Accelerating massive MIMO uplink detection on GPU for SDR systems

We present a reconfigurable GPU-based uplink detector for massive MIMO software-defined radio (SDR) systems. To enable high throughput, we implement a configurable linear minimum mean square error (MMSE) soft-output detector and reduce the complexity without sacrificing its error-rate performance. To take full advantage of the GPU computing resources, we exploit the algorithm´s inherent parallelism and make use of efficient CUDA libraries and the GPU´s hierarchical memory resources. We furthermore use multi-stream scheduling and multi-GPU workload deployment strategies to pipeline streaming-detection tasks with little host-device memory copy overhead. Our flexible design is able to switch between a high accuracy Cholesky-based detection mode and a high throughput conjugate gradient (CG)-based detection mode, and supports various antenna configurations. Our GPU implementation exceeds 250 Mb/s detection throughput for a 128×16 antenna system.