Eigenvalue ratio based blind spectrum sensing algorithm for multiband cognitive radios with relatively small samples

2017 ◽  
Vol 53 (16) ◽  
pp. 1150-1152 ◽  
Author(s):  
Xi Yang ◽  
Kejun Lei ◽  
Li Hu ◽  
Xiuying Cao ◽  
Xiaoyu Huang
Keyword(s):  
Spectrum Sensing ◽  
Cognitive Radios ◽  
Small Samples ◽  
Eigenvalue Ratio

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.

scholarly journals Passive Switched Capacitor RF Front Ends for Spectrum Sensing in Cognitive Radios

2014 ◽  
Vol 2014 ◽  
pp. 1-20
Author(s):  
Bodhisatwa Sadhu ◽  
Martin Sturm ◽  
Brian M. Sadler ◽  
Ramesh Harjani
Keyword(s):  
Spectrum Sensing ◽  
Circuit Design ◽  
Software Defined Radio ◽  
High Speed ◽  
Cognitive Radios ◽  
Optimization Techniques ◽  
Switched Capacitor ◽  
Rf Receiver ◽  
Front End ◽  
5 Ghz

This paper explores passive switched capacitor based RF receiver front ends for spectrum sensing. Wideband spectrum sensors remain the most challenging block in the software defined radio hardware design. The use of passive switched capacitors provides a very low power signal conditioning front end that enables parallel digitization and software control and cognitive capabilities in the digital domain. In this paper, existing architectures are reviewed followed by a discussion of high speed passive switched capacitor designs. A passive analog FFT front end design is presented as an example analog conditioning circuit. Design methodology, modeling, and optimization techniques are outlined. Measurements are presented demonstrating a 5 GHz broadband front end that consumes only 4 mW power.

An Intelligent cognitive sensing and detection strategy for medical telemetry networks

2021 ◽  
pp. 1-10
Author(s):  
S. Surekha ◽  
Md. Zia Ur Rahman
Keyword(s):  
Spectrum Sensing ◽  
Smart Cities ◽  
Cognitive Radios ◽  
False Alarm Probability ◽  
Energy Detection ◽  
Least Mean Square ◽  
Radio Technology ◽  
Smart Hospitals ◽  
Medical Telemetry ◽  
Cognitive Radio Technology

In medical telemetry networks, cognitive radio technology is mostly used to avoid licensed spectrum underutilization and by providing access to unlicensed spectrum users without causing interference to primary users, this concept is widely used in development of smart hospitals and smart cities. In medical telemetry networks frequency spectrum concept is used for providing treatment to patients who are far away from hospitals. In cognitive radios, spectrum sensing concept is used in which energy detection method is mostly used because it is simple to implement. While measuring health care environments using cognitive radios probability detection, false alarm probability and threshold parameters are calculated. In this paper for identifying spectrum holes in spectrum sensing using energy detection, distributed diffusion non-negative least mean square algorithm is proposed. It gives better results compared to energy detection concept alone in terms of probability detection converged earlier. If number of nodes are increasing probability detection is decreased from one and move towards left and its SNR is around 1.5-2 dB with proposed method. Hence simulation results give better results in terms of sensing ability while measuring patient condition.

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