Zhou, F;
Ding, R;
Wu, Q;
Ng, DWK;
Wong, KK;
Al-Dhahir, N;
(2024)
A Partially Observable Deep Multi-Agent Active Inference Framework for Resource Allocation in 6G and Beyond Wireless Communications Networks.
In:
Proceedings - IEEE Global Communications Conference, GLOBECOM.
(pp. pp. 2662-2667).
IEEE: Kuala Lumpur, Malaysia.
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Abstract
Resource allocation is of crucial importance in wireless communications. However, it is extremely challenging to design efficient resource allocation schemes for future wireless communication networks since the formulated resource allocation problems are generally non-convex and consist of various coupled variables. Moreover, the dynamic changes of practical wireless communication environment and user service requirements thirst for efficient real-time resource allocation. To tackle these issues, a novel partially observable deep multi-agent active inference (PODMAI) framework is proposed for realizing intelligent resource allocation. A belief based learning method is exploited for updating the policy by minimizing the variational free energy. A decentralized training with a decentralized execution multi-agent strategy is designed to overcome the limitations of the partially observable state information. Exploited the proposed framework, an intelligent spectrum allocation and trajectory optimization scheme is developed for a spectrum sharing unmanned aerial vehicle (UAV) network with dynamic transmission rate requirements as an example. Simulation results demonstrate that our proposed framework can significantly improve the sum transmission rate of the secondary network compared to various benchmark schemes. Moreover, the convergence speed of the proposed PODMAI is significantly improved compared with the conventional reinforcement learning framework. Overall, our proposed framework can enrich the intelligent resource allocation frameworks and pave the way for realizing real-time resource allocation.
| Type: | Proceedings paper |
|---|---|
| Title: | A Partially Observable Deep Multi-Agent Active Inference Framework for Resource Allocation in 6G and Beyond Wireless Communications Networks |
| Event: | GLOBECOM 2023 - 2023 IEEE Global Communications Conference |
| Dates: | 4 Dec 2023 - 8 Dec 2023 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/GLOBECOM54140.2023.10437731 |
| Publisher version: | http://dx.doi.org/10.1109/globecom54140.2023.10437... |
| 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: | Wireless communication , Simulation , Benchmark testing , Autonomous aerial vehicles , Resource management , Vehicle dynamics , 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 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/10189913 |
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