Configurable Quasi-Optimal Sphere Decoding for Scalable MIMO Communications

Sphere Decoding (SD) enables real-time quasi-optimal symbol detection for Multiple-Input Multiple-Output (MIMO) communication systems via custom circuit accelerators. Configurable SDs allow accelerator cost to be balanced with detection accuracy for the most constrained MIMO environments, such as power-constrained Internet-of-Things (IoT) scenarios. However this high detection accuracy comes at high accelerator cost. This paper proposes a novel configurable SD which addresses this issue. A Robust Bounded Spanning with Fast Enumeration (R-BSFE) approach employs novel strategies for channel matrix pre-processing and symbol enumeration to maintain quasi-ML accuracy whilst reducing complexity by up to 74%. This enables accelerators for 802.11n on Xilinx FPGA with significantly lower cost and higher throughput. To the best of the authors' knowledge, the accelerators produced are the highest performance, lowest cost quasi-ML SD accelerators on record.

Automated summary

The paper introduces a novel configurable Sphere Decoding (SD) approach that reduces complexity while maintaining quasi-ML accuracy, resulting in lower cost accelerators with higher throughput. This new Robust Bounded Spanning with Fast Enumeration (R-BSFE) method significantly improves performance and cost efficiency for MIMO communication systems.