Paper Info
Title
A fast and energy-efficient Hamming decoder for software-defined radio using graphics processing units
Abstract
The demand for scalable and fast error decoders has recently increased in software-defined radio-based communication systems. Hamming code, which is one of the promising error decoders, shows acceptable accuracy; however, the computational complexity of the decoder limits its use in real-time communication. To address this issue, this paper proposes a fully parallel implementation of the (7, 4) Hamming code on a graphics processing unit (GPU) by exploiting massive data-parallelism and increasing on-chip constant memory accesses. To further improve the performance of this proposed parallel approach, this paper explores the impact of different thread/block configurations and selects optimal thread/block configurations, which can occupy more hardware resources for performing parity checks, error detection and correction, and decoding of the received codeword. In addition, the proposed GPU-based Hamming decoder can provide significant scalability by supporting different message sizes, including 355,907 bytes, 2,959,475 bytes, and 12,835,890 bytes. To verify the effectiveness of the GPU-based parallel Hamming decoder, this paper compares its performance with that of the multi-threading central processing unit (CPU) approach which is executed on an Intel multi-core processor. Experimental results indicate that the proposed GPU-based decoder operates at least 15.13 times faster and reduces the energy consumption by up to 913.17 % compared to the multi-threading CPU-based approach.
Year
DOI
Venue
2015
10.1007/s11227-015-1396-x
The Journal of Supercomputing
Keywords
Field
DocType
Software-defined radio,Hamming code,Error coding,Graphics processing units,High-speed and real-time communication
Hamming code,Forward error correction,Constant-weight code,Computer science,Parallel computing,Error detection and correction,Soft-decision decoder,Hamming bound,Decoding methods,Graphics processing unit,Distributed computing
Journal
Volume
Issue
ISSN
71
7
0920-8542
Citations 
PageRank 
References 
0
0.34
26
Authors
5
Name
Order
Citations
PageRank
Jaeyoung Kim1777.21
Myeongsu Kang210712.81
Md Shohidul Islam392.98
Cheol Hong Kim47324.39
Jong-Myon Kim59125.99