Semantic RF Waveform Adaptation in Flying Ad Hoc Networks via Hybrid Knowledge Bases and Deep Reinforcement Learning
By directly encoding the semantic meaning into each RF waveform symbol, we can significantly reduce the communication overhead. Particularly, semantic communication in Flying Ad Hoc Networks (FANETs)—formed by uncrewed aerial vehicles (UAVs)—is a promising application for enabl...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11021564/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849332580775624704 |
|---|---|
| author | Weiqiang Lyu Linsheng He Jiamiao Zhao Fei Hu |
| author_facet | Weiqiang Lyu Linsheng He Jiamiao Zhao Fei Hu |
| author_sort | Weiqiang Lyu |
| collection | DOAJ |
| description | By directly encoding the semantic meaning into each RF waveform symbol, we can significantly reduce the communication overhead. Particularly, semantic communication in Flying Ad Hoc Networks (FANETs)—formed by uncrewed aerial vehicles (UAVs)—is a promising application for enabling efficient airborne networks. This paper introduces a semantics-aware approach to optimize OFDM (Orthogonal Frequency Division Multiplexing) waveforms, leveraging Deep Q-Network (DQN)-based reinforcement learning (RL) with nonlinear optimization, enhanced by an innovative hybrid knowledge base shared by all nodes. Our method incorporates semantic importance through adaptive parameter tuning and constrained optimization. The hybrid knowledge base allows for efficient representation and utilization of semantic information. We propose a framework that jointly optimizes modulation order and cyclic prefix length while considering the semantic importance of transmitted data. The system is formulated as a Markov Decision Process (MDP) with scalable state/action spaces. This algorithm enhances the system’s ability to handle diverse and dynamic communication scenarios, adapting to varying semantic importance levels. |
| format | Article |
| id | doaj-art-175d8f8aa2ad4f8ab4ea97bd00e69571 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-175d8f8aa2ad4f8ab4ea97bd00e695712025-08-20T03:46:09ZengIEEEIEEE Access2169-35362025-01-0113981909820710.1109/ACCESS.2025.357614611021564Semantic RF Waveform Adaptation in Flying Ad Hoc Networks via Hybrid Knowledge Bases and Deep Reinforcement LearningWeiqiang Lyu0https://orcid.org/0009-0002-7187-4592Linsheng He1https://orcid.org/0000-0001-9067-5121Jiamiao Zhao2https://orcid.org/0000-0002-3670-219XFei Hu3https://orcid.org/0000-0003-4346-9477Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL, USADepartment of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL, USADepartment of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL, USADepartment of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL, USABy directly encoding the semantic meaning into each RF waveform symbol, we can significantly reduce the communication overhead. Particularly, semantic communication in Flying Ad Hoc Networks (FANETs)—formed by uncrewed aerial vehicles (UAVs)—is a promising application for enabling efficient airborne networks. This paper introduces a semantics-aware approach to optimize OFDM (Orthogonal Frequency Division Multiplexing) waveforms, leveraging Deep Q-Network (DQN)-based reinforcement learning (RL) with nonlinear optimization, enhanced by an innovative hybrid knowledge base shared by all nodes. Our method incorporates semantic importance through adaptive parameter tuning and constrained optimization. The hybrid knowledge base allows for efficient representation and utilization of semantic information. We propose a framework that jointly optimizes modulation order and cyclic prefix length while considering the semantic importance of transmitted data. The system is formulated as a Markov Decision Process (MDP) with scalable state/action spaces. This algorithm enhances the system’s ability to handle diverse and dynamic communication scenarios, adapting to varying semantic importance levels.https://ieeexplore.ieee.org/document/11021564/Cyclic prefixdeep reinforcement learninghybrid knowledge basemodulationOFDMsemantic communications |
| spellingShingle | Weiqiang Lyu Linsheng He Jiamiao Zhao Fei Hu Semantic RF Waveform Adaptation in Flying Ad Hoc Networks via Hybrid Knowledge Bases and Deep Reinforcement Learning IEEE Access Cyclic prefix deep reinforcement learning hybrid knowledge base modulation OFDM semantic communications |
| title | Semantic RF Waveform Adaptation in Flying Ad Hoc Networks via Hybrid Knowledge Bases and Deep Reinforcement Learning |
| title_full | Semantic RF Waveform Adaptation in Flying Ad Hoc Networks via Hybrid Knowledge Bases and Deep Reinforcement Learning |
| title_fullStr | Semantic RF Waveform Adaptation in Flying Ad Hoc Networks via Hybrid Knowledge Bases and Deep Reinforcement Learning |
| title_full_unstemmed | Semantic RF Waveform Adaptation in Flying Ad Hoc Networks via Hybrid Knowledge Bases and Deep Reinforcement Learning |
| title_short | Semantic RF Waveform Adaptation in Flying Ad Hoc Networks via Hybrid Knowledge Bases and Deep Reinforcement Learning |
| title_sort | semantic rf waveform adaptation in flying ad hoc networks via hybrid knowledge bases and deep reinforcement learning |
| topic | Cyclic prefix deep reinforcement learning hybrid knowledge base modulation OFDM semantic communications |
| url | https://ieeexplore.ieee.org/document/11021564/ |
| work_keys_str_mv | AT weiqianglyu semanticrfwaveformadaptationinflyingadhocnetworksviahybridknowledgebasesanddeepreinforcementlearning AT linshenghe semanticrfwaveformadaptationinflyingadhocnetworksviahybridknowledgebasesanddeepreinforcementlearning AT jiamiaozhao semanticrfwaveformadaptationinflyingadhocnetworksviahybridknowledgebasesanddeepreinforcementlearning AT feihu semanticrfwaveformadaptationinflyingadhocnetworksviahybridknowledgebasesanddeepreinforcementlearning |