Subcarrier modulation identification of underwater acoustic OFDM based on block expectation maximization and likelihood
Introduction
OFDM has been widely implemented in underwater acoustic communication systems because of its high spectral efficiency and good performance in combating interference, and has become the prominent technology for high-speed underwater acoustic communication systems [1], [2], [3], [4]. Further, research on Non-Cooperative underwater acoustic OFDM has become a hotspot as well [5], [6]. In addition, subcarrier modulation identification provide important basis for the multiuser and the jamming technology [7], [8], especially in the military application. Lots of research on the identification of non-cooperative underwater acoustic communication has been carried out in the literature. However, the identification of OFDM is mainly focused on the identification of single carrier and multi carrier, and the identification of subcarrier modulation is given less importance.
Various authors have discussed the subcarrier modulation identification of OFDM in the literature and have proposed many schemes, however, most of the application scenarios are focused on the radio. For instance, in [9], the maximum likelihood method is used to identify the subcarrier modulation in the Gaussian channel, but this method is not suitable for the multipath channel. In [10], maximum a posteriori probability (MAP) combined with maximum likelihood is used to identify subcarrier modulation in the fading channel, however, its performance is poor at low SNR. In [11], a high-order cumulant combined with Bayesian is proposed to identify subcarrier modulation, but this method can be identified only when the probability density function (PDF) of the transmitted signal is known, and hence limits its performance in practical use. Although significant results are achieved by the above mentioned schemes, however, still research work is needed to validate these schemes practically. In the aspect of modulation identification in MPSK and multiple quadrature amplitude modulation(MQAM), the identification method based on likelihood is very popular, and shows significant performance when the CIR and noise power are known [12]. However, this identification method draws researcher’s attention when the CIR and noise power are unknown in the multipath fading channel.In [13], [14], EM-QHLRT is proposed for BPSK and QPSK signals. The CIR and noise power are estimated by EM, the QHLRT is used to calculate the likelihood value, and the modulation is identified by comparing the likelihood value. EM-QHLRT effectively improved the identification performance and provided a new idea of modulation identification in fading channel. However, the performance of the channel estimation based on EM is severely affected by the multipath channel.
Combining the characteristics of underwater acoustic OFDM, we propose a novel method of subcarrier modulation identification based on EM-Block-QHLRT, and provide the following contributions in this regard.
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A novel method of subcarrier modulation identification based on QHLRT is proposed when the CIR and noise power are unknown. After removing cyclic prefix and FFT, the received signal is modeled as Gaussian mixture models (GMM), and the EM-QHLRT is used to identify subcarrier modulation;
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We proposed the channel estimation strategy of EM-Block to improve the reliability of channel estimation. It is because of the fact that in the multi-carrier of OFDM system, the channels experienced by different subcarriers are different, while the CIR in every subcarrier calculated by EM is the same;
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We performed simulation analyzing the influence of iteration times, the length of symbols and the number of blocks of the proposed EM-Block-QHLRT method, and then compared the performance of ALRT-UB, EM-QHLRT and EM-Block-QHLRT;
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We also validated the proposed EM-Block-QHLRT method by using sea trial data.
Section snippets
System model
The difference between underwater acoustic OFDM and single carrier signal indicates that the N-point IFFT transform at transmitter divides the underwater acoustic channel into N parallel subcarrier channels. The OFDM signal generated by the N-point IFFT transform at transmitter can be expressed as:
Where is the subcarrier modulation signal of the OFDM symbol mapped by subcarrier modulation, is the data on the subcarrier of the OFDM symbol. Hence,
Parameter setting
In order to verify the validity of the proposed EM-Block-QHLRT method, an underwater acoustic sparse channel is generated for simulation, using the method used in [20] for channel generation. We set the number of paths to 7, and the time delay of adjacent paths follows the exponential distribution using the mean value of 3 ms and the average multipath delay of 21 ms, while the amplitudes are Rayleigh distributed with the average power decreasing exponentially with delay. The noise included in
Experimental results
In this section, we provide the experimental work in Hainan Lingshui, verifying practically the validity of our proposed EM-Block-QHLRT method. In the experiment, we hanged underwater acoustic transducer and hydrophone with two ships. One ship was used as the target node receiving the signal, and the other ship was used as the mobile node transmitting the signals at different distances. The water depth at the experimental site was 60–70 m, and the sea condition was well, however, there was a
Conclusion
A novel EM-Block-QHLRT subcarrier modulation identification method is proposed for the underwater acoustic multipath channel, using the characteristics of multicarrier OFDM, the performance of blind channel estimation is greatly improved. The simulation results showed that the proposed EM-Block-QHLRT method showed better performance than EM-QHLRT method, in terms of improved accuracy. The identification of underwater acoustic OFDM using QPSK subcarrier modulation is verified by sea trial data.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This work was supported by the National Key R&D Program of China (Grant Nos. 2018YFC0308500), National Natural Science Foundation of China (Grant Nos. 61771152, 11974090, 11774074 and 11704090), the Natural Science Foundation of Heilongjiang Province of China (Grant No. YQ2019F002). The authors wish to thank all participants of the research.
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