Trajectory tracking control of autonomous vehicle with random network delay

Z Luan, J Zhang, W Zhao… - IEEE transactions on …, 2020 - ieeexplore.ieee.org
… network bandwidth limitations in practice, the increasingcontrol of networked control
systems with communication … of random network delay on the system performance is positively …

Deep reinforcement learning based dynamic trajectory control for UAV-assisted mobile edge computing

L Wang, K Wang, C Pan, W Xu, N Aslam… - IEEE Transactions …, 2021 - ieeexplore.ieee.org
… Experience Replay (PER) to improve the convergence of the … Then, we show the
convergence performance of RAT in … Zhang, “Joint trajectory and communication design for multi-…

Deep -Learning-Based Node Positioning for Throughput-Optimal Communications in Dynamic UAV Swarm Network

AM Koushik, F Hu, S Kumar - … on Cognitive Communications …, 2019 - ieeexplore.ieee.org
… to find an optimal position within the link to improve the communication performance. … the
control node in higher layer. To meet the delay constraints of each packet over the multihop path

Joint optimization of vehicle trajectories and intersection controllers with connected automated vehicles: Combined dynamic programming and shooting heuristic …

Y Guo, J Ma, C Xiong, X Li, F Zhou, W Hao - Transportation research part C …, 2019 - Elsevier
performance (eg, minimum delay and maximum throughput). … trajectory control, there are still
a few aspects to be improved, … a reaction time τ (or a communication delay) ago. We call the …

Deep reinforcement learning based latency minimization for mobile edge computing with virtualization in maritime UAV communication network

Y Liu, J Yan, X Zhao - IEEE Transactions on Vehicular …, 2022 - ieeexplore.ieee.org
latency requirements of maritime users by optimal trajectoryimprove the latency performance.
However, it is obvious that DDPG algorithm has better latency performance improvement

Fundamental trade-offs in communication and trajectory design for UAV-enabled wireless network

Q Wu, L Liu, R Zhang - IEEE Wireless Communications, 2019 - ieeexplore.ieee.org
… UAV trajectory and communication design to characterize the throughput-delay trade-off. …
be jointly designed with the UAV trajectory to further improve the throughput-delay trade-off and …

Peekaboo: Learning-based multipath scheduling for dynamic heterogeneous environments

H Wu, Ö Alay, A Brunstrom, S Ferlin… - … in Communications, 2020 - ieeexplore.ieee.org
bandwidth and RTT delay characteristics of the paths to quantify their heterogeneity. While
Path 1 mimics a slower link with 2Mbps bandwidth … cannot significantly improve performance. …

Optimizing computation offloading in satellite-UAV-served 6G IoT: A deep learning approach

B Mao, F Tang, Y Kawamoto, N Kato - Ieee Network, 2021 - ieeexplore.ieee.org
… However, the large transmission latency, serious path loss, … service requirements for throughput
and latency in the 6G era. To … For UAV communications, trajectory control is an important …

An adaptive traffic routing approach toward load balancing and congestion control in Cloud–MANET ad hoc networks

S Dalal, B Seth, V Jaglan, M Malik, Surbhi, N Dahiya… - Soft Computing, 2022 - Springer
… the role of the Cloud–MANET framework for communication … a multiple-path congestion
control algorithm was proposed in … algorithms in terms of achieved throughput, end-to-end delay, …

Experience-driven congestion control: When multi-path TCP meets deep reinforcement learning

Z Xu, J Tang, C Yin, Y Wang… - … Areas in Communications, 2019 - ieeexplore.ieee.org
… and 4G/LTE) to improve end-to-end bandwidth and robustness, and has … controlled some
key parameters of the communication links in the test environment, such as delay, bandwidth