scholarly journals Federated Learning for 5G Radio Spectrum Sensing

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 198
Author(s):  
Małgorzata Wasilewska ◽  
Hanna Bogucka ◽  
Adrian Kliks
Keyword(s):  
Spectrum Sensing ◽  
Spectrum Sharing ◽  
Signal To Noise Ratio ◽  
Radio Spectrum ◽  
Good Choice ◽  
Decision Making Process ◽  
Detection Accuracy ◽  
Secondary Users ◽  
Spectrum Detection ◽  
Model Training

Spectrum sensing (SS) is an important tool in finding new opportunities for spectrum sharing. The users, called Secondary Users (SU), who do not have a license to transmit without hindrance, need to employ SS in order to detect and use the spectrum without interfering with the licensed users’ (primary users’ (PUs’)) transmission. Deep learning (DL) has proven to be a good choice as an intelligent SS algorithm that considers radio environmental factors in the decision-making process. It is impossible though for SU to collect the required data and train complex DL models. In this paper, we propose to employ a Federated Learning (FL) algorithm in order to distribute data collection and model training processes over many devices. The proposed method categorizes FL devices into groups by their mean Signal-to-Noise ratio (SNR) and creates a common DL model for each group in the iterative process. The results show that detection accuracy obtained via the FL algorithm is similar to detection accuracy obtained by employing several DL models, namely convolutional neural networks (CNNs), specialized in spectrum detection for a PU signal with a given mean SNR value. At the same time, the main goal of simplification of the SS process in the network is achieved.

scholarly journals Utilization of idle time slot in spectrum sensing under noise uncertainty

2022 ◽  
Vol 12 (1) ◽  
pp. 431
Author(s):  
Iyad Khalil Tumar ◽  
Adnan Mohammad Arar ◽  
Ayman Abd El Saleh
Keyword(s):  
Spectrum Sensing ◽  
Time Slot ◽  
Signal To Noise Ratio ◽  
Primary User ◽  
Idle Time ◽  
Detection Accuracy ◽  
Secondary Users ◽  
Noise Uncertainty ◽  
Received Power ◽  
Sensing Time

<p>Spectrum sensing in cognitive radio (CR) is a critical process as it directly influences the accuracy of detection. Noise uncertainty affects the reliability of detecting vacant holes in the spectrum, thus limiting the access of that spectrum by secondary users (SUs). In such uncertain environment; SUs sense the received power of a primary user (PU) independently with different measures of signal-to-noise ratio (SNR). Long sensing time serves in mitigating the effect of noise uncertainty, but on the cost of throughput performance of CR system. In this paper, the scheme of an asynchronous and crossed sensing-reporting is presented. The scheme reduces energy consumption during sensing process without affecting the detection accuracy. Exploiting the included idle time (𝑇𝑖) in sensing time slot; each SU collects power samples with higher SNR directly performs the reporting process to a fusion center (FC) consecutively. The FC terminates the sensing and reporting processes at a specific sensing time that corresponds to the lowest SNR (𝑆𝑁𝑅𝑤𝑎𝑙𝑙). Furthermore, this integrated scheme aims at optimizing the total frame duration (𝑇𝑓). Mathematical expressions of the scheme are obtained. Analytical results show the efficiency of the scheme in terms of energy saving and throughput increment under noise uncerainty.</p>

Research of Spectrum Detecting Technology on Cognitive Radio

2013 ◽  
Vol 433-435 ◽  
pp. 911-914
Author(s):  
Hong Yan Mao
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Spectrum Sharing ◽  
Cooperative Spectrum Sensing ◽  
Detection Method ◽  
Radio Spectrum ◽  
Spectrum Utilization ◽  
Transmission Parameters ◽  
Spectrum Detection

Cognitive radio (CR) is an intelligent spectrum sharing technology. It can improve the spectrum utilization by sensing spectrum environment, learning intelligently and adjusting the transmission parameters. The discussion is focused on spectrum detecting technology in cognitive radio. Spectrum detecting algorithms are analyzed and compared .The centralized cooperative spectrum detection method, distributed cooperative spectrum sensing method and relay cooperative spectrum detection method are analyzed also.

scholarly journals Spectrum Sensing in Cognitive Radio using Frequency Domain

2019 ◽  
Vol 9 (2) ◽  
pp. 3475-3477
Keyword(s):  
Cognitive Radio ◽  
Frequency Domain ◽  
Spectrum Sensing ◽  
Bandwidth Allocation ◽  
Dynamic Spectrum Access ◽  
Signal To Noise Ratio ◽  
Wide Band ◽  
Energy Detection ◽  
Secondary Users ◽  
Sensing Applications

An efficient bandwidth allocation and dynamic bandwidth access away from its previous limits is referred as cognitive radio (CR).The limited spectrum with inefficient usage requires the advances of dynamic spectrum access approach, where the secondary users are authorized to utilize the unused temporary licensed spectrum. For this reason it is essential to analyze the absence/presence of primary users for spectrum usage. So spectrum sensing is the main requirement and developed to sense the absence/ presence of a licensed user. This paper shows the design model of energy detection based spectrum sensing in frequency domain utilizing Binary Symmetric Channel (BSC) ,Additive white real Gaussian channel (AWGN), Rayleigh fading channel users for 16-Quadrature Amplitude Modulation(QAM) which is utilized for the wide band sensing applications at low Signal to noise Ratio(SNR) level to reduce the false error identification. The spectrum sensing techniques has least computational complexity. Simulink model for the energy detection based spectrum sensing using frequency domain in MATLAB 2014a.

scholarly journals Cooperative Spectrum Sensing Based on Multi-Features Combination Network in Cognitive Radio Network

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 129
Author(s):  
Mingdong Xu ◽  
Zhendong Yin ◽  
Yanlong Zhao ◽  
Zhilu Wu
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Network ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Primary User ◽  
Radio Spectrum ◽  
Local Information ◽  
Channel State ◽  
Single Node

cognitive radio, as a key technology to improve the utilization of radio spectrum, acquired much attention. Moreover, spectrum sensing has an irreplaceable position in the field of cognitive radio and was widely studied. The convolutional neural networks (CNNs) and the gate recurrent unit (GRU) are complementary in their modelling capabilities. In this paper, we introduce a CNN-GRU network to obtain the local information for single-node spectrum sensing, in which CNN is used to extract spatial feature and GRU is used to extract the temporal feature. Then, the combination network receives the features extracted by the CNN-GRU network to achieve multifeatures combination and obtains the final cooperation result. The cooperative spectrum sensing scheme based on Multifeatures Combination Network enhances the sensing reliability by fusing the local information from different sensing nodes. To accommodate the detection of multiple types of signals, we generated 8 kinds of modulation types to train the model. Theoretical analysis and simulation results show that the cooperative spectrum sensing algorithm proposed in this paper improved detection performance with no prior knowledge about the information of primary user or channel state. Our proposed method achieved competitive performance under the condition of large dynamic signal-to-noise ratio.

The New Research of Spectrum Sensing Technology for Cognitive Radio

2014 ◽  
Vol 945-949 ◽  
pp. 2301-2305
Author(s):  
Yi Peng ◽  
Yan Jun Wang
Keyword(s):  
Cognitive Radio ◽  
Wireless Communication ◽  
Spectrum Sensing ◽  
Communication Technology ◽  
Spectrum Sharing ◽  
Rapid Development ◽  
Current Model ◽  
Radio Spectrum ◽  
Spectrum Management ◽  
Sensing Technology

With the rapid development of wireless communication technology, the shortage of spectrum resources is becoming more and more serious, and may even become a bottleneck restricting of the development wireless communication technology in the future. Now, Spectrum sensing technology, spectrum sharing technology and spectrum management technology is the three core technologies of cognitive radio spectrum,and sensing technology is to implement the follow-up of spectrum sharing and the premise of spectrum management.So mainly to the current model of the cognitive radio spectrum sensing technology,to make a classification and comparison, finally it is concluded that cognitive users under the environment of higher signal-to-noise ratio, the better results of the perceived performance.

scholarly journals Use Hierarchical Codebook to Improve the Primary User Detection in the Cognitive Radio’s Cooperative Spectrum Sensing

2021 ◽  
Vol 5 (6) ◽  
pp. 22-28
Author(s):  
Hoai Trung Tran
Keyword(s):  
Spectrum Sensing ◽  
Channel Model ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Primary User ◽  
Mimo Channel ◽  
High Signal ◽  
Secondary Users ◽  
Input Multiple Output

Currently, the cognitive network is receiving much attention due to the advantages it brings to users. An important method in cognitive radio networks is spectrum sensing, as it allows secondary users (SUs) to detect the existence of a primary user (PU). Information of probability of false detection or warning about the PU is sent to a fusion center (FC) by the SUs, from which the FC will decide whether or not to allow the SUs to use the PU spectrum to obtain information. The transmission of information with a high signal to noise ratio (SNR) will increase the FC's ability to detect the existence of the PU. However, researchers are currently focusing on probabilistic formulas assuming that the channel is known ideally or there is nominal channel information at the FC; moreover, one model where the FC only knows the channel correlation matrix. Furthermore, studies are still assuming this is a simple multiple input – multiple output (MIMO) channel model but do not pay much attention to the signal processing at the transmitting and receiving antennas between the SUs and the FCs. A new method introduced in this paper when combining beamforming and hierarchical codebook makes the ability to detect the existence of the PU at the FC significantly increased compared to traditional methods.

TRUST-BASED SPECTRUM SHARING FOR COGNITIVE RADIO NETWORKS

2011 ◽  
Vol 12 (03) ◽  
pp. 155-171 ◽  
Author(s):  
SAZIA PARVIN ◽  
FAROOKH KHADEER HUSSAIN ◽  
SONG HAN ◽  
OMAR KHADEER HUSSAIN
Keyword(s):  
Cognitive Radio ◽  
Cognitive Radio Networks ◽  
Spectrum Sharing ◽  
Stochastic Approach ◽  
Radio Spectrum ◽  
Spectrum Management ◽  
Radio Networks ◽  
Secondary Users ◽  
Reliable Technique ◽  
Free Spectrum

Cognitive Radio Networks (CRNs) is a promising technology which deals with shared spectrum access and usage in order to improve the utilization of limited radio spectrum resources for future wireless communications and mobile computing. Security becomes a very challenging issue in CRNs as different types of attacks are very common to cognitive radio technology compared to general wireless networks. The proper working of cognitive radio and the functionality of CRNs relies on the compliant behaviour of the secondary user. In order to address this issue, we propose two approaches in this paper. Firstly, we propose a trust aware model to authenticate the secondary users of CRNs which offers a reliable technique to provide a security-conscious decision by using trust evaluation for CRNs. Secondly, we propose an analytical model for analyzing the availability of spectrum in CRNs using a stochastic approach. We have modeled and analyzed the availability of free spectrum for the usage of secondary users by adopting different activities in a spectrum management scheme to improve the spectrum availability in CRNs.

scholarly journals Spectrum sensing based on adaptive sampling of received signal

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jiawu Miao ◽  
Youheng Tan ◽  
Yangying Zhang ◽  
Yuebo Li ◽  
Junsheng Mu ◽  
...  
Keyword(s):  
Spectrum Sensing ◽  
Adaptive Sampling ◽  
Short Term Memory ◽  
Signal To Noise Ratio ◽  
Sampling Point ◽  
Short Term ◽  
Spectrum Detection ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Sensing Performance

AbstractSpectrum sensing (SS) has been heatedly discussed due to its capacity to discover the idle registered spectrum bands, which effectively alleviates the shortage of spectrum by spectrum reuse. Energy detector (ED) is widely accepted for SS as its complexity is very low. In this paper, an adaptive sampling scheme is proposed to improve the sensing performance of ED, where the sampling point of the received signal is adaptively adjusted with the environment signal-to-noise ratio (SNR). When SNR decreases, the sensing performance can be maintained and even improved by the rise of the sampling point. When SNR increases, the improved ED is considered for idle spectrum detection. The SNR is evaluated based on the joint of convolutional neural network (CNN) and long short-term memory (LSTM) network. Both theoretical derivations and simulation experiments validate the effectiveness of the proposed scheme.

scholarly journals Estimation of Best Spectrum Sensing Concept in Cognitive Radio Networks

2019 ◽  
Vol 8 (12) ◽  
pp. 1159-1163
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cognitive Radio Networks ◽  
Specific Energy ◽  
Spectrum Sharing ◽  
Matched Filter ◽  
Energy Detection ◽  
Radio Networks ◽  
Radio System ◽  
Spectrum Detection

Cognitive radio is a versatile and sharp radio system learning that can naturally recognize accessible divert in a remote range and change correspondence parameters empower more data to run at the same time. Psychological radio is estimated as a point towards which a product characterized radio stage ought to create. The significant elements of CR incorporate Spectrum detecting, Spectrum portability, Spectrum choice, Spectrum sharing. Range detecting frames the base of subjective radios and is one of the principle strategies that empower the intellectual radios to improve the range use. Range detecting is for the most part done in the recurrence and time area. In this paper we will analyze about and investigate four noteworthy range detecting systems to be specific Energy detection, Matched filter spectrum detection, Cyclostationary spectrum detection and Waveform based spectrum detection. In view of the similar outcomes we can appraise the best spectrum detection for remote portable applications

scholarly journals Cognitive Radio Spectrum Sensing Mechanisms in TV White Spaces: A Survey

2018 ◽  
Vol 8 (6) ◽  
pp. 3673-3680
Author(s):  
K. Kimani ◽  
M. Njiraine
Keyword(s):  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Spectrum Sharing ◽  
Limited Resource ◽  
Radio Spectrum ◽  
Spectrum Utilization ◽  
Transmission Parameters ◽  
Wireless Users ◽  
Radio Technology ◽  
Cognitive Radio Technology

Frequency spectrum is a limited resource and the increasing demand caused by emerging services, augmented number of wireless users along with the demand for high-quality multimedia applications have resulted in the overcrowding of the allocated spectrum bands. The overcrowding of spectrum bands has exacerbated by the current spectrum licensing policy which has emerged as a bottleneck to efficient spectrum utilization, due to its inflexibility, resulting in most of the licensed spectrum being severely under-utilized. However, the problem of scarcity of spectrum bands and the inefficient utilization of the already allocated radio spectrum can be smartly addressed through spectrum sharing by enabling opportunistic usage of the underutilized frequency bands. One of the most exciting ways of spectrum sharing is cognitive radio technology which allows a wireless node to sense the environment, detect the network changes, and then make intelligent decisions by dynamically changing its reception or transmission parameters to communicate while ensuring that no interference is affected to the licensed users. It thus improves the spectrum utilization by reusing the unused or underutilized spectrum owned by the incumbent systems (primary systems). In this paper, a comprehensive survey and review of recent research about the advances in cognitive radio technology will be carried out. We will also evaluate the various spectrum sensing techniques in a cognitive radio network in the UHF/VHF bands allocated for TV broadcasting.

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