Paper Info
Title
A reconfigurable application specific instruction set processor for convolutional and turbo decoding in a SDR environment
Abstract
Future mobile and wireless communication networks require flexible modem architectures to support seamless services between different network standards. Hence, a common hardware platform that can support multiple protocols implemented or controlled by software, generally referred to as software defined radio (SDR), is essential. This paper presents a family of dynamically reconfigurable application-specific instruction-set processors (ASIP) for the application domain of channel coding in wireless communication systems. As a weakly programmable IP core, it can implement trellis based channel decoding in a SDR environment. It features binary convolutional decoding, and turbo decoding for binary as well as duobinary turbo codes for all current and upcoming standards. The ASIPs consist of a specialized pipeline with 15 stages and a dedicated communication and memory infrastructure. Logic synthesis revealed a maximum clock frequency of 400 MHz and a total area of 0.42 mm2 for a 65 nm technology. Simulation results for Viterbi and turbo decoding demonstrate maximum throughput of 196 and 34 Mbps, respectively, and outperforms existing SDR based approaches for channel decoding.
Year
DOI
Venue
2008
10.1145/1403375.1403388
Proceedings of the conference on Design, automation and test in Europe
Keywords
Field
DocType
decoding,protocols,channel coding,turbo code,convolutional codes,application specific instruction set processor,viterbi decoding,application software,logic synthesis,computer architecture,software radio,turbo codes,hardware,software defined radio,wireless communication
Application-specific instruction-set processor,Convolutional code,Computer science,Software-defined radio,Parallel computing,Turbo code,Real-time computing,Viterbi decoder,Decoding methods,Throughput,Viterbi algorithm
Conference
ISSN
Citations 
PageRank 
1530-1591
10
0.73
References 
Authors
11
2
Name
Order
Citations
PageRank
Timo Vogt1778.09
Norbert Wehn21165137.17