@article {2021,
	title = {Underwater Acoustic Communication Receiver Using Deep Belief Network},
	journal = {IEEE Transactions on Communications},
	year = {2021},
	pages = {1-1},
	abstract = {Underwater environments create a challenging channel for communications. In this paper, we design a novel receiver system by exploring the machine learning technique{\textendash}Deep Belief Network (DBN){\textendash} to combat the signal distortion caused by the Doppler effect and multi-path propagation. We evaluate the performance of the proposed receiver system in both simulation experiments and sea trials. Our proposed receiver system comprises of DBN based de-noising and classification of the received signal. First, the received signal is segmented into frames before the each of these frames is individually pre-processed using a novel pixelization algorithm. Then, using the DBN based de-noising algorithm, features are extracted from these frames and used to reconstruct the received signal. Finally, DBN based classification of the reconstructed signal occurs. Our proposed DBN based receiver system does show better performance in channels influenced by the Doppler effect and multi-path propagation with a performance improvement of 13.2dB at 10-3 Bit Error Rate (BER).},
	doi = {10.1109/TCOMM.2021.3063353},
	url = {https://ieeexplore.ieee.org/document/9367188},
	author = {A. Lee-Leon and C. Yuen and D. Herremans}
}
@conference {2019,
	title = {Doppler Invariant Demodulation for Shallow Water Acoustic Communications Using Deep Belief Networks},
	booktitle = {16th IEEE Asia Pacific Wireless Communications Symposium (APWCS)},
	year = {2019},
	abstract = {Shallow water environments create a challenging channel for communications. In this paper, we focus on the challenges posed by the frequency-selective signal distortion called the Doppler effect. We explore the design and performance of machine learning (ML) based demodulation methods {\textemdash} (1) Deep Belief Network-feed forward Neural Network (DBN-NN) and (2) Deep Belief Network-Convolutional Neural Network (DBN-CNN) in the physical layer of Shallow Water Acoustic Communication (SWAC). The proposed method comprises of a ML based feature extraction method and classification technique. First, the feature extraction converts the received signals to feature images. Next, the classification model correlates the images to a corresponding binary representative. An analysis of the ML based proposed demodulation shows that despite the presence of instantaneous frequencies, the performance of the algorithm shows an invariance with a small 2dB error margin in terms of bit error rate (BER).},
	author = {A. Lee-Leon and C. Yuen and D. Herremans}
}
@conference {2019,
	title = {A Hybrid Fuzzy Logic-Neural Network Approach For Multi-path Separation Of Underwater Acoustic Signals},
	booktitle = {89th IEEE Vehicular Technology Conference},
	year = {2019},
	month = {04/2019},
	address = {Kuala Lumpur, Malaysia},
	abstract = {Abstract{\textemdash}Underwater acoustic channels are generally recognized as one of the most difficult communication media in use today. One of the most important constraints of underwater communications is the acoustic propagation of the signals. The aim of a multi-path separator is to extract individual correlated signals from their mixtures. We present an algorithm, Tag Receiver, that is similar to the RAKE receiver, but interpreted from the neural network viewpoint. First, the received signal is split into frames. Next, the frames are {\textquotedblleft}tagged{\textquotedblright} with a predicted number of multi-path in the framed segment via fuzzy logic and feature extraction. Finally, the frame and {\textquotedblleft}tag{\textquotedblright} number are inputted into a neural network, which separates the signals. The analysis of the proposed algorithm shows an improvement from the conventional RAKE receiver in terms of BER.},
	doi = {10.1109/VTCSpring.2019.8746614},
	url = {https://ieeexplore.ieee.org/abstract/document/8746614},
	author = {A. Lee-Leon and C. Yuen and D. Herremans}
}
