Exploration of Lattice Reduction Aided Soft-Output MIMO Detection on a DLP/ILP Baseband Processor

Abstract

Lattice Reduction aided soft output MIMO detectors (LR-SOMD) have been demonstrated to offer a promising gain. This work explores the potential of implementing a LR-SOMD on a parallel programmable baseband processor. In this paper, first a LR algorithm called the Data Regularized Parallel Lattice Reduction algorithm (DRP-LR) is proposed. Afterwards, a low-complexity LR-SOMD, Radius Constrained Multi-Tree Selective Spanning (RC-MTSS) is presented. RC-MTSS uses a novel multiple-tree search approach for LR-SOMD, while combining the benefits of Sphere Detection (SD) and Selective Spanning with Fast Enumeration (SSFE). A fixed complexity LR-SOMD, Multi-Tree Selective Spanning (MTSS) is also proposed for implementation. Both the algorithms, DRP-LR and MTSS, are enabled to exploit data level parallelism (DLP) and instruction level parallelism (ILP). In order to evaluate performance, the proposed DRP-LR and MTSS are implemented on the ADRES baseband processor for a 4 × 4 LTE system using QAM-64. DRP-LR achieves an average throughput of 33.33 M LR per second, which is comparable to recently reported ASIC implementations, while MTSS shows an average throughput of 730 Mbps on the same processor. To the best of authors' knowledge, this is the first reported implementation of a LR-SOMD algorithm on a parallel programmable baseband processor.