 ns and a throughput of 25.5 M matrix/s with a clock frequency of 357.1 MHz for $8 \times 8$ MIMO systems.