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.

scholarly journals A Tradeoff between Rich Multipath and High Receive Power in MIMO Capacity

2013 ◽  
Vol 2013 ◽  
pp. 1-7
Author(s):  
Zimu Cheng ◽  
Binghao Chen ◽  
Zhangdui Zhong
Keyword(s):  
Channel Capacity ◽  
Channel Model ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Mimo Channel ◽  
Channel Condition ◽  
Multiple Input ◽  
Two Factors ◽  
Input Multiple Output ◽  
Interesting Discussion

A discussion about which of the two factors, rich multipath (in NLOS) or signal-to-noise ratio (SNR) (usually in LOS), affects the Multiple-Input Multiple-Output (MIMO) channel capacity more is presented in this paper. This interesting discussion is investigated by performing simulations using simple circle scatterer model and WINNER II channel model. And the simulation shows that these two factors behave differently as the channel condition varyies. When the scatterer number in channel is low, the high receive SNR is more important to capacity. The multipath richness will have greater influence when the scatterer number exceeds a certain threshold. However, the channel capacity will not change much as the scatterers continue to increase.

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.

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.

scholarly journals Secure Beamforming in 5G-Based Cognitive Radio Network

Symmetry ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1260
Author(s):  
Hyils Sharon Magdalene Antony ◽  
Thulasimani Lakshmanan
Keyword(s):  
Cognitive Radio ◽  
Information Exchange ◽  
Cognitive Radio Network ◽  
Multiple Input Multiple Output ◽  
Primary User ◽  
Radio Network ◽  
Secrecy Rate ◽  
Secondary Users ◽  
Input Multiple Output ◽  
The Impact

Cognitive radio network (CRN) and non-orthogonal multiple-access (NOMA) is a significant system in the 5G wireless communication system. However, the system is an exceptional way for the cognitive users to secure a communication from the interferences in multiple-input multiple-output (MIMO)-NOMA-based cognitive radio network. In this article, a new beamforming technique is proposed to secure an information exchange within the same cells and neighboring cells from all intervened users. The interference is caused by an imperfect spectrum sensing of the secondary users (SUs). The SUs are intended to access the primary channels. At the same time, the primary user also returns to the channel before the SUs access ends. This similar way of accessing the primary channel will cause interference between the users. Thus, we predicted that the impact of interferences would be greatly reduced by the proposed technique, and that the proposed technique would maximize the entire secrecy rate in the 5G-based cognitive radio network. The simulation result provides better evidence for the performance of the proposed technique.

scholarly journals Adaptive Threshold-Aided K-Best Sphere Decoding for Large MIMO Systems

2019 ◽  
Vol 9 (21) ◽  
pp. 4624
Author(s):  
Uzokboy Ummatov ◽  
Kyungchun Lee
Keyword(s):  
Mimo Systems ◽  
Signal To Noise Ratio ◽  
Multiple Input Multiple Output ◽  
Threshold Value ◽  
Adaptive Threshold ◽  
Sphere Decoding ◽  
High Signal ◽  
Signal To Noise ◽  
Multiple Input ◽  
Input Multiple Output

This paper proposes an adaptive threshold-aided K-best sphere decoding (AKSD) algorithm for large multiple-input multiple-output systems. In the proposed scheme, to reduce the average number of visited nodes compared to the conventional K-best sphere decoding (KSD), the threshold for retaining the nodes is adaptively determined at each layer of the tree. Specifically, we calculate the adaptive threshold based on the signal-to-noise ratio and index of the layer. The ratio between the first and second smallest accumulated path metrics at each layer is also exploited to determine the threshold value. In each layer, in addition to the K paths associated with the smallest path metrics, we also retain the paths whose path metrics are within the threshold from the Kth smallest path metric. The simulation results show that the proposed AKSD provides nearly the same bit error rate performance as the conventional KSD scheme while achieving a significant reduction in the average number of visited nodes, especially at high signal-to-noise ratios.

scholarly journals Support Vector Machine-Based Classification of Malicious Users in Cognitive Radio Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Muhammad Sajjad Khan ◽  
Liaqat Khan ◽  
Noor Gul ◽  
Muhammad Amir ◽  
Junsu Kim ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Cognitive Radio ◽  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Learning Algorithm ◽  
Evidence Theory ◽  
Primary User ◽  
Support Vector ◽  
Radio Network ◽  
Secondary Users

Cognitive radio is an intelligent radio network that has advancement over traditional radio. The difference between the traditional radio and the cognitive radio is that all the unused frequency spectrum can be utilized to the best of available resources in the cognitive radio unlike the traditional radio. The core technology of cognitive radio is spectrum sensing, in which secondary users (SUs) opportunistically access the spectrum while avoiding interference to primary user (PU) channels. Various aspects of the spectrum sensing have been studied from the perspective of cognitive radio. Cooperative spectrum sensing (CSS) technique provides a promising performance, compared with individual sensing techniques. However, the existence of malicious users (MUs) highly degrades the performance of cognitive radio network (CRN) by sending falsified results to a fusion center (FC). In this paper, we propose a machine learning algorithm based on support vector machine (SVM) to classify legitimate SUs and MUs in the CRN. The proposed SVM-based algorithm is used for both classification and regression. It clearly classifies legitimate SUs and MUs by drawing a hyperplane on the base of maximal margin. After successful classification, the sensing results from the legitimate SUs are combined at the FC by utilizing Dempster-Shafer (DS) evidence theory. The effectiveness of the proposed SVM-based classification algorithm is demonstrated through simulations, compared with existing schemes.

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>

scholarly journals One-to-Many Relationship Based Kullback Leibler Divergence against Malicious Users in Cooperative Spectrum Sensing

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Noor Gul ◽  
Ijaz Mansoor Qureshi ◽  
Sadiq Akbar ◽  
Muhammad Kamran ◽  
Imtiaz Rasool
Keyword(s):  
Spectrum Sensing ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
Performance Degradation ◽  
Fusion Center ◽  
Secondary Users ◽  
Malicious User ◽  
Leibler Divergence ◽  
Higher Weights ◽  
The Individual

The centralized cooperative spectrum sensing (CSS) allows unlicensed users to share their local sensing observations with the fusion center (FC) for sensing the licensed user spectrum. Although collaboration leads to better sensing, malicious user (MU) participation in CSS results in performance degradation. The proposed technique is based on Kullback Leibler Divergence (KLD) algorithm for mitigating the MUs attack in CSS. The secondary users (SUs) inform FC about the primary user (PU) spectrum availability by sending received energy statistics. Unlike the previous KLD algorithm where the individual SU sensing information is utilized for measuring the KLD, in this work MUs are identified and separated based on the individual SU decision and the average sensing statistics received from all other users. The proposed KLD assigns lower weights to the sensing information of MUs, while the normal SUs information receives higher weights. The proposed method has been tested in the presence of always yes, always no, opposite, and random opposite MUs. Simulations confirm that the proposed KLD scheme has surpassed the existing soft combination schemes in estimating the PU activity.

scholarly journals Hybrid Beamforming Analysis Based on MIMO Channel Measurements at 28 GHz

2020 ◽  
Author(s):  
Joerg Eisenbeis ◽  
Magnus Tingulstad ◽  
Nicolai Kern ◽  
Zsolt Kollár ◽  
Jerzy Kowalewski ◽  
...  
Keyword(s):  
Communication Systems ◽  
Channel Model ◽  
Multiple Input Multiple Output ◽  
Wireless Channel ◽  
Mimo Channel ◽  
Channel Measurements ◽  
Electromagnetic Propagation ◽  
Channel Models ◽  
Input Multiple Output ◽  
Hybrid Beamforming

<div>Hybrid beamforming systems represent an efficient</div><div>architectural solution to realize massive multiple-input multiple-output (MIMO) communication systems in the centimeter wave (cmW) and millimeter wave (mmW) region. These hybrid beamforming systems separate the beamforming process into a digital and analog beamforming network. The analog beamforming networks can be realized by different architectural solutions, which demand dedicated algorithms to determine the complex weighting factors in the digital and analog domain. To date, novel hybrid beamforming architectures and algorithms are solely compared in numerical simulations based on statistical channel models. These abstract channel models simplify the complicated electromagnetic propagation process, thereby not exactly reconstructing the wireless channel. Within this work, we present a measurement-based evaluation of hybrid beamforming algorithms and compare them with numerical results gained from a statistical path-based MIMO channel model. The results show that by adjustment of the channel model parameter the simulation achieves a good match with the measured maximum achievable spectral efficiencies.</div>

scholarly journals Efficient Utilization of Cooperative Spectrum Sensing (CSS) in Cognitive Radio Network (CRN) Using Non-Orthogonal Multiple Access (NOMA)

2021 ◽  
Author(s):  
BALACHANDER T ◽  
Mukesh Krishnan M B
Keyword(s):  
Cognitive Radio ◽  
Wireless Communication ◽  
Spectrum Sensing ◽  
Multiple Access ◽  
Cognitive Radio Network ◽  
Cooperative Spectrum Sensing ◽  
Primary User ◽  
The Novel ◽  
Radio Network ◽  
Secondary Users

Abstract In the recent past, efficient cooperative spectrum sensing and usage are playing a vital role in wireless communication because of the significant progress of mobile devices. There is a recent surge and interest on Non-Orthogonal Multiple Access (NOMA) focused on communication powered by wireless mode. In modern research, more attention has been focused on efficient and accurate Non-Orthogonal Multiple Access (NOMA). NOMA wireless communication is highly adapted with Cognitive Radio Network (CRN) for improving performance. In the existing cognitive radio network, the secondary users could be able to access the idle available spectrum while primary users are engaged. In the traditional CRN, the primary user’s frequency bands are sensed as free, the secondary users could be utilized those bands of frequency resources. In this research, the novel methodology is proposed for cooperative spectrum sensing in CRN for 5G wireless communication using NOMA. The higher cooperative spectrum efficiency can be detected in the presence of channel noise. Cooperative spectrum sensing is used to improve the efficient utilization of spectrum. The spectrum bands with license authority primary user are shared by Secondary Users (SU) by simultaneously transmitting information with Primary Users (PU). The cooperative spectrum sensing provides well under the circumstances that the different channel interference to the primary user can be guaranteed to be negligible than an assured thresholding value. The Noisy Channel State Information (CSI) like AWGN and Rayleigh fading channels are considered as wireless transmission mediums for transmitting a signal using Multiple-Input-Multiple-Output (MIMO) NOMA to increase the number of users. The proposed NOMA is fascinated with significant benefits in CRN is an essential wireless communication method for upcoming 5G technology. From experimental results it has been proved that the novel methodology performance is efficient and accurate than existing methodologies by showing graphical representations and tabulated parameters.

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