DocumentCode :
635562
Title :
Tuning an underwater communication link
Author :
Shankar, Subramaniam ; Chitre, Mandar
Author_Institution :
Acoust. Res. Lab., Nat. Univ. of Singapore, Singapore, Singapore
fYear :
2013
fDate :
10-14 June 2013
Firstpage :
1
Lastpage :
9
Abstract :
We present machine learning algorithms to tune an underwater communication link. The link tuner is characterized by 3 features: a) It is data driven, rather than physics driven. Hence, it only needs bit error rate information as input and is independent of the modem implementation, b) The tuner balances exploration of the search space against exploitation of existing knowledge, and c) It optimizes for the average data rate, instead of searching for maximum possible data rate. We implement the link tuner on the UNET-II modem and present results from simulations, water tank tests and field trials. The results demonstrate a significant improvement in average data rate as compared to the average data rate attained without tuning.
Keywords :
learning (artificial intelligence); modems; telecommunication computing; underwater acoustic communication; UNET-II modem; field trials; link tuner; machine learning algorithm; underwater communication link tuning; water tank tests; Bit error rate; Encoding; Forward error correction; Modems; Noise measurement; Tuners;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
OCEANS - Bergen, 2013 MTS/IEEE
Conference_Location :
Bergen
Print_ISBN :
978-1-4799-0000-8
Type :
conf
DOI :
10.1109/OCEANS-Bergen.2013.6607956
Filename :
6607956
Link To Document :
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