{"title":"认知无线电网络中的 IRS 辅助频谱感知与灰狼优化","authors":"Vishwas Srivastava, Binod Prasad","doi":"10.1016/j.phycom.2024.102436","DOIUrl":null,"url":null,"abstract":"<div><p>Cognitive radio (CR) is of crucial importance in providing efficient management of limited spectrum resources. However, its performance relies on efficient spectrum sensing. This paper investigates a novel approach for CR networks that leverages intelligent reflecting surface (IRS) specifically for spectrum sensing and non-orthogonal multiple access (NOMA) for data transmission. We propose a Grey-Wolf Optimization (GWO) based IRS optimization approach to maximize spectrum sensing performance. Independent of the IRS, NOMA is employed to improve spectral efficiency during data transmission. The performance is evaluated in terms of throughput and spectrum sensing parameters, namely probability of false alarm and missed detection. Numerical and simulation results demonstrate that GWO-based IRS optimization significantly outperforms conventional nature-inspired algorithms, achieving approximately 97% improvement in spectrum sensing accuracy. Based on the improved spectrum sensing results, the effective data transmission throughput is evaluated and validated through extensive simulation.</p></div>","PeriodicalId":48707,"journal":{"name":"Physical Communication","volume":"66 ","pages":"Article 102436"},"PeriodicalIF":2.0000,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"IRS assisted spectrum sensing in cognitive radio network with grey wolf optimization\",\"authors\":\"Vishwas Srivastava, Binod Prasad\",\"doi\":\"10.1016/j.phycom.2024.102436\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Cognitive radio (CR) is of crucial importance in providing efficient management of limited spectrum resources. However, its performance relies on efficient spectrum sensing. This paper investigates a novel approach for CR networks that leverages intelligent reflecting surface (IRS) specifically for spectrum sensing and non-orthogonal multiple access (NOMA) for data transmission. We propose a Grey-Wolf Optimization (GWO) based IRS optimization approach to maximize spectrum sensing performance. Independent of the IRS, NOMA is employed to improve spectral efficiency during data transmission. The performance is evaluated in terms of throughput and spectrum sensing parameters, namely probability of false alarm and missed detection. Numerical and simulation results demonstrate that GWO-based IRS optimization significantly outperforms conventional nature-inspired algorithms, achieving approximately 97% improvement in spectrum sensing accuracy. Based on the improved spectrum sensing results, the effective data transmission throughput is evaluated and validated through extensive simulation.</p></div>\",\"PeriodicalId\":48707,\"journal\":{\"name\":\"Physical Communication\",\"volume\":\"66 \",\"pages\":\"Article 102436\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-07-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Physical Communication\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S187449072400154X\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical Communication","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S187449072400154X","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
IRS assisted spectrum sensing in cognitive radio network with grey wolf optimization
Cognitive radio (CR) is of crucial importance in providing efficient management of limited spectrum resources. However, its performance relies on efficient spectrum sensing. This paper investigates a novel approach for CR networks that leverages intelligent reflecting surface (IRS) specifically for spectrum sensing and non-orthogonal multiple access (NOMA) for data transmission. We propose a Grey-Wolf Optimization (GWO) based IRS optimization approach to maximize spectrum sensing performance. Independent of the IRS, NOMA is employed to improve spectral efficiency during data transmission. The performance is evaluated in terms of throughput and spectrum sensing parameters, namely probability of false alarm and missed detection. Numerical and simulation results demonstrate that GWO-based IRS optimization significantly outperforms conventional nature-inspired algorithms, achieving approximately 97% improvement in spectrum sensing accuracy. Based on the improved spectrum sensing results, the effective data transmission throughput is evaluated and validated through extensive simulation.
期刊介绍：
PHYCOM: Physical Communication is an international and archival journal providing complete coverage of all topics of interest to those involved in all aspects of physical layer communications. Theoretical research contributions presenting new techniques, concepts or analyses, applied contributions reporting on experiences and experiments, and tutorials are published.
Topics of interest include but are not limited to:
Physical layer issues of Wireless Local Area Networks, WiMAX, Wireless Mesh Networks, Sensor and Ad Hoc Networks, PCS Systems; Radio access protocols and algorithms for the physical layer; Spread Spectrum Communications; Channel Modeling; Detection and Estimation; Modulation and Coding; Multiplexing and Carrier Techniques; Broadband Wireless Communications; Wireless Personal Communications; Multi-user Detection; Signal Separation and Interference rejection: Multimedia Communications over Wireless; DSP Applications to Wireless Systems; Experimental and Prototype Results; Multiple Access Techniques; Space-time Processing; Synchronization Techniques; Error Control Techniques; Cryptography; Software Radios; Tracking; Resource Allocation and Inference Management; Multi-rate and Multi-carrier Communications; Cross layer Design and Optimization; Propagation and Channel Characterization; OFDM Systems; MIMO Systems; Ultra-Wideband Communications; Cognitive Radio System Architectures; Platforms and Hardware Implementations for the Support of Cognitive, Radio Systems; Cognitive Radio Resource Management and Dynamic Spectrum Sharing.
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