Sphere Decoding (SD) enables real-time quasioptimal 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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.