Abstract
This paper is aimed to study the characteristics of the underwater acoustic channel with non-Gaussian noise channel. And Gaussian mixture model (GMM) is utilized to fit the background noise over the non-Gaussian noise channel. Furthermore, coding techniques which use a sequence of rate-compatible low-density parity-check (RC-LDPC) convolutional codes with separate rates are constructed based on graph extension method. The performance study of RC-LDPC convolutional codes over non-Gaussian noise channel and the additive white Gaussian noise (AWGN) channel is performed. Study implementation of simulation is that modulation with binary phase shift keying (BPSK), and iterative decoding based on pipeline log-likelihood rate belief propagation (LLRBP) algorithm. Finally, it is shown that RC-LDPC convolutional codes have good bit-rate-error (BER) performance and can effectively reduce the impact of noise.
Highlights
Non-Gaussian noise is a major impairment in many wireless communication
Underwater acoustic channel is characterized as the non-Gaussian noise channel
Study implementation of simulation is acquired by ratecompatible low-density parity-check (RC-low-density parity-check (LDPC)) convolutional code family over communication system with binary phase shift keying (BPSK) and iterative decoding by likelihood rate belief propagation (LLRBP) algorithm based on the Tanner graph
Summary
Non-Gaussian noise is a major impairment in many wireless communication. In signal processing, background noise is generally regarded as additive white Gaussian noise (AWGN) because of its additivity, amplitude obeying Gaussian distribution, being a kind of white noise and easy to analyze and approximate. More non-Gaussian noise models are worth evaluating the quality of underwater acoustic communication under realistic environmental conditions. Such models are being developed [4,5,6]. We use the constructed rate-compatible code family of regular low-density parity-check (LDPC) convolutional codes via graph extension method [14], which overcomes the drawbacks caused by puncturing and simplifies the optimization. We believe that using RC-LDPC convolutional codes constructed by graphic extension method to improve the quality of nonGaussian noise channel communication is a very effective method. The RC-LDPC convolutional code based on graph extension method is employed for non-Gaussian noise channel.
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