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Optimizing of Iterative Turbo Equalizer for Underwater Sensor Communication
Author(s) -
Ji Won Jung,
Ki Man Kim
Publication year - 2013
Publication title -
international journal of distributed sensor networks
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.324
H-Index - 53
eISSN - 1550-1477
pISSN - 1550-1329
DOI - 10.1155/2013/129781
Subject(s) - turbo equalizer , computer science , turbo code , equalization (audio) , turbo , interleaving , mimo , channel (broadcasting) , algorithm , decoding methods , intersymbol interference , soft decision decoder , underwater acoustic communication , real time computing , concatenated error correction code , telecommunications , underwater , block code , automotive engineering , engineering , operating system , oceanography , geology
We presented an iterative turbo equalization to cope with intersymbol interference induced by reflection of sea level and sea bottom for underwater sensor communication channel. Iterative turbo equalizer consists of inner codes and outer codes; we employ decision feedback equalizer as an outer code and turbo codes as an inner code. Equalizer and decoder are connected through the interleaving and deinterleaving that update each other's information repeatedly. At the receiver side, we resort to powerful turbo equalization algorithms that iteratively exchange probabilistic information between inner decoder and outer decoder, thereby reducing the error rates significantly. Furthermore, we expand iterative turbo equalizer techniques for single-input-single-output (SISO) system to multiple-input-multiple-output (MIMO) system in order to increase data rates for underwater sensor communication channel. Based on experimental channel response, we confirmed that the performance is improved as iteration number is increased. The performance is improved by 3.5 [dB] compared to noniteration for SISO channel and by 1 [dB] for MIMO channel, respectively. We also decided that optimal iterations are 3. Very important for a successful decoding is the channel estimation, which is also discussed.

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