Towards approaching near-optimal MIMO detection performance ONAC-programmable baseband processor

Abstract

Lattice Reduction aided softoutput MIMO detectors have been demonstrated to offer a promising gain. However, computing Log-Likelihood ratios (LLR) for near-optimal MIMO detection, still poses a significant challenge for practical implementations. In this work, we present counter-ML bitflipping algorithm for LLR generation. The proposed LLR generation algorithm has been designed to take advantage of the previously reported list generation algorithm, MultiTree Selective Spanning (MTSS), by maximizing the reuse of computations. Afterwards, a C-programmable MIMO detector architecture providing both data level parallelism (DLP) and instruction level parallelism (ILP), is designed for implementation. The proposed solution supports multiple MIMO detection modes, with both hard and softoutput. Performance of the proposed solution can be tuned ranging from SIC to near-ML to near-MAP, by adjusting a single parameter. In case of 4 × 4 QAM-64, it achieves peak-throughputs of 2.43Gbps and 629Mbps in case of hard and softoutput MIMO detection, with only 66.37mW and 76.14mW respective power consumption.