Title: Efficient LLL-based Lattice Reduction for MIMO Detection: from Algorithms to Implementations
Dr. Xiaoli Ma, ECE, Chair , Advisor
Dr. Gee-Kung Chang, ECE
Dr. Robert Baxley, GTRI
Dr. Geoffrey Li, ECE
Dr. Yao Xie, ISyE
Lenstra-Lenstra-Lovasz (LLL) algorithm has been adopted as a lattice reduction (LR) technique for multiple-input multiple-output (MIMO) systems in wireless communications to improve performance with low complexity. Recently, some enhanced LLL variants are proposed, such as greedy LLL algorithms with fast convergence and fixed-complexity LLL (fcLLL) algorithms with constant hardware run-time. However, the existing greedy LLL and fcLLL algorithms are still inefficient which do not fully exploit the inherent characteristics of LLL algorithms. In this dissertation, we present enhanced greedy LLL and fcLLL algorithms for LR-aided MIMO detectors, which deal with the aforementioned shortcomings in the existing greedy LLL and fcLLL algorithms. Furthermore, we implement the proposed enhanced fcLLL algorithm in hardware by two types of architectures for low complexity and high throughput, respectively. Both simulations and implementations show that the proposed algorithms and architectures exhibit much better performance than the state-of-the-art solutions.