Signal Detection for Large MIMO Systems Using Sphere Decoding on FPGAs


Wireless communication systems rely on aggressive spatial multiplexing Multiple-Input Multiple-Output (MIMO) access points to enhance network throughput. A significant computational hurdle for large MIMO systems is signal detection and decoding, which has exponentially increasing computational complexity as the number of antennas increases. Hence, the feasibility of large MIMO systems depends on suitable implementations of signal decoding schemes. This paper presents an FPGA-based Sphere Decoder (SD) architecture that provides high-performance signal decoding for large MIMO systems, supporting up to 16-QAM modulation. The SD algorithm is refactored to map well to the FPGA architecture using a GEMM-based approach to exploit the parallel computational power of FPGAs. We implement FPGA-specific optimization techniques to improve computational complexity. We show significant improvement in time to decode the received signal with under 10−2 BER. The design is deployed on a Xilinx Alveo U280 FPGA and shows up to a 9× speedup compared to optimized multi-core CPU execution, achieving real-time requirements. Our proposed design reduces power consumption by a geo-mean of 38.1× compared to CPU implementation, which is important in real-world deployments. We also evaluate our design against alternative approaches on GPU.

In IEEE International Parallel and Distributed Processing Symposium (IPDPS)
Mohamed W. Hassan
Mohamed W. Hassan
Postdoctoral Fellow

My research interests include distributed robotics, mobile computing and programmable matter.

Suhaib A. Fahmy
Suhaib A. Fahmy
Associate Professor of Computer Science

Suhaib is Principal Investigator of the Accelerated Connected Computing Lab (ACCL) at KAUST. His research explores hardware acceleration of complex algorithms and the integration of these accelerators within wider computing infrastructure.