Self-Supervised Path Planning in UAV-aided Wireless Networks based on Active Inference
Abstract: This paper presents a novel self-supervised path-planning method for UAV-aided networks. First, we employed an optimizer to solve training examples offline and then used the resulting solutions as demonstrations from which the UAV can learn the world model to understand the environment and implicitly discover the optimizer's policy. UAV equipped with the world model can make real-time autonomous decisions and engage in online planning using active inference. During planning, UAV can score different policies based on the expected surprise, allowing it to choose among alternative futures. Additionally, UAV can anticipate the outcomes of its actions using the world model and assess the expected surprise in a self-supervised manner. Our method enables quicker adaptation to new situations and better performance than traditional RL, leading to broader generalizability.
- “UAV Communications for 5G and Beyond: Recent Advances and Future Trends,” IEEE Internet of Things Journal, vol. 6, no. 2, pp. 2241–2263, April 2019.
- “Joint Power and Trajectory Design for Physical-Layer Secrecy in the UAV-Aided Mobile Relaying System,” IEEE Access, vol. 6, pp. 62849–62855, 2018.
- “Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective,” IEEE Transactions on Communications, vol. 67, no. 3, pp. 2580–2604, March 2019.
- “UAV-Assisted Wireless Networks for Stringent Applications: Resource Allocation and Positioning,” in 2023 IEEE Wireless Communications and Networking Conference (WCNC), March 2023, pp. 1–6.
- “Trajectory Design for UAV-Enabled Multiuser Wireless Power Transfer With Nonlinear Energy Harvesting,” IEEE Transactions on Wireless Communications, vol. 20, no. 2, pp. 1105–1121, Feb 2021.
- “Deep Reinforcement Learning Based Resource Allocation and Trajectory Planning in Integrated Sensing and Communications UAV Network,” IEEE Transactions on Wireless Communications, pp. 1–1, 2023.
- “An Emergent Self-Awareness Module for Physical Layer Security in Cognitive UAV Radios,” IEEE Transactions on Cognitive Communications and Networking, vol. 8, no. 2, pp. 888–906, June 2022.
- “Trajectory Design and Generalization for UAV Enabled Networks:A Deep Reinforcement Learning Approach,” in 2020 IEEE Wireless Communications and Networking Conference (WCNC), May 2020, pp. 1–6.
- “Active Inference: A Process Theory,” Neural Computation, vol. 29, no. 1, pp. 1–49, 01 2017.
- Karl Friston, “Active inference and free energy,” Behavioral and Brain Sciences, vol. 36, no. 3, pp. 212–213, 2013.
- “A Goal-Directed Trajectory Planning Using Active Inference in UAV-Assisted Wireless Networks,” Sensors, vol. 23, no. 15, 2023.
- “Wireless Communication Using Unmanned Aerial Vehicles (UAVs): Optimal Transport Theory for Hover Time Optimization,” IEEE Transactions on Wireless Communications, vol. 16, no. 12, pp. 8052–8066, Dec 2017.
- “Traveling Salesman Problems with Profits,” Transportation Science, vol. 39, no. 2, pp. 188–205, 2005.
- “Worst Case and Probabilistic Analysis of the 2-Opt Algorithm for the TSP,” Algorithmica, vol. 68, pp. 190–264, 2007.
- Vladimir I. Levenshtein, “Binary codes capable of correcting deletions, insertions, and reversals,” Soviet physics. Doklady, vol. 10, pp. 707–710, 1965.
- “Technical Note: Q-Learning,” Machine Learning, vol. 8, pp. 279–292, 05 1992.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.