Abstract
Achieving accurate channel estimation and adaptive communications with moving transceivers is challenging due to rapid changes in the underwater acoustic channels. We achieve an accurate channel estimation of fast time-varying underwater acoustic channels by using the superimposed training scheme with a powerful channel estimation algorithm and turbo equalization, where the training sequence and the symbol sequence are linearly superimposed. To realize this, we develop a ‘global’ channel estimation algorithm based on Gaussian likelihood, where the channel correlation between (among) the segments is fully exploited by using the product of the Gaussian probability-density functions of the segments, thereby realizing an ideal channel estimation of each segment. Moreover, the Gaussian-likelihood-based channel estimation is embedded in turbo equalization, where the information exchange between the equalizer and the decoder is carried out in an iterative manner to achieve an accurate channel estimation of each segment. In addition, an adaptive communication algorithm based on constellation aggregation is proposed to resist the severe fast time-varying multipath interference and environmental noise, where the encoding rate is automatically determined for reliable underwater acoustic communications according to the constellation aggregation degree of equalization results. Field experiments with moving transceivers (the communication distance was approximately 5.5 km) were carried out in the Yellow Sea in 2021, and the experimental results verify the effectiveness of the two proposed algorithms.
Highlights
Underwater acoustic communication technology can be widely applied in many fields, such as marine pollution monitoring, underwater rescue, underwater autonomous underwater vehicle (AUV) positioning and navigation
Equalization and decoding for each segment are carried out, where the linear minimum mean square error (LMMSE) equalization can be efficiently implemented with fast Fourier transform (FFT), where the initial a priori logarithm likelihood ratios (LLRs) of the interleaved encoded bits are set to zeros, i.e., La = 0
The corresponding cases are denoted by S128, S256, S512 and W1024, respectively, where the prefix W means that the standard block is treated as a segment
Summary
Underwater acoustic communication technology can be widely applied in many fields, such as marine pollution monitoring, underwater rescue, underwater autonomous underwater vehicle (AUV) positioning and navigation. With a high transmission rate and good anti-frequency-offset characteristics [17–19], the single carrier technology is adopted in this paper It can be used with a variety of encoding rates to realize adaptive underwater acoustic communications with moving transceivers. Different from literatures [29,30], in this paper, the same thought as message passing [29,30] is realized in a ‘novel’ Gaussian product way; in particular, the proposed algorithm is applied in actual underwater acoustic communication machines, and the effective communication distance is extended from 1 km to 5.5 km. GL-based channel estimation, LMMSE equalization and decoding are iteratively performed (turbo equalization), leading to a significant performance improvement of the whole system; The proposed algorithms are applied in actual underwater acoustic communication machines to verify their effectiveness. Throughout the paper, superscripts [·] Tr and [·] H represent transpose and conjugate transpose, respectively
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