Paper Info
Title
GPU Acceleration of a Configurable N-Way MIMO Detector for Wireless Systems
Abstract
Multiple-input multiple-output (MIMO) wireless is an enabling technology for high spectral efficiency and has been adopted in many modern wireless communication standards, such as 3GPP-LTE and IEEE 802.11n. However, (optimal) maximum a-posteriori (MAP) detection suffers from excessively high computational complexity, which prevents its deployment in practical systems. Hence, many algorithms have been proposed in the literature that trade-off performance versus detection complexity. In this paper, we propose a flexible N-Way MIMO detector that achieves excellent error-rate performance and high throughput on graphics processing units (GPUs). The proposed detector includes the required QR decomposition step and a tree-search detector, which exploits the massive parallelism available in GPUs. The proposed algorithm performs multiple tree searches in parallel, which leads to excellent error-rate performance at low computational complexity on different GPU architectures, such as Nvidia Fermi and Kepler. We highlight the flexibility of the proposed detector and demonstrate that it achieves higher throughput than existing GPU-based MIMO detectors while achieving the same or better error-rate performance.
Year
DOI
Venue
2014
10.1007/s11265-014-0877-0
Journal of Signal Processing Systems
Keywords
Field
DocType
MIMO,GPU,Parallel processing,GPGPU,Detectors,Maximum likelihood,Sphere detection,MAP detection
Wireless,Computer science,Massively parallel,Parallel computing,MIMO,Real-time computing,General-purpose computing on graphics processing units,Throughput,Detector,QR decomposition,Computational complexity theory
Journal
Volume
Issue
ISSN
76
2
1939-8018
Citations 
PageRank 
References 
2
0.53
16
Authors
5
Name
Order
Citations
PageRank
Michael Wu127118.30
Bei Yin221214.61
Guohui Wang3108860.78
Christoph Studer4109785.83
Joseph R. Cavallaro51175115.35