User Detection Using Cyclostationary Feature Detection in Cognitive Radio Networks with Various Detection Criteria

2020 ◽  
pp. 1013-1029
Author(s):  
Budati Anil Kumar ◽  
V. Hima Bindu ◽  
N. Swetha
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Feature Detection ◽  
Radio Networks ◽  
Cyclostationary Feature Detection

scholarly journals Noise uncertainty effect on multi-channel cognitive radio networks

2020 ◽  
Vol 10 (5) ◽  
pp. 4818
Author(s):  
Amira Osama ◽  
Heba A. Tag El-Dien ◽  
Ahmad A. Aziz El-Banna ◽  
Adly S. Tag El-Dien
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Feature Detection ◽  
Matched Filter ◽  
Energy Detection ◽  
Radio Networks ◽  
Main Process ◽  
Fluctuation Effect ◽  
Noise Uncertainty ◽  
Channel Throughput

Achieving high throughput is the most important goal of cognitive radio networks. The main process in cognitive radio is spectrum sensing that targets getting vacant channels. There are many sensing methods like matched filter, feature detection, interference temperature and energy detection which is employed in the proposed system; however, energy detection suffers from noise uncertainty. In this paper a study of throughput under noise fluctuation effect is introduced. The work in this paper proposes multi-channel system; the overall multi-channel throughput is studied under noise fluctuation effect. In addition, the proficiency of the network has been examined under different number of channels and sensing time with noise uncertainty.

Exact Optimum RAN Selection Algorithm for Heterogeneous-Type Cognitive Radio Networks

2014 ◽  
Vol 1 ◽  
pp. 652-655
Author(s):  
Takumi.Matsui Takumi.Matsui ◽  
Mikio.Hasegawa Mikio.Hasegawa ◽  
Hiroshi.Hirai Hiroshi.Hirai ◽  
Kiyohito.Nagano Kiyohito.Nagano ◽  
Kazuyuki.Aihara Kazuyuki.Aihara
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Radio Networks ◽  
Selection Algorithm
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