Joint Detection of Spatial-Temporal Spectrum Holes for Cognitive Radio Networks

2014 ◽  
Vol 556-562 ◽  
pp. 2802-2805
Author(s):  
Fu Lai Liu ◽  
Shou Ming Guo ◽  
Rui Yan Du
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Primary User ◽  
Spatial Spectrum ◽  
Radio Networks ◽  
Spectrum Utilization ◽  
Secondary Users ◽  
Secondary Network ◽  
Spectrum Holes

Spectrum sensing is a key technology to reliably detect spectrum holes in multi-dimensions for cognitive radio networks. In this paper, a joint spatial-temporal spectrum sensing scheme is proposed. First of all, the secondary users (SUs) located inside the primary exclusive region (PER) perform temporal sensing. When the primary user (PU) is present, the SUs located outside the PER perform spatial spectrum sensing. The proposed method can improve the spectrum utilization by exploiting both temporal and spatial spectrum holes. The achievable throughput for the secondary network of joint spatial-temporal sensing is higher than that of pure temporal sensing. Simulation results demonstrate the effectiveness of the proposed approach.

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.

Secondary Data Transmissions with Two-Way Relay in Cognitive Radio Networks

2013 ◽  
Vol 336-338 ◽  
pp. 1650-1655
Author(s):  
Yuan Gao ◽  
Chang Ping Zhu ◽  
Yi Bin Tamg
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Secondary Data ◽  
Primary User ◽  
Radio Networks ◽  
Secondary Users ◽  
Step Model ◽  
Spectrum Hole ◽  
User Protection

For improving the spectrum hole utilization in cognitive radio networks, two-way relaying is used in this paper to assist two secondary users in exchanging information. The 2-step and 3-step two-way relaying models are respectively discussed with imperfect spectrum sensing. Moreover, the closed-form expressions of outage probability for the two models are derived with a primary user protection constraint. Simulation shows that 3-step model outperforms 2-step in terms of the outage performance.

scholarly journals Optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Haiyan Ye ◽  
Jiabao Jiang
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Cooperative Spectrum Sensing ◽  
Radio Networks ◽  
Spectrum Utilization ◽  
Channel Characteristics ◽  
Optimal Linear ◽  
Static Division ◽  
Cooperative Nodes

AbstractThe lack of spectrum resources restricts the development of wireless communication applications. In order to solve the problems of low spectrum utilization and channel congestion caused by the static division of spectrum resource, this paper proposes an optimal linear weighted cooperative spectrum sensing for clustered-based cognitive radio networks. In this scheme, different weight values will be assigned for cooperative nodes according to the SNR of cognitive users and the historical sensing accuracy. In addition, the cognitive users can be clustered, and the users with the better channel characteristics will be selected as cluster heads for gathering the local sensing information. Simulation results show that the proposed scheme can obtain better sensing performance, improve the detection probability and reduce the error probability.

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.

Deep Cooperative Spectrum Sensing Utilizing Recurrent Convolutional Neural Networks

2020 ◽  
Author(s):  
Rahil Sarikhani ◽  
Farshid Keynia
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Recurrent Neural Network ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Secondary Users ◽  
Spectrum Holes

Abstract Cognitive Radio (CR) network was introduced as a promising approach in utilizing spectrum holes. Spectrum sensing is the first stage of this utilization which could be improved using cooperation, namely Cooperative Spectrum Sensing (CSS), where some Secondary Users (SUs) collaborate to detect the existence of the Primary User (PU). In this paper, to improve the accuracy of detection Deep Learning (DL) is used. In order to make it more practical, Recurrent Neural Network (RNN) is used since there are some memory in the channel and the state of the PUs in the network. Hence, the proposed RNN is compared with the Convolutional Neural Network (CNN), and it represents useful advantages to the contrast one, which is demonstrated by simulation.

Interference Modeling and Analysis in Cognitive Radio Networks

2013 ◽  
Vol 4 (4) ◽  
pp. 1-15
Author(s):  
Yanxiao Zhao ◽  
Bighnaraj Panigrahi ◽  
Kazem Sohraby ◽  
Wei Wang
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Critical Role ◽  
Radio Networks ◽  
Secondary Users ◽  
Idle State ◽  
Modeling And Analysis ◽  
Interference Modeling ◽  
Primary Users

Cognitive radio networks (CRNs) have received considerable attention and viewed as a promising paradigm for future wireless networking. Its major difference from the traditional wireless networks is that secondary users are allowed to access the channel if they pose no harmful interference to primary users. This distinct feature of CRNs has raised an essential and challenging question, i.e., how to accurately estimate interference to the primary users from the secondary users? In addition, spectrum sensing plays a critical role in CRNs. Secondary users have to sense the channel before they transmit. A two-state sensing model is commonly used, which classifies a channel into either busy or idle state. Secondary users can only utilize a channel when it is detected to be in idle state. In this paper, we tackle the estimation of interference at the primary receiver due to concurrently active secondary users. With the spectrum sensing, secondary users are refrained from transmitting once an active user falls into their sensing range. As a result, the maximum number of simultaneously interfering secondary users is bounded, typically ranging from 1 to 4. This significant conclusion considerably simplifies interference modeling in CRNs. The authors present all the cases with possible simultaneously interfering secondary users. Moreover, the authors derive the probability for each case. Extensive simulations are conducted and results validate the effectiveness and accuracy of the proposed approach.

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