DocumentCode :
1002528
Title :
Sample rejection for efficient simulation of binary coding schemes over quantized additive white Gaussian noise channels
Author :
Loskot, Pavel ; Beaulieu, Norman C.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Alberta, Edmonton, Alta., Canada
Volume :
53
Issue :
7
fYear :
2005
fDate :
7/1/2005 12:00:00 AM
Firstpage :
1145
Lastpage :
1154
Abstract :
We re-examine sample rejection (SR), introduced previously as an easy-to-implement efficient simulation technique. Since the decoding operation often represents a major part of the required simulation time, SR can be used to avoid decoding of the received sequences that are known beforehand to be decoded error-free. Previous work seems to indicate that SR may be effective only for simulations having small dimensionality, less than 10. We assume estimation of decoded bit-error probabilities for a general coding scheme of finite block length transmitted over an additive white Gaussian noise channel with quantized output using binary antipodal signaling and maximum-likelihood sequence decoding. We show that knowledge of the minimum Hamming distance of the code and conditioning on the transmitted sequence can be exploited to form the rejection regions. In particular, we investigate hypersphere, hypercube, and hyperquadrant rejection regions. Our analysis shows that SR can be effective for some systems with dimensionality on the order of hundreds with soft-decision decoding, and some systems with dimensionality more than a thousand with hard-decision decoding if the rejection regions are properly chosen.
Keywords :
AWGN channels; Hamming codes; binary codes; channel coding; error statistics; maximum likelihood decoding; telecommunication signalling; Hamming distance; binary antipodal signaling; binary coding schemes; bit-error probabilities; easy-to-implement efficient simulation technique; finite block length; hard-decision decoding; hypercube rejection region; hyperquadrant rejection region; hypersphere rejection region; maximum-likelihood sequence decoding; quantized additive white Gaussian noise channels; received sequences decoding; sample rejection; soft-decision decoding; AWGN; Additive white noise; Bit error rate; Communication systems; Maximum likelihood decoding; Monte Carlo methods; Signal to noise ratio; Strontium; Viterbi algorithm; Wireless communication; Maximum-likelihood (ML) decoding; Monte Carlo (MC) methods; multidimensional systems; simulation;
fLanguage :
English
Journal_Title :
Communications, IEEE Transactions on
Publisher :
ieee
ISSN :
0090-6778
Type :
jour
DOI :
10.1109/TCOMM.2005.851582
Filename :
1468437
Link To Document :
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