UAV-Enabled Wireless-Powered Two-Way Communications Under Probabilistic LoS Channelsopen access
- Authors
- Park, Gitae; Jang, Gihyeon; Lee, Woongsup; Lee, Kisong
- Issue Date
- Jan-2026
- Publisher
- IEEE
- Keywords
- convex optimization; probabilistic LoS channel; resource allocation; trajectory design; Unmanned aerial vehicle; wireless-powered two-way communications
- Citation
- IEEE Internet of Things Journal, v.13, no.2, pp 3188 - 3199
- Pages
- 12
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE Internet of Things Journal
- Volume
- 13
- Number
- 2
- Start Page
- 3188
- End Page
- 3199
- URI
- https://scholarworks.dongguk.edu/handle/sw.dongguk/62161
- DOI
- 10.1109/JIOT.2025.3631828
- ISSN
- 2372-2541
2327-4662
- Abstract
- This study investigates the joint optimization of trajectory and resource allocation in unmanned aerial vehicle (UAV)-enabled wireless-powered two-way communication (WPTWC) under probabilistic line-of-sight (LoS) channel models. In this communication protocol, the UAV transmits signals over wireless links while the ground nodes (GNs) simultaneously receive information and harvest energy based on either a power splitting (PS) or time switching (TS) policy. Each GN then uses the harvested energy to transmit collected data back to the UAV. Due to the nature of downlink broadcast transmission in UAV-enabled WPTWC, where GNs at different locations receive signals simultaneously, both LoS and non-LoS components significantly influence the channel characteristics. To capture these location-dependent effects accurately, we reformulate both components into equivalent convex forms, which enhances analytical tractability. Subsequently, we jointly optimize the time allocation, the three-dimensional UAV trajectory, the transmit powers of the UAV and GNs, and the energy harvesting ratio of the GNs to maximize the minimum uplink spectral efficiency (SE) of the GNs while satisfying the downlink SE requirements for all GNs. To tackle the nonconvexity of the optimization problem, we first decompose it into four subproblems and then transform each subproblem into a convex problem with respect to its corresponding optimization variable via successive convex approximation and advanced optimization methods. Thereafter, we propose a low-complexity algorithm that employs the block coordinate descent method to iteratively find the optimal solution for each convex subproblem. Simulations reveal distinct trajectory and resource allocation behaviors under the PS and TS policies, influenced by the downlink SE requirement. Furthermore, by adaptively optimizing the UAV trajectory and radio resource allocation based on the network conditions, the proposed scheme significantly improves the minimum uplink SE of the GNs relative to baseline schemes. © 2025 Elsevier B.V., All rights reserved.
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