Author : Qiang Fu
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)
Book Synopsis Reinforcement Learning-Based Mobile Underwater Acoustic Communications by : Qiang Fu
Download or read book Reinforcement Learning-Based Mobile Underwater Acoustic Communications written by Qiang Fu and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Underwater acoustic communication technologies are becoming increasingly important due to the widespread adoption of autonomous unmanned vehicles (AUVs) in ocean data collection. The communication support provided by acoustic telemetry makes it possible for multiple AUVs to coordinate during underwater missions. Mobile underwater acoustic communications is still an active field of research. Various technical issues, such as reliable communications between two mobile nodes, multi-user communications, and joint optimization of navigation and communications are awaiting for satisfactory solutions. In this thesis, we develop solutions for several of these issues. The first effort considers an adaptive communication system based on time-reversed orthogonal frequency-division multiplexing methods for the underwater acoustic channels. In this adaptive system, the receiver sends truncated q-functions to the transmitter, which then performs mapping selection for the individual sub-carriers. Simulations demonstrate the advantages of the proposed adaptive system, achieving higher data rates and lower feedback costs. The second effort addresses the extended propagation delay in closed-loop adaptive communications for mobile platforms. An adaptive modulation strategy is developed based on reinforcement learning. Specifically, a Dyna-Q algorithm is presented to improve the communication throughput. Our simulations show that the Dyna-Q algorithm achieves a higher throughput and lower bit-error-rates than the direct feedback. The third effort provides a solution to the acoustic communication problem with multiple AUVs. We propose a virtual multiple-input/multiple-output (MIMO) strategy that selects transmitters from a subset of AUVs to form a virtual transmit array. A user selection algorithm is used to determine the active AUV subset for data transmissions. Adaptive modulation is combined to further improve throughput. The user selection and modulation choice are determined by the predicted data rates, thus increasing spectral efficiency of the uplink. In the last effort of this thesis, we address the trajectory optimization for underwater data muling with mobile nodes. In this scenario, multiple AUVs sample a mission area and autonomous surface vehicles (ASVs) visit underway AUVs to retrieve survey data. We propose a nearest-K reinforcement learning algorithm to optimize ASV travel tracks. The learning-based algorithm can simultaneously maximize fairness in data transmissions and minimize the travel distance of the surface nodes.