Abstract
An underwater acoustic communication channel is time varying and has multipath propagation with bottom and surface reflection. A rake receiver has time diversity effects in multipath propagation environments. We have to recognize the correct path for a rake receiver, as this affects communication performance in the underwater acoustic channel. In this paper, we propose a more reliable rake receiver based on BER (bit error rate) of training sequence duration. We conducted simulations and lake trials to evaluate the performance of the proposed method. When the channel coding technique was not applied, the uncoded BER performances of the proposed method, the conventional method, and the nonrake method in the lake trial were 0.016, 0.088, and 0.141, respectively.
Highlights
Underwater sensor networks (USNs) can be used for environment monitoring, disaster prevention, and military surveillance [1,2,3,4,5]
When sound waves are passed through the underwater, they are affected by attenuation, ambient noise, and multipath propagation effect including reflection of bottom and surface
For the purpose of improving reliability in an underwater acoustic channel, rake receiver has been used for time diversity, which utilizes multipath propagation
Summary
Underwater sensor networks (USNs) can be used for environment monitoring, disaster prevention, and military surveillance [1,2,3,4,5]. If the received signal is passed through the matched filters, the energy peaks would emerge in accordance with the number of paths In this method, the weighting values of each path are dependent on the path gain. The matched filter output peak of the path is so high that the synchronization point is incorrect in underwater acoustic channels. To prevent the assignment of a high weighting value to incorrect path symbols, the method that uses the BER of training sequence duration was applied. The proposed rake receiver assigns weighting value in which low BER of training sequence constitutes a high weighting value Since it compensates channel environments of each path using training sequence, better BER performance than previous method can be derived.
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