Reinforcement Learning Based Decision Fusion Scheme for Cooperative Spectrum Sensing in Cognitive Radios

2018 ◽  
Author(s):  
V. Balaji
Keyword(s):  
Reinforcement Learning ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Cognitive Radios ◽  
Decision Fusion ◽  
Fusion Scheme

Cooperative Spectrum Sensing with Censoring of Cognitive Radios and MRC-Based Fusion in Fading and Shadowing Channels

2015 ◽  
pp. 38-67
Author(s):  
Srinivas Nallagonda ◽  
Sanjay Dhar Roy ◽  
Sumit Kundu ◽  
Gianluigi Ferrari ◽  
Riccardo Raheli
Keyword(s):  
Channel Estimation ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Minimum Mean Square Error ◽  
Cognitive Radios ◽  
Decision Fusion ◽  
Soft Data ◽  
Fading And Shadowing ◽  
Log Normal ◽  
Reporting Channels

In this chapter, the authors study the performance of Cooperative Spectrum Sensing (CSS) with soft data fusion, given by Maximal Ratio Combining (MRC)-based fusion with Weibull faded channels, and Log-normal shadowed channels. More precisely, they evaluate the performance of a MRC-based CSS with Cognitive Radios (CRs) censored on the basis of the quality of the reporting channels. The performance of CSS with two censoring schemes, namely rank-based and threshold-based, is studied in the presence of Weibull fading, Rayleigh fading, and Log-normal shadowing in the reporting channels, considering MRC fusion. The performance is compared with those of schemes based on hard decision fusion rules. Furthermore, depending on perfect or imperfect Minimum Mean Square Error (MMSE) channel estimation, the authors analyze the impact of channel estimation strategy on the censoring schemes. The performance is studied in terms of missed detection probability as a function of several network, fading, and shadowing parameters.

scholarly journals A Genetic Algorithm-Based Soft Decision Fusion Scheme in Cognitive IoT Networks with Malicious Users

2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Muhammad Sajjad Khan ◽  
Noor Gul ◽  
Junsu Kim ◽  
Ijaz Mansoor Qureshi ◽  
Su Min Kim
Keyword(s):  
Genetic Algorithm ◽  
Spectrum Sensing ◽  
Dynamic Spectrum Access ◽  
Cooperative Spectrum Sensing ◽  
Decision Fusion ◽  
Weighting Coefficient ◽  
Soft Decision ◽  
Secondary Users ◽  
Coefficient Vector ◽  
Fusion Scheme

Internet of Things (IoT) is a new challenging paradigm for connecting a variety of heterogeneous networks. Since its introduction, many researchers have been studying how to efficiently exploit and manage spectrum resource for IoT applications. An explosive increase in the number of IoT devices accelerates towards the future-connected society but yields a high system complexity. Cognitive radio (CR) technology is also a promising candidate for future wireless communications. CR via dynamic spectrum access provides opportunities to secondary users (SUs) to access licensed spectrum bands without interfering primary users by performing spectrum sensing before accessing available spectrum bands. However, multipath effects can degrade the sensing capability of an individual SU. Therefore, for more precise sensing, it is helpful to exploit multiple collaborative sensing users. The main problem in cooperative spectrum sensing is the presence of inaccurate sensing information received from the multipath-affected SUs and malicious users at a fusion center (FC). In this paper, we propose a genetic algorithm-based soft decision fusion scheme to determine the optimum weighting coefficient vector against SUs’ sensing information. The weighting coefficient vector is further utilized in a soft decision rule at FC in order to make a global decision. Through extensive simulations, the effectiveness of the proposed scheme is evaluated compared with other conventional schemes.

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.

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