GPU Accelerated Reconfigurable Detector and Precoder for Massive MIMO SDR Systems

Date
2015-12-02
Journal Title
Journal ISSN
Volume Title
Publisher
Description
Abstract

We present a reconfigurable GPU-based unified detector and precoder for massive MIMO software-defined radio systems. To enable high throughput, we implement the linear minimum mean square error detector/precoder and further reduce the algorithm complexity by numerical approximation without sacrificing the error-rate performance. For efficient GPU implementation, we explore the algorithm's inherent parallelism and take advantage of the GPU's numerous computing cores and hierarchical memories for the optimization of kernel computations. We furthermore perform multi-stream scheduling and multi-GPU workload deployment to pipeline multiple detection or precoding tasks on GPU streams for the reduction of host-device memory copy overhead. The flexible design supports both detection and precoding and can switch between Cholesky based mode and conjugate gradient based mode for accuracy and complexity tradeoff. The GPU implementation exceeds 250 Mb/s detection and precoding throughput for a 128x16 antenna system.

Description
Degree
Master of Science
Type
Thesis
Keywords
GPGPU, Massive MIMO, Software-defined radio
Citation

Li, Kaipeng. "GPU Accelerated Reconfigurable Detector and Precoder for Massive MIMO SDR Systems." (2015) Master’s Thesis, Rice University. https://hdl.handle.net/1911/88088.

Has part(s)
Forms part of
Published Version
Rights
Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.
Link to license
Citable link to this page