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.