Software acceleration of multi-user MIMO uplink detection on GPU

This paper presents the exploration of GPU-accelerated block-wise decompositions for zero-forcing (ZF) based QR and Cholesky methods applied to massive multiple-input multiple-output (MIMO) uplink detection algorithms. Three algorithms are evaluated: ZF with block Cholesky decomposition, ZF with block QR decomposition (QRD), and minimum mean square error (MMSE) with block Cholesky decomposition. The latter was the only one previously explored, but it used standard Cholesky decomposition. Our approach achieves an 11% improvement over the previous GPU-accelerated MMSE study. Through performance analysis, we observe a trade-off between precision and execution time. Reducing precision from FP64 to FP32 improves execution time but increases bit error rate (BER), with ZF-based QRD reducing execution time from 2 . 04 μ s to 1 . 24 μ s for a 128 × 8 MIMO size. The study also highlights that larger MIMO sizes, particularly 2048 × 32, require GPUs to fully utilize their computational and memory capabilities, especially under FP64 precision. In contrast, smaller matrices are compute-bound. Our results recommend GPUs for larger MIMO sizes, as they offer the parallelism and memory resources necessary to efficiently handle the computational demands of next-generation networks. This work paves the way for scalable, GPU-based massive MIMO uplink detection systems. • GPU acceleration of ZF and MMSE algorithms using block QR and Cholesky decompositions. • ZF-QRD achieved 1 . 24 μ s for 128 × 8 MIMO (1% memory and 4% GPU usage at 14,336 vectors). • GPUs offer performance improvements and are recommended for large MIMO sizes. • Lowering the precision improves speed up but increases BER in all implemented algorithms.