Electrical and Computer Engineering Annual Report 2018

Welcome New President and Dean Faculty Directory Faculty Highlights Exciting Challenges for Expert Teacher Mobility Research: Sumit Paudyal Mobility Research: Chee-Wooi Ten Mobility Research: Bruce Mork Faculty Profile and Department Award Faculty Publications Staff Profile and Directory Graduate Student Awards and Degrees Student Highlights Senior Design Enterprise Enterprise and Undergraduate Student Awards ECE Academy Class of 2018 External Advisory Committee Contracts and Grants Department Statisticshttps://digitalcommons.mtu.edu/ece-annualreports/1000/thumbnail.jp

Intelligent and Secure Underwater Acoustic Communication Networks

Underwater acoustic (UWA) communication networks are promising techniques for medium- to long-range wireless information transfer in aquatic applications. The harsh and dynamic water environment poses grand challenges to the design of UWA networks. This dissertation leverages the advances in machine learning and signal processing to develop intelligent and secure UWA communication networks. Three research topics are studied: 1) reinforcement learning (RL)-based adaptive transmission in UWA channels; 2) reinforcement learning-based adaptive trajectory planning for autonomous underwater vehicles (AUVs) in under-ice environments; 3) signal alignment to secure underwater coordinated multipoint (CoMP) transmissions. First, a RL-based algorithm is developed for adaptive transmission in long-term operating UWA point-to-point communication systems. The UWA channel dynamics are learned and exploited to trade off energy consumption with information delivery latency. The adaptive transmission problem is formulated as a partially observable Markov decision process (POMDP) which is solved by a Monte Carlo sampling-based approach, and an expectation-maximization-type of algorithm is developed to recursively estimate the channel model parameters. The experimental data processing reveals that the proposed algorithm achieves a good balance between energy efficiency and information delivery latency. Secondly, an online learning-based algorithm is developed for adaptive trajectory planning of multiple AUVs in under-ice environments to reconstruct a water parameter field of interest. The field knowledge is learned online to guide the trajectories of AUVs for collection of informative water parameter samples in the near future. The trajectory planning problem is formulated as a Markov decision process (MDP) which is solved by an actor-critic algorithm, where the field knowledge is estimated online using the Gaussian process regression. The simulation results show that the proposed algorithm achieves the performance close to a benchmark method that assumes perfect field knowledge. Thirdly, the dissertation presents a signal alignment method to secure underwater CoMP transmissions of geographically distributed antenna elements (DAEs) against eavesdropping. Exploiting the low sound speed in water and the spatial diversity of DAEs, the signal alignment method is developed such that useful signals will collide at the eavesdropper while stay collision-free at the legitimate user. The signal alignment mechanism is formulated as a mixed integer and nonlinear optimization problem which is solved through a combination of the simulated annealing method and the linear programming. Taking the orthogonal frequency-division multiplexing (OFDM) as the modulation technique, simulation and emulated experimental results demonstrate that the proposed method significantly degrades the eavesdropper\u27s interception capability

Reinforcement learning-based adaptive transmission in time-varying underwater acoustic channels

This paper studies adaptive transmission in an underwater acoustic (UWA) point-to-point communication system that operates on an epoch-by-epoch basis for a long term. A fixed amount of information bits periodically arrive at the transmitter data queue, and wait for transmission via a number of packets within each epoch. To trade off energy consumption with transmission latency, the transmitter decides the transmission action at the beginning of each epoch, including to transmit or not, and the transmission power and the modulation-and-coding parameters, based on the data queue status and the predicted channel conditions in the current and future epochs. To describe both the fast fading and the large-scale shadowing of UWA channels, the channel within each epoch is characterized by a compound Nakagamilognormal distribution, and the evolution of the distribution parameters is modeled as an unknown Markov process. Given that the channel can only be observed during active transmissions, we formulate the adaptive transmission problem as a partially observable Markov decision process, and develop an online algorithm in a model-based reinforcement learning framework. The algorithm recursively estimates the channel model parameters, tracks the channel dynamics, and computes the optimal transmission action that minimizes a long-term system cost. Emulated results based on channel measurements from two-field experiments demonstrate that the proposed algorithm achieves decent performance relative to a benchmark method that assumes perfect and non-causal channel knowledge