Improved Detection Performance of Cognitive Radio Networks in AWGN and Rayleigh Fading Environments



Título del documento: Improved Detection Performance of Cognitive Radio Networks in AWGN and Rayleigh Fading Environments
Revista: Journal of applied research and technology
Base de datos: PERIÓDICA
Número de sistema: 000377657
ISSN: 1665-6423
Autores: 1
2
1
2
Instituciones: 1Multimedia University, Faculty of Engineering, Cyberjaya, Selangor. Malasia
2Universiti Kebangsaan Malaysia, Faculty of Engineering and Built Environment, Bangi, Selangor. Malasia
Año:
Periodo: Jun
Volumen: 11
Número: 3
Paginación: 437-446
País: México
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Experimental, aplicado
Resumen en inglés Cognitive radios (CRs) have been recently emerging as prime candidates to enhance spectral efficiency by exploiting spectrum-aware systems which can reliably monitor licensed users' activities. CR users monitor such activities by performing spectrum sensing to detect potential white spaces. However, this process of local sensing might be a challenging task in fading environments. The inefficiency of spectrum sensing might cause interference to licensees if they are miss-detected by CR users. Thus, cooperative spectrum sensing is proposed as a means to combat fading and improve the detection performance. However, the detection performance does not improve by such cooperation when low-SNR environment is considered. In this paper, cooperative spectrum sensing with PSO-based threshold adaptation is presented to address the aforementioned problem. Simulation results show that the detection performance with PSO-based adaptive detection threshold is improved, particularly, in low-SNR environment
Disciplinas: Ingeniería,
Ciencias de la computación
Palabras clave: Ingeniería de telecomunicaciones,
Radio cognitiva,
Espectro cooperativo,
Adaptación dinámica del umbral,
Optimización por enjambre de partículas
Keyword: Engineering,
Computer science,
Telecommunications engineering,
Cognitive radio,
Cooperative spectrum,
Dynamic threshold adaptation,
Particle swarm optimization
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