Ding, R;
Zhou, F;
Wu, Q;
Ng, DWK;
Wong, KK;
Al-Dhahir, N;
(2025)
A Novel PODMAI Framework Enhanced by User Demand Prediction for Resource Allocation in Spectrum Sharing UAV Networks.
IEEE Transactions on Communications
p. 1.
10.1109/TCOMM.2025.3547722.
(In press).
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Abstract
Spectrum sharing unmanned aerial vehicle (UAV) network is a promising technology for future communication systems to mitigate the spectrum scarcity problem. However, the future sixth-generation large-scale wireless communication networks are expected not only to provide a high data rate for massive numbers of users but also to meet their stringent service requirements. Particularly in dynamic spectrum sharing UAV networks, the coupling of multi-dimensional resources and diverse user demands make the efficient and real-time resource allocation exceptionally challenging. A partially observable deep multi-agent active inference (PODMAI) framework is proposed to tackle these issues. The variational free energy is minimized to update the policy exploiting the belief based learning method. A decentralized training and execution multi-agent strategy is designed to navigate the challenges posed by partially observable information. To further satisfy the dynamic user demand and supplement partial observations, a joint spatial-temporal-attention prediction network is designed to construct the demand prediction enhanced PODMAI framework for resource allocation. Exploiting the established framework, an intelligent spectrum allocation and trajectory optimization scheme is elaborated for a spectrum sharing UAV network with multi-modal dynamic transmission rate demands. Simulation results demonstrate that our proposed scheme outperforms benchmark schemes in terms of the network sum transmission rate. Additionally, our proposed scheme exhibits faster convergence compared to the conventional reinforcement learning. Overall, our proposed framework can enrich intelligent resource allocation frameworks and pave the way for realizing real-time resource allocation.
| Type: | Article |
|---|---|
| Title: | A Novel PODMAI Framework Enhanced by User Demand Prediction for Resource Allocation in Spectrum Sharing UAV Networks |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TCOMM.2025.3547722 |
| Publisher version: | https://doi.org/10.1109/tcomm.2025.3547722 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Deep active inference, demand prediction, spectrum sharing, resource allocation, trajectory optimization |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10207195 |
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