TY - GEN
T1 - Optimizing energy in cooperative sensing cognitive radios
AU - Fajemilehin, Temitope
AU - Yahya, Abid
AU - Aldhaibani, Jaafar A.
AU - Langat, Kibet
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - One of the viable solutions for effective spectrum management is cognitive radio. Single sensing systems are prone to interference; thus, the use of cooperative spectrum sensing. This paper aims to determine the required number of cognitive radios that would optimize the performance of a communication network in terms of energy utilization and bandwidth requirement. The cognitive sensing technique used was energy detection due to its reduced energy, computational, and communication resources requirement. The channel noise variance was set to −25 dB. Spectrum sensing was carried out at a frequency of 936 MHz and bandwidth of 200 kHz. Machine learning was first used to enhance the specificity of detection to minimize interference. Genetic Algorithm (GA) and Simulated Annealing (SA) were used to optimize the number of cognitive radios putting into consideration all constraints in the network. Genetic Algorithm gave a better result of two optimization techniques used. It gave an overall reduction of 40.74% in energy conserved without affecting the detection accuracy.
AB - One of the viable solutions for effective spectrum management is cognitive radio. Single sensing systems are prone to interference; thus, the use of cooperative spectrum sensing. This paper aims to determine the required number of cognitive radios that would optimize the performance of a communication network in terms of energy utilization and bandwidth requirement. The cognitive sensing technique used was energy detection due to its reduced energy, computational, and communication resources requirement. The channel noise variance was set to −25 dB. Spectrum sensing was carried out at a frequency of 936 MHz and bandwidth of 200 kHz. Machine learning was first used to enhance the specificity of detection to minimize interference. Genetic Algorithm (GA) and Simulated Annealing (SA) were used to optimize the number of cognitive radios putting into consideration all constraints in the network. Genetic Algorithm gave a better result of two optimization techniques used. It gave an overall reduction of 40.74% in energy conserved without affecting the detection accuracy.
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U2 - 10.1007/978-3-030-55340-1_13
DO - 10.1007/978-3-030-55340-1_13
M3 - Conference contribution
AN - SCOPUS:85089718774
SN - 9783030553395
T3 - Communications in Computer and Information Science
SP - 178
EP - 188
BT - New Trends in Information and Communications Technology Applications - 4th International Conference, NTICT 2020, Proceedings
A2 - Al-Bakry, Abbas M.
A2 - Al-Mamory, Safaa O.
A2 - Sahib, Mouayad A.
A2 - Hasan, Haitham S.
A2 - Nayl, Thaker M.
A2 - Al-Dhaibani, Jaafar A.
A2 - Oreku, George S.
PB - Springer India
T2 - 4th International Conference on New Trends in Information and Communications Technology Applications, NTICT 2020
Y2 - 15 June 2020 through 15 June 2020
ER -