An enhanced fixed-complexity LLL algorithm for MIMO detection

Lenstra-Lenstra-Lovász (LLL) lattice reduction technique has been applied to multiple-input multiple-output (MIMO) detectors to collect full diversity while enjoying low complexity. However, the original LLL algorithm has variable complexity, which is not desirable in hardware implementation. To solve this problem, some fixed-complexity LLL (fcLLL) algorithms have recently been proposed by using the fixed-column traverse strategy with limited number of LLL iterations. The existing fcLLL algorithms are designed to process each column with equal priority, which is not optimized in terms of error performance and complexity. In this paper, we propose an enhanced fcLLL algorithm with a novel column traverse strategy by allocating priorities to columns based on the characteristics of LLL and MIMO detection. In addition, we propose an improved termination criterion without sacrificing the error performance in the proposed fcLLL algorithm. Simulations show that our proposed fcLLL algorithm converges faster than LLL and existing fcLLL algorithms, and yields better error performance than the LLL and existing fcLLL algorithms when the maximum number of LLL iterations is fixed. Furthermore, in large MIMO systems, our proposed fcLLL algorithm exhibits significant complexity advantage, saving about 90% LLL iterations in average compared to the existing fcLLL algorithms for a 128× 128 MIMO system with 64-QAM.