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Panda, Sunita
- CASE STUDIES OF ENERGY-BASED RADIO FREQUENCY SPECTRUM SENSING TECHNIQUE FOR COGNITIVE RADIO NETWORKS
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Authors
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1 GITAM School of Technology, IN
1 GITAM School of Technology, IN
Source
ICTACT Journal on Communication Technology, Vol 12, No 1 (2021), Pagination: 2251-2258Abstract
The trend to overcome wireless interference emerged in techniques like frequency agility, hopping, dual band technologies etc. However, these techniques suppress inter-operability of wireless technologies operating at the same frequency like Wi-Fi, ZigBee, Bluetooth, Z-Wave, etc. As a result, in the unlicensed wireless public infrastructures bandwidth available for usage is becoming short of hand and utilizing only licensed band increases cost of ownership which cannot be sustained by public infrastructures and developing countries. Thus, new digital era requires a technology which can help overcome the foresaid challenges that can utilize both licensed and unlicensed spectrums through adaptive methodologies that ensures very high wireless communication availability in circumstances of bandwidth non-availability due to overcrowding or wireless interference at lower affordable cost. Thus, a group of radio that forms a network designed for this purpose is called Cognitive Radio Network (CRN). In the proposed paper an effort is made to provide simulated performance analysis insight of novel energy-based channel sensing mechanism which is the crucial aspect of spectrum management in cognitive radios. In simulation the mechanism is analysed for different cases in which combination of both real and complex information and noise signals are used. Through proper results and graphs different factors for consideration are presented. Sensing in co-operative cognitive networks is also discussed, addressing different issues like bandwidth, quantisation techniques, detection accuracy etc. that may help fellow researcher to continue the further developments with less time and efforts.Keywords
Cognitive Radio Networks, Energy Based Sensing, Null Hypothesis, Low SNR, Complex Gaussian Noise.Full Text
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