Bio-inspired collaborative spectrum sensing and allocation for cognitive radios

2015 ◽  
Vol 9 (16) ◽  
pp. 1949-1959 ◽  
Author(s):  
Freeha Azmat ◽  
Yunfei Chen ◽  
Nigel Stocks
Keyword(s):  
Spectrum Sensing ◽  
Cognitive Radios ◽  
Collaborative Spectrum Sensing

Spectrum Sensing in Emergency Cognitive Radio Ad Hoc Networks

2013 ◽  
pp. 86-102
Author(s):  
Sasirekha GVK ◽  
Jyotsna Bapat
Keyword(s):  
Cognitive Radio ◽  
Ad Hoc Networks ◽  
Spectrum Sensing ◽  
Ad Hoc ◽  
Cognitive Radios ◽  
Distributed Architectures ◽  
Emergency Situations ◽  
Collaborative Spectrum Sensing ◽  
Military Applications ◽  
Hoc Networks

Ad hoc networks are infrastructure less networks which are self organizing and adaptive. Such networks can be used in emergency situations like disaster management and military applications. Usage of cognitive radios as the wireless terminals in ad hoc networks in emergency situations has distinct advantages. Better bandwidth, interoperability, avoidance of interference, and ant-jamming capabilities are a few such advantages. Ad hoc networks with cognitive radios are wireless terminals used in emergency situations and can be referred to as Emergency Cognitive Radio Ad Hoc Networks (Emergency CRAHNs). In this chapter, the authors discuss emergency CRAHNs and the specific requirements that must be met by the spectrum sensing mechanism used by them. In particular, the authors discuss collaborative spectrum sensing methodology; where in multiple cognitive radios operate together such that reliability of spectrum sensing in improved. This collaborative sensing in ad hoc networks can be either of centralized or distributed architectures, both of which are discussed in this chapter.

Spectrum Sensing in Emergency Cognitive Radio Ad Hoc Networks

2013 ◽  
pp. 944-960
Author(s):  
Sasirekha GVK ◽  
Jyotsna Bapat
Keyword(s):  
Cognitive Radio ◽  
Ad Hoc Networks ◽  
Spectrum Sensing ◽  
Ad Hoc ◽  
Cognitive Radios ◽  
Distributed Architectures ◽  
Emergency Situations ◽  
Collaborative Spectrum Sensing ◽  
Military Applications ◽  
Hoc Networks

Ad hoc networks are infrastructure less networks which are self organizing and adaptive. Such networks can be used in emergency situations like disaster management and military applications. Usage of cognitive radios as the wireless terminals in ad hoc networks in emergency situations has distinct advantages. Better bandwidth, interoperability, avoidance of interference, and ant-jamming capabilities are a few such advantages. Ad hoc networks with cognitive radios are wireless terminals used in emergency situations and can be referred to as Emergency Cognitive Radio Ad Hoc Networks (Emergency CRAHNs). In this chapter, the authors discuss emergency CRAHNs and the specific requirements that must be met by the spectrum sensing mechanism used by them. In particular, the authors discuss collaborative spectrum sensing methodology; where in multiple cognitive radios operate together such that reliability of spectrum sensing in improved. This collaborative sensing in ad hoc networks can be either of centralized or distributed architectures, both of which are discussed in this chapter.

scholarly journals Novel filter bank-based cooperative spectrum sensing under practical challenges for beyond 5G cognitive radios

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Sener Dikmese ◽  
Kishor Lamichhane ◽  
Markku Renfors
Keyword(s):  
Spectrum Sensing ◽  
Filter Bank ◽  
Cooperative Spectrum Sensing ◽  
Minimum Energy ◽  
Cognitive Radios ◽  
Spectrum Management ◽  
Wireless Channel ◽  
First Case ◽  
Channel Effects

AbstractCognitive radio (CR) technology with dynamic spectrum management capabilities is widely advocated for utilizing effectively the unused spectrum resources. The main idea behind CR technology is to trigger secondary communications to utilize the unused spectral resources. However, CR technology heavily relies on spectrum sensing techniques which are applied to estimate the presence of primary user (PU) signals. This paper firstly focuses on novel analysis filter bank (AFB) and FFT-based cooperative spectrum sensing (CSS) techniques as conceptually and computationally simplified CSS methods based on subband energies to detect the spectral holes in the interesting part of the radio spectrum. To counteract the practical wireless channel effects, collaborative subband-based approaches of PU signal sensing are studied. CSS has the capability to relax the problems of both hidden nodes and fading multipath channels. FFT- and AFB-based receiver side sensing methods are applied for OFDM waveform and filter bank-based multicarrier (FBMC) waveform, respectively, the latter one as a candidate beyond-OFDM/beyond-5G scheme. Subband energies are then applied for enhanced energy detection (ED)-based CSS methods that are proposed in the context of wideband, multimode sensing. Our first case study focuses on sensing potential spectral gaps close to relatively strong primary users, considering also the effects of spectral regrowth due to power amplifier nonlinearities. The study shows that AFB-based CSS with FBMC waveform is able to improve the performance significantly. Our second case study considers a novel maximum–minimum energy detector (Max–Min ED)-based CSS. The proposed method is expected to effectively overcome the issue of noise uncertainty (NU) with remarkably lower implementation complexity compared to the existing methods. The developed algorithm with reduced complexity, enhanced detection performance, and improved reliability is presented as an attractive solution to counteract the practical wireless channel effects under low SNR. Closed-form analytic expressions are derived for the threshold and false alarm and detection probabilities considering frequency selective scenarios under NU. The validity of the novel expressions is justified through comparisons with respective results from computer simulations.

Exploiting Radio Correlation and Reliability Information in Collaborative Spectrum Sensing

2011 ◽  
Vol 15 (8) ◽  
pp. 825-827 ◽  
Author(s):  
William C. Headley ◽  
V. Gautham Chavali ◽  
Claudio R. C. M. da Silva
Keyword(s):  
Spectrum Sensing ◽  
Collaborative Spectrum Sensing
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