scholarly journals Spectrum Handoff by Baum-Welch algorithm for services in Cognitive Radio Networks

2019 ◽  
Vol 8 (12) ◽  
pp. 1717-1721
Keyword(s):  
Cognitive Radio ◽  
Markov Model ◽  
Cognitive Radio Networks ◽  
Transition Probabilities ◽  
Primary User ◽  
Radio Networks ◽  
Available Bandwidth ◽  
Spectrum Handoff ◽  
Neural Network Algorithm ◽  
Limited Spectrum

Cognitive Radio Networks (CRN) is the upcoming future prospect in 5G networks. Lack of available spectrum is a serious problem in the networking industry nowadays since, for each individual organization only a limited spectrum bandwidth is offered by National Telecommunications and Information Administration (NTIA). The problem arises due to the increase in the number of users who are supposed to use a limited amount of available bandwidth. Using spectrum handoff allows a cognitive user to access the available licensed spectrum in the absence of the primary user in that particular channel. Efficient spectrum sensing has to be done to check the availability of unused spectrum holes. Machine learning models such as Markov model and Hidden Markov model are used to predict the probabilities. In this paper we have presented a model for efficient sensing using Baum-Welch algorithm, a neural network algorithm which can train inner layer channel traits for given sequence of switching services to yield accurate results without huge datasets. Following emission probabilities are obtained for the channels that are trained from transition probabilities of channel services such as video, voice and data. From the obtained probability values each channel can be offered with best suited services.

scholarly journals Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks

2020 ◽  
Vol 10 (5) ◽  
pp. 1674 ◽  
Author(s):  
Kaleem Arshid ◽  
Iftikhar Hussain ◽  
Muhammad Khawar Bashir ◽  
Shahid Naseem ◽  
Allah Ditta ◽  
...  
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Service Time ◽  
Primary User ◽  
Spectrum Management ◽  
Radio Networks ◽  
Secondary User ◽  
Traffic Pattern ◽  
Spectrum Handoff ◽  
Cognitive Radio User

Through the expeditious expansion of the wireless network, the unlicensed bandwidth-based devices are growing substantially as compared to the present vacant bandwidth. Cognitive radio networks present a proficient solution to the spectrum shortage diminution hitch by allowing the usage of the vacant part of the spectrum that is not currently in use of the Primary User licensed bandwidth to the secondary user or cognitive radio user. Spectrum management procedure in cognitive radio network comprises of spectrum sharing, sensing and handoff. Spectrum handoff plays a vital role in spectrum management and primarily focuses on single handoff strategies. This paper presents a primary user traffic pattern-based opportunistic spectrum handoff (PUTPOSH) approach to use in the cognitive radio networks. PUTPOSH permits a secondary user to sense the arrival of a primary user and use an opportunistic handoff scheme. The opportunistic handoff scheme firstly detects the arrival of the primary users by energy detection sensing and secondly, it allows a cognitive radio user to decide whether to do handoff or not contingent upon the overall service time to reduce the unused handoffs. The handoffs can either be reactive or proactive based on the arrival rate of the primary user. The simulation results show that the presented PUTPOSH approach (a) minimizes the number of handoffs and the overall service time, and (b) maintains the channel utilization and throughput of the system at a maximal point.

scholarly journals Spectrum Handoff in Cognitive Radio Networks: A Survey

2017 ◽  
Vol 10 (04) ◽  
pp. 765-772 ◽  
Author(s):  
Nisar Lala ◽  
Altaf Balkhi ◽  
G M Mir
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Primary User ◽  
Radio Networks ◽  
Spectrum Utilization ◽  
Secondary Users ◽  
Spectrum Handoff ◽  
Suitable Target ◽  
Promising Solution ◽  
Minimum Performance

Cognitive radio (CR) is a promising solution to improve the spectrum utilization by enabling unlicensed users to exploit the spectrum in an opportunistic manner. Spectrum handoff is a different type of handoff in CR necessitated by the reappearance of primary user (PU) in the licensed band presently occupied by the secondary users (SUs). Spectrum handoff procedures aim to help the SUs to vacate the occupied licensed spectrum and find suitable target channel to resume the unfinished transmission. The purpose of spectrum mobility management in cognitive radio networks is to make sure that the transitions are made smoothly and rapidly such that the applications running on a cognitive user perceive minimum performance degradation during a spectrum handoff. In this paper, we will survey the literature on spectrum handoff in cognitive radio networks.

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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