scholarly journals Sensing Device Management for History-Based Spectrum Sharing in Cognitive Radio Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Mohammed Hawa ◽  
Fahed Jubair ◽  
Raed Al-Zubi ◽  
Ramzi Saifan
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sharing ◽  
Primary User ◽  
Radio Networks ◽  
Base Stations ◽  
Spectrum Access ◽  
Device Management ◽  
Novel Approach ◽  
The Status ◽  
Energy Detectors

A novel approach to managing a fully distributed cognitive radio network (CRN) is presented. This approach builds on the concept of history-based spectrum access, in which cognitive base stations (BSs) independently estimate the system load using history records and adaptively swap their occupied spectrum bands to ensure allocation fairness and high overall throughput. In addition, cognitive BSs monitor primary user (PU) behavior in order to avoid interfering with active PUs. In this work, we address two issues that afflict history-based access: the first is the high cost of the sensing devices needed at each cognitive BS to be able to independently draw conclusions about the status of the CRN and the second is the unreliability inherent in practical sensing hardware (such as energy detectors). Simulation results show that the proposed technique manages to solve the two abovementioned issues without any noticeable drop in performance and without sacrificing the distributed nature of the protocol.

White Cloud or Black Cloud: Opportunity and Challenge of Spectrum Sharing on Cloud Computing

2012 ◽  
Vol 430-432 ◽  
pp. 1290-1293 ◽  
Author(s):  
Li Xia Liu ◽  
Gang Hu ◽  
Zhen Huang ◽  
Yu Xing Peng
Keyword(s):  
Cloud Computing ◽  
Cognitive Radio ◽  
Spectrum Sharing ◽  
Dynamic Spectrum Access ◽  
Rapid Development ◽  
Radio Networks ◽  
Dynamic Spectrum ◽  
Spectrum Access ◽  
Limited Power ◽  
Computing Capacity

In order to fully utilize the spectrum resource, dynamic spectrum access becomes a promising approach to increase the opportunity of spectrum access with the rapid development of cognitive radio. However the performance of cognitive radio networks (CRN) is considerably constrained by its limited power, memory and computing ability. Fortunately cloud computing, which is the highlight of current research, has the potential to make up for the disadvantages because of its vast storage and computing capacity. In this paper we will discuss the convergence of spectrum sharing and cloud computing from several aspects including model, advantages and challenges. A spectrum sharing model based on cloud computing (SSC) will be introduced.

scholarly journals Session based cooperation in cognitive radio networks: network level approach

2020 ◽  
Vol 9 (1) ◽  
pp. 40
Author(s):  
Sunil Ghildiyal
Keyword(s):  
Resource Allocation ◽  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Spectrum Sharing ◽  
Radio Networks ◽  
Spectrum Access ◽  
Secondary Users ◽  
Generation Spectrum ◽  
Primary Users ◽  
Present Generation

<p>The cognitive radio prototype performance is to alleviate the scarcity of spectral resources for wireless communication through intelligent sensing and quick resource allocation techniques. Secondary users (SU’s) actively obtain the spectrum access opportunity by supporting primary users (PU’s) in cognitive radio networks (CRNs). In present generation, spectrum access is endowed through cooperative communication based link-level frame-based cooperative (LLC) principle. In this SUs independently act as conveyors for PUs to achieve spectrum access opportunities. Unfortunately, this LLC approach cannot fully exploit spectrum access opportunities to enhance the throughput of CRNs and fails to motivate PUs to join the spectrum sharing processes. Therefore to overcome this con, network level cooperative (NLC) principle was used, where SUs are integrated mutually to collaborate with PUs session by session, instead of frame based cooperation for spectrum access opportunities. NLC approach has justified the challenges facing in LLC approach. In this paper we make a survey of some models that have been proposed to tackle the problem of LLC. We show the relevant aspects of each model, in order to characterize the parameters that we should take in account to achieve a spectrum access opportunity.</p>

scholarly journals Joint resource optimization in spectrum sharing decode and forward relaying networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0251509
Author(s):  
Dong Qin
Keyword(s):  
Cognitive Radio ◽  
Communication Networks ◽  
Cognitive Radio Networks ◽  
Orthogonal Frequency Division Multiplexing ◽  
Spectrum Sharing ◽  
Primary User ◽  
Radio Networks ◽  
Decode And Forward ◽  
Secondary Network ◽  
Sum Rate

This paper proposes an adaptive power allocation and subcarrier pairing algorithm for orthogonal frequency division multiplexing based decode and forward cognitive radio networks, where primary and secondary users achieve spectrum sharing in the same frequency band. The secondary network tries to maximize its sum rate while ensuring that the interference introduced to the primary network is below an acceptable level. Although similar problems have been investigated in traditional cooperative communication networks, it’s still an open issue in cognitive radio networks due to interference thresholds. The power consumed by the secondary network is not only limited by its own power peak, but also by the interference threshold of the primary user. Our proposed algorithm not only allocates power and pairs subcarriers reasonably, but also specifies the conditions under which the relaying link is superior to the direct transmission. Simulation results show that the sum rate of the proposed algorithm exceeds other methods and obtains a significant performance gain.

scholarly journals Second order Kalman filtering channel estimation and machine learning methods for spectrum sensing in cognitive radio networks

2021 ◽  
Author(s):  
Olusegun Peter Awe ◽  
Daniel Adebowale Babatunde ◽  
Sangarapillai Lambotharan ◽  
Basil AsSadhan
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Clustering Algorithm ◽  
Polynomial Regression ◽  
Primary User ◽  
Radio Networks ◽  
Second Order

AbstractWe address the problem of spectrum sensing in decentralized cognitive radio networks using a parametric machine learning method. In particular, to mitigate sensing performance degradation due to the mobility of the secondary users (SUs) in the presence of scatterers, we propose and investigate a classifier that uses a pilot based second order Kalman filter tracker for estimating the slowly varying channel gain between the primary user (PU) transmitter and the mobile SUs. Using the energy measurements at SU terminals as feature vectors, the algorithm is initialized by a K-means clustering algorithm with two centroids corresponding to the active and inactive status of PU transmitter. Under mobility, the centroid corresponding to the active PU status is adapted according to the estimates of the channels given by the Kalman filter and an adaptive K-means clustering technique is used to make classification decisions on the PU activity. Furthermore, to address the possibility that the SU receiver might experience location dependent co-channel interference, we have proposed a quadratic polynomial regression algorithm for estimating the noise plus interference power in the presence of mobility which can be used for adapting the centroid corresponding to inactive PU status. Simulation results demonstrate the efficacy of the proposed algorithm.

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