Semantic aware intelligent optimization for IRS/UAV-enabled MEC in wideband cognitive radio networks
Abstract The efficient integration of communication and computation in the internet of things (IoT) presents new opportunities for enhancing system performance but still faces challenges such as interference management, resource allocation and task scheduling. To address these issues, this paper pro...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | EURASIP Journal on Wireless Communications and Networking |
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| Online Access: | https://doi.org/10.1186/s13638-025-02478-5 |
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| author | Wei Zheng Pengshan Ren Qing Li |
| author_facet | Wei Zheng Pengshan Ren Qing Li |
| author_sort | Wei Zheng |
| collection | DOAJ |
| description | Abstract The efficient integration of communication and computation in the internet of things (IoT) presents new opportunities for enhancing system performance but still faces challenges such as interference management, resource allocation and task scheduling. To address these issues, this paper proposes a semantic-aware intelligent optimization framework that combines unmanned aerial vehicles (UAVs) and intelligent reflecting surface (IRS) with mobile edge computing (MEC) to enhance communication quality and semantic awareness in wideband cognitive radio networks. The proposed semantic-aware optimization framework incorporates semantic information to achieve more efficient task scheduling and resource allocation. Particularly, the proposed optimization framework jointly optimizes UAV trajectories, subcarrier allocation, IRS reflection coefficients, task offloading ratios, task priorities and contextual relevance to maximize semantic utility and system energy efficiency while dynamically ensuring task demands. Furthermore, to tackle the non-convexity caused by highly coupled optimization variables, we employ a deep reinforcement learning algorithm based on double deep Q-network and twin delayed deep deterministic policy gradient (DDQN-TD3). Simulation results demonstrate that the proposed approach significantly outperforms baseline schemes by better aligning with user priorities, task requirements, and contextual awareness, leading to improved task completion rates and semantic utility, providing an innovative optimization solution for wideband cognitive radio networks. |
| format | Article |
| id | doaj-art-90caf3af09d34ae4bcdc6ee7c08bd2f0 |
| institution | Kabale University |
| issn | 1687-1499 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Wireless Communications and Networking |
| spelling | doaj-art-90caf3af09d34ae4bcdc6ee7c08bd2f02025-08-20T03:45:22ZengSpringerOpenEURASIP Journal on Wireless Communications and Networking1687-14992025-07-012025112110.1186/s13638-025-02478-5Semantic aware intelligent optimization for IRS/UAV-enabled MEC in wideband cognitive radio networksWei Zheng0Pengshan Ren1Qing Li2School of Electronic and Information Engineering, Henan Institute of TechnologySchool of Electronic and Information Engineering, Henan Institute of TechnologyData Center of Jiangsu Provincial Administration for Market RegulationAbstract The efficient integration of communication and computation in the internet of things (IoT) presents new opportunities for enhancing system performance but still faces challenges such as interference management, resource allocation and task scheduling. To address these issues, this paper proposes a semantic-aware intelligent optimization framework that combines unmanned aerial vehicles (UAVs) and intelligent reflecting surface (IRS) with mobile edge computing (MEC) to enhance communication quality and semantic awareness in wideband cognitive radio networks. The proposed semantic-aware optimization framework incorporates semantic information to achieve more efficient task scheduling and resource allocation. Particularly, the proposed optimization framework jointly optimizes UAV trajectories, subcarrier allocation, IRS reflection coefficients, task offloading ratios, task priorities and contextual relevance to maximize semantic utility and system energy efficiency while dynamically ensuring task demands. Furthermore, to tackle the non-convexity caused by highly coupled optimization variables, we employ a deep reinforcement learning algorithm based on double deep Q-network and twin delayed deep deterministic policy gradient (DDQN-TD3). Simulation results demonstrate that the proposed approach significantly outperforms baseline schemes by better aligning with user priorities, task requirements, and contextual awareness, leading to improved task completion rates and semantic utility, providing an innovative optimization solution for wideband cognitive radio networks.https://doi.org/10.1186/s13638-025-02478-5Semantic communicationCognitive radio networksIntelligent reflecting surfacesMobile edge computingDeep reinforcement learning |
| spellingShingle | Wei Zheng Pengshan Ren Qing Li Semantic aware intelligent optimization for IRS/UAV-enabled MEC in wideband cognitive radio networks EURASIP Journal on Wireless Communications and Networking Semantic communication Cognitive radio networks Intelligent reflecting surfaces Mobile edge computing Deep reinforcement learning |
| title | Semantic aware intelligent optimization for IRS/UAV-enabled MEC in wideband cognitive radio networks |
| title_full | Semantic aware intelligent optimization for IRS/UAV-enabled MEC in wideband cognitive radio networks |
| title_fullStr | Semantic aware intelligent optimization for IRS/UAV-enabled MEC in wideband cognitive radio networks |
| title_full_unstemmed | Semantic aware intelligent optimization for IRS/UAV-enabled MEC in wideband cognitive radio networks |
| title_short | Semantic aware intelligent optimization for IRS/UAV-enabled MEC in wideband cognitive radio networks |
| title_sort | semantic aware intelligent optimization for irs uav enabled mec in wideband cognitive radio networks |
| topic | Semantic communication Cognitive radio networks Intelligent reflecting surfaces Mobile edge computing Deep reinforcement learning |
| url | https://doi.org/10.1186/s13638-025-02478-5 |
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