Cognitive Radio Users Research Articles

SDR implementation of wideband spectrum sensing using machine learning

SummaryNew cognitive radio (CR) systems require high throughput and bandwidth. Hence, CR users need to detect wide frequency bands of the radio spectrum to exploit unused frequency channels. This paper proposes a new wideband spectrum sensing (WBSS) detection approach based on machine learning (ML) for scanning subchannels. The originality of the proposed approach is to detect spectrum opportunities using a narrowband spectrum sensing (NBSS) method‐based support vector machine (SVM) classification and two features: energy and goodness of fit (GoF). The simulation results show that the proposed WBSS approach‐based ML presents a higher probability of detection than the WBSS approach‐based conventional detectors, even at low signal‐to‐noise ratio (SNR). Finally, the software defined radio (SDR) implementation validates the proposed WBSS approach for real detection scenarios.

Secure Radio Resource Management in Cloud Computing using CRN

Limited processing power in Cognitive Radio Networks (CRNs) creates challenges for secure radio resource management, particularly in dynamic spectrum access environments. This paper proposes a novel cloud-assisted CRN framework that addresses this limitation. By offloading spectrum sensing and allocation tasks to the cloud's vast computational resources, CR users can achieve a more secure and efficient spectrum utilization. The cloud platform prioritizes robust security protocols to safeguard spectrum information and communication, ensuring confidentiality, integrity, and user trust. This innovative approach optimizes spectrum usage, improves Quality of Service (QoS) for CR users, and strengthens the CRN's defense against unauthorized access and malicious attacks

Adaptive Combined Channel Network Coding for Cognitive Radio Networks with Cooperative Relay

Cognitive radio is one of the new technologies for 4G/5G applications, so cooperative relay communication and network coding are considered as certain methods to help improve their re-spective applications. A primary broadcast system for multimedia video streaming applications that broad¬casts data to primary users and an auxiliary cooperative relay secondary cognitive ra-dio system are considered. The secondary cooperative overlay system can use multiple error control coding methods for point-to-point data retransmission, such as channel coding, network coding, and combi¬ned coding methods to improve system performance under variable link con-ditions. A new technique of adaptive coding of AC2NC combined channel network for data re-transmission is proposed. The new AC2NC first analyzes the channel feedback information and then selects the best retransmission coding technique based on the target bandwidth or trans-mission time optimization. This is instead of using a single static channel or a network coding technique with dynamic channel conditions. The proposed AC2NC improves system through-put, reduces retransmission time, and provides more spectrum access opportunities for the sec-ondary sys¬tem's own data transmissions. AC2NC's relative throughput and time saving capabil-ities for cognitive radio users have been shown to exceed ninety percent, under certain channel conditions, compared to some static coding methods.

Optimized Cluster-Based Cooperative Spectrum Sensing Over Weibull, Nakagami, Rician, and Rayleigh Fading Channels

This paper reports cluster-based cooperative spectrum sensing (CB-CSS) over Weibull, Nakagami, Rician, and Rayleigh fading channels in cognitive radio networks (CRNs). The performance assessment of centralized cooperative spectrum sensing (CCSS) and CB-CSS with different fusion rules over the Weibull fading channel is presented. The goal is to improve detection performance in a practical and efficient manner for CB-CSS. For this purpose, an expression for the optimal inter-fusion rule threshold (fusion rule between cluster head (CH) and fusion center (FC)) is proposed to minimize total error probability (TEP) over Weibull for CB-CSS. Simulations are conducted using analytical equations for missed detection and false alarm probability. The obtained expression for optimal inter-fusion rule threshold which minimizes TEP is verified by the results. The proposed model is validated with other fading channels such as Nakagami, Rician, and Rayleigh to show the superiority of the approach. It is observed that for a given range of detection threshold and for 4 × 6 cluster size (4 clusters and 6 CR users inside each cluster), the lowest total error probability is achieved over the Weibull fading channel. Minimum TEP over the Weibull fading channel for given network parameters is TEP = 1.656 × 10 − 6 when intra- and inter-fusion rules are l = 1 (OR fusion) and c = 3 . The least number of clusters and users essential to achieving target error bound over different fading channels are also determined.

Robust transmission for multi-user OFDM-based IRS-assisted cognitive radio networks

In cognitive radio networks (CRNs) employing underlay multi-user scenarios, intelligent reflecting surface (IRS) plays a crucial function in augmenting total transmission rate for cognitive radio (CR) users, while adhering to restrictions caused by the interference from secondary users (SUs) to primary users (PUs). However, due to the passive characteristics of IRS, it operates in a non-cooperative relationship with both PU and SU network, resulting in delayed information acquisition and channel errors. Therefore, in this research, considering channel uncertainties, limited interference, and interference among SUs, we put forward a robust power and subcarrier allocation strategy for orthogonal frequency division multiplexing (OFDM) with IRS-assisted CRNs in a multi-user scenario. Formulated as a resource allocation (RA) problem in an underlay spectrum sharing mode for multi-user CR, the joint optimization problem encompasses subcarrier allocation and power allocation for SUs, a phase shift matrix for IRS, and minimum rate constraints. We transform the problem using worst-case criterion into a deterministic convex optimization problem to handle the constraints of bounded channel uncertainties. By utilizing matching theory, variable substitution, scaling methods, convex-concave programming (CCP), and alternating optimization techniques, the converted non-convex problem is transformed into a convex one. Simulation results showcase that the proposed algorithm achieves significant gains in transmission rate and exhibits robustness.

A Unified Rate-Splitting Framework for Secure Spectrum Sharing via Joint Precoding Optimization

To handle the security problem of cognitive radio (CR) systems, secondary users (SUs) are able to be designed to assist the primary user (PU) in secure transmission. To realize the spectrum sharing among CR users, we adopt the rate-splitting multiple access (RSMA) properly, with nonorthogonal multiple access (NOMA) and space division multiple access (SDMA) as its special cases. In this article, we set up a unified rate-splitting framework to guarantee the safe spectrum sharing through multiple multiaccess strategies, without the knowledge of wiretap channels. The precoding vectors at the transmitter are conjointly designed to boost the transmission of common stream, which is from both PU and SU. Besides, the private stream of the downlink CR system can be well secreted in the high-power common signal. As for NOMA and SDMA, when the message of SU is regarded as the friendly jamming signal, the security transmission requirement of PU can be similarly guaranteed via precoding design. Simulations validate that the rate-splitting framework we established can be effective in ensuring the spectrum sharing security for the secure users through RSMA, and the security for NOMA and SDMA is also able to be promoted through adjusting our designed framework.

Joint Multidimensional Pattern for Spectrum Prediction Using GNN.

In general, judging the use/idle state of the wireless spectrum is the foundation for cognitive radio users (secondary users, SUs) to access limited spectrum resources efficiently. Rich information can be mined by the inherent correlation of electromagnetic spectrum data from SUs in time, frequency, space, and other dimensions. Therefore, how to efficiently use the spectrum status of each SU implementation of reception multidimensional combination forecasting is the core of this paper. In this paper, we propose a deep-learning hybrid model called TensorGCN-LSTM based on the tensor data structure. The model treats SUs deployed at different spatial locations under the same frequency, and the spectrum status of SUs themselves under different frequencies in the task area as nodes and constructs two types of graph structures. Graph convolutional operations are used to sequentially extract corresponding spatial-domain and frequency-domain features from the two types of graph structures. Then, the long short-term memory (LSTM) model is used to fuse the spatial, frequency, and temporal features of the cognitive radio environment data. Finally, the prediction task of the spectrum distribution situation is accomplished through fully connected layers. Specifically, the model constructs a tensor graph based on the spatial similarity of SUs' locations and the frequency correlation between different frequency signals received by SUs, which describes the electromagnetic wave's dependency relationship in spatial and frequency domains. LSTM is used to capture the electromagnetic wave's dependency relationship in the temporal domain. To evaluate the effectiveness of the model, we conducted ablation experiments on LSTM, GCN, GC-LSTM, and TensorGCN-LSTM models using simulated data. The experimental results showed that our model achieves better prediction performance in RMSE, and the correlation coefficient R2 of 0.8753 also confirms the feasibility of the model.

SIMULASI DISTRIBUTED BINARY POWER ALLOCATION PADA SISTEM RADIO KOGNITIF

Cognitive radio is a technology for wireless communication networks that employ the upcoming Dynamic Spectrum Access ( DSA ) . DSA technology allows users who do not have a license called the secondary spectrum users to use the licensed spectrum that primary users while not in use. Application of DSA by secondary users to improve the spectrum utilization efficiency without disrupting or causing interference to the primary user. Later developed into a multi - user cognitive radio or cognitive radio networks use frequency spectrum together with a primary user ( PU ).
 Methods switch 'off' the user who gives the biggest interference to PU is the best method with the greatest increase in performance, both the capacity and the number of active SU while the combination of methods switch 'off' the user who raised the largest overall interference with the method switches 'off' user given biggest interference to the PU is an alternative method that shows the consistency of performance improvement in the number of potential users over 10 and able to dominate the entire method over another is begun saturation and decreased capacity.
 Abstracts are made in two languages, English and Bahasa Indonesia. Abstract more about background, purpose, up to, the results of research, and manai research. Abstract contains up to 250 words, single write spaces with italics (Italics) for English abstracts. Below the abstract are listed keywords consisting of six words, where the first word is again the forward. Abstract in Indonesian can be a translation of an English translation. Tiff editor for abstract syncing for reasons of abstract content

Cooperative Spectrum Sensing with Amplify and Forward Scheme in CRNs

Recent research has identified cognitive radio (CR) as a possible solution for increasing spectrum usage by allowing secondary access to unlicensed bands. Having no interference with the primary system is a need for this secondary access. Due to this requirement, spectrum sensing becomes a crucial component of cognitive radio systems. The ease and effectiveness of energy detection make it an appealing technique among popular spectrum sensing techniques. The uses of the available radio network spectrum, finite and important resources, have been severely constrained by the rising demand for wireless applications. It is essential to develop a successful cooperative spectrum sensing (CSS) system for cognitive radio (CR), whose are seen to be a promising solution for improving spectrum usage. A clusters-based optimal selective CSS system is suggested in this paper to shorten reporting times for the bandwidth whiles preserving a given performance by the sensing mode. The clusters have a dynamic head in each selected cluster in accordance with data sensing qualities for CR users in the cognitive radio. Clusters have been arranged based on identifying the major signals to the noise ratio value. Making a decision to use cluster sensing is based on a chosen CSS threshold that minimizes the likelihood of sensing inaccuracy. It takes less time to make the report for the decision of the clusters’ fusion centre is significantly reduced by the Amplify and Forward-based CSS approach that is proposed. The optimal Chair-Varshney rule is applied to the fusion centre for achieving a high sensing performance is depending on the information that is currently available about the cluster. Based on the results availability of simulation, it is clear to propose a way to supersede the other existing methods.

Cardiac Rehabilitation Reduces 2-Year Mortality After Coronary Artery Bypass Grafting

BackgroundCardiac rehabilitation (CR) is a supervised outpatient exercise and risk reduction program offered to patients who have undergone coronary revascularization procedures. Multiple professional societal guidelines support the use of CR after coronary artery bypass grafting (CABG) based on studies in combined percutaneous coronary intervention and CABG populations with surrogate outcomes. This statewide analysis of patients undergoing CABG evaluated the relationship between CR use and long-term mortality. MethodsMedicare fee-for-service claims were linked to surgical data for patients discharged alive after isolated CABG from January 1, 2015, through September 30, 2019. Outpatient facility claims were used to identify any CR use within 1 year of discharge. Death within 2 years of discharge was the primary outcome. Mixed-effects logistic regression was used to predict CR use, adjusting for a variety of comorbidities. Unadjusted and inverse probability treatment weighting (IPTW) were used to compare 2-year mortality among CR users vs nonusers. ResultsA total of 3848 of 6412 patients (60.0%) were enrolled in CR for an average of 23.2 (SD, 12.0) sessions, with 770 of 6412 (12.0%) completing all recommended 36 sessions. Logistic regression identified increasing age, discharge to home (vs extended care facility), and shorter length of stay as predictors of postdischarge CR use (P < .05). Unadjusted and IPTW analyses showed significant reduction in 2-year mortality in CR users compared with CR nonusers (unadjusted: 9.4% reduction; 95% CI, 10.8%-7.9%; P < .001; IPTW: −4.8% reduction; 95% CI, 6.0%-3.5%; P < .001). ConclusionsThese data suggest that CR use is associated with lower 2-year mortality. Future quality initiatives should consider identifying and addressing root causes of poor CR enrollment and completion.

Hermitan matrices based malicious cognitive radio detection and bayesian method for detecting primary user emulation attack

Cognitive radio (CR) is a facilitating technology to efficiently deal with the spectrum scarceness, and it will significantly enhance the spectrum deployment of upcoming wireless transmission method. Security is a significant concern, although not well tackle in cognitive radio networks (CRN). In CR networks, this approach regard as a security issue happen from primary user emulation attack (PUEA). A PUEA attacker forwards an emulated primary signal and defraud the CR users to avoid them from accessing spectrum holes. Here, we introduce a Hermitan matrices based malicious cognitive radio (CMCR) detection and Bayesian method for detecting PUEA attack in the CRN. In this approach, the Bayesian method is used for detecting the PUEA attack. The trust analyzer evaluates the CR trust. Here, the node trust value is computed by node activeness and inactiveness, degree of data transmission, and hermitan matrics verification. In addition, the Hermitan Matrices method is used to detect the malicious CR user in the CRN. The simulation outcomes propose that the CMCR leads to improve the performance in terms of better detection ratio, minimized the possibility of miss detection ratio. Furthermore, it minimized the possibility of false alarm in the CRN.

Deep learning-based selective spectrum sensing and allocation in cognitive vehicular radio networks

The main challenge with Vehicular Ad-Hoc Networks (VANETs) for assisting Intelligent Transportation Services (ITSs) is ensuring effective data delivery under various network circumstances despite the scarcity of radio frequency spectrum channels. Meanwhile, Dynamic Spectrum Access (DSA) utilizing Cognitive Radio (CR) technology shows its potential to solve the channel shortage issue. Reliable Spectrum Sensing (SS) and Opportunistic Spectrum Allocation (OSA) are the two most important and challenging aspects of deciding the success of the deployment of CR-enabled VANETs (CR-VANETs). In this paper, we propose three relevant issues of CR-VANETs in a single framework, i.e., reliable Cooperative Spectrum Sensing (CSS), channel indexing for selective SS, and best channel allocation to the CR users. In CSS, we use the local SS decision with more critical attributes like the geographical position of sensing signal acquisition and timestamp to obtain the global CSS session using the Deep Reinforcement Learning (DRL) technique. Selective channel-based spectrum sensing is essential to minimize the sensing overload by CR users. The present work uses the time series analysis through the deep learning-based Long short-term memory (LSTM) model for indexing the primary user channels for selective SS. Finally, for the channel allocation to the CR-VANETs, we model the complex environment as a Partial Observable Markov Decision Process (POMDP) framework and solve using a value iteration-based algorithm. The simulation results show the proposed work's efficacy over the existing works.

An artificial intelligence‐based spectrum sensing methodology for LoRa and cognitive radio networks

SummaryThe artificial intelligence‐based spectrum sensing approach is extremely important in terms of effective bandwidth utilization for low power wide area networks (LPWANs) based on cognitive radio networks (CRNs). Most studies perform spectrum detection with CRNs using optimization or deep neural network methods. However, optimization‐based spectrum detection approaches based on current LPWANs are scarce. For this purpose, in this study, a hybrid optimization methodology integrated with CRNs is proposed for LoRa, which is one of the most compatible LPWAN technologies in the Internet of Things (IoTs) recently. In the particle swarm optimization (PSO) part of this hybrid methodology, agent users are created so that secondary users (SUs) could use the licensed band of primary users (PUs) in cognitive radio. On the genetic algorithm side, LoRa error rates are minimized in order to further improve the performance of the proposed method. In this way, effective spectrum sensing is performed in the LoRa network. Various LoRa‐CRN experiments have been carried out in the simulation environment, and the probability of detection and false alarm performances have been compared with both theoretical and proposed approaches in terms of quality estimation parameters. It is clear from the results that the proposed methods give successful results for the LoRa‐CRNs.

Employing machine learning based malicious signal detection for cognitive radio networks

SummaryIn cognitive radio networks, the empty spectrum that is also named as spectrum hole is detected with the help of spectrum sensing techniques. Energy detection is the most utilized spectrum sensing technique owing to its low complexity. In the energy detection technique, a spectrum hole is detected with a predefined threshold. In this article, machine learning based malicious signal detection is employed for cognitive radio networks. The design of cognitive radio users and network environment is simulated with Riverbed simulation software. The received signal is controlled whether it is a malicious signal or just a secure sensing signal. The fuzzy logic based system is utilized for the security categorization of spectrum sensing signals as malicious, suspicious, and secure sensing signals. Fuzzy logic parameters are taken from the machine learning features that are chosen as the most effective 3 features among all 49 features. The security of primary users is enhanced when compared to other schemes in the literature. The results of the proposed machine learning based malicious signal detection system are validated with the results acquired from the fuzzy logic based approach. The random forest method gives the best results among all machine learning methods for the detection of signals.

Optimization of inter-fusion rule threshold for energy-efficient cluster-based cooperative spectrum sensing over Nakagami and Rician fading channels

ABSTRACT In cooperative spectrum sensing (CSS), in order to maximise the energy efficiency (EE) of cognitive radio (CR) users, users are divided into clusters. In this paper, the optimal inter-fusion rule threshold formula for different number of clusters and users is proposed for maximum EE in cluster-based CSS (CB-CSS). Different fusion rules are investigated for this purpose over Nakagami and Rician fading channel conditions. Simulations are conducted for different number of clusters and users using AND and OR intra-fusion rules and inter-fusion rule threshold is optimised in CB-CSS over the Nakagami-m fading channel and the Rician fading channel. The obtained mathematical expression for the inter-fusion rule threshold is verified by the results. It is investigated that when number of clusters are and number of users in a cluster are , OR-AND (, ) fusion rule achieves 2.46% more EE than AND-OR (, ) fusion rule in case of Nakagami fading channel and 5.06% more EE than AND-OR fusion rule in case of Rician fading channel. Further, EE with respect to number of users is compared between additive white gaussian noise (AWGN) channel and Nakagami and Rician fading channels.

Cooperative spectrum sensing algorithm based on cognitive users selection and Adaboost

In cognitive radio networks, it is a challenge to increase the performance of cooperative spectrum sensing when the number of cognitive users become large. In this paper, a novel spectrum sensing algorithm based on the cognitive users selection strategy and Adaboost classification is proposed by adaptively selecting uncorrelated cognitive radio users with the difference of the maximum degree based on the K‐medoids clustering. Since the proposed users selection strategy is based on the correlation evaluation among the cognitive radio users, it is mandatory to have parameters able to measure the correlation among them and divide into distinct clusters. For this, the spatial correlation coefficient and the clustering quality coefficient are proposed to express the correlation characteristics of cognitive radio users in sensing environments. Performance evaluation is conducted through simulations, and the results are shown to the superior detection performance of the proposed CR user selection strategy based on the K‐medoids clustering for cooperative spectrum sensing.

Wireless Communication Network Security System Based on Big Data Information Transmission Technology

This paper establishes a unified perception information transmission model for the difference in the number of cognitive radio users in wireless communication network systems. The model can effectively improve the utilization of spectrum resources. At the same time, this paper introduces a cyclic delay full diversity sensing information coding transmission scheme for the security risks of wireless communication networks. We established a system simulation model to compare performance under different correlation coefficients. The study found that the total interference intensity in the cyclic delay diversity system is lower than that of the frequency division duplex and time division duplex. Its antijamming attack capability also becomes stronger accordingly.

Cross Layering using Reinforcement Learning in Cognitive Radio-based Industrial Internet of Ad-hoc Sensor Network

The coupling of multiple protocol layers for a Cognitive Radio-based Industrial Internet of Ad-hoc Sensor Network, enables better interaction, coordination, and joint optimization of different protocols in achieving remarkable performance improvements. In this paper, network, and medium access control (MAC) layer functionalities are cross-layered by developing the joint strategy of routing and effective spectrum sensing and Dynamic Channel Selection (DCS) using the Reinforcement Learning (RL) algorithm. In an industrial ad-hoc scenario, the network layer utilizes the sensed spectrum and selected channel by MAC layer for next-hop routing. MAC layer utilizes the lowest known transmission delay of a channel for a single hop as computed by the network layer, which improves the MAC channel selection operation. The applied RLbased technique (Q learning) enables the CR Secondary Users (SUs) to sense, learn, and make the optimal decision on their environment of operations. The proposed RLCLD schemes improve the SU network performance up to 30% as compared to conventional methods.

Joint bandwidth and power resource allocation technique in multi‐carrier non‐orthogonal multiple access‐based cognitive Internet of Things networks

AbstractThe recent development of the Internet of Things (IoT) devices offers an intelligent concept for integrating massive number of interconnected wireless devices. However, supporting such massive number of IoT connections (hundreds of billions) requires an efficient and dynamic spectrum access strategy. Accordingly, the integration of non‐orthogonal multiple access (NOMA) in the cognitive radio (CR) systems, referred to as a multi‐carrier NOMA CR‐enabled system, is envisioned as a potential spectrum‐efficient networking paradigm that can support massive energy‐efficient IoT connectivity in B5G networks while maintaining the quality of services (QoS) of the IoT devices. However, the power consumption issue of the multi‐carrier NOMA CR‐based system becomes as one of key challenges, especially when considering the expected massive connectivity. Accordingly, several power‐aware optimization frameworks have been considered to address this issue. While most of the existing CR‐based multi‐carrier NOMA power allocation mechanisms only optimize the allocated per‐user power, this article proposes a novel joint bandwidth and power allocation problem over each available idle channel with the aim of minimizing the overall transmission power under a set of QoS constrains. Specifically, the bandwidth of each channel is adaptively subdivided into two variable‐width sub‐channels, each is capable to serve a group of CR users using power‐domain NOMA. This novel design is formulated as a joint optimization problem that is shown to be a non‐convex. Therefore, an iterative algorithm with a second‐order cone programming is deployed to handle the non‐convexity of the problem, and thus, determine the solution of it. Simulation results reveal that the developed variable bandwidth adaptive power allocation mechanism outperforms the conventional equal sub‐channel allocation in terms of the required transmission power, which significantly improves the energy efficiency in the system.

On Sensing Performance of Multi-Antenna Mobile Cognitive Radio Conditioned on Primary User Activity Statistics

In limited space scenarios, the antennas in a multiantenna cognitive radio (CR) system are closely spaced and often experience correlation among them. In this paper, the sensing performance of arbitrarily correlated antennas over the Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading channel for the mobile CR user is analysed. In particular, the analytical expression for the average detection probability for a mobile CR user employing the selection combining with arbitrarily correlated triple diversity branches is derived as a special case. Moreover, to characterize the performance of an energy detector under mobility, the area under the curve of a receiver operating characteristic is analysed. Furthermore, as the sensing decisions can be utilized to improve the sensing performance, a comprehensive analysis of sensing performance of the mobile secondary user conditioned on the primary user (PU) activity statistics is carried out. We assume the PU traffic to be following the Discrete-Time Markov Chain (DTMC) model, and its <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$idle$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$busy$ </tex-math></inline-formula> periods to be following generalised Pareto distribution. The analytical framework is substantiated by Monte Carlo simulations. Results indicate that antenna correlation deteriorates detection performance. Moreover, the detection performance deteriorates further due to the mobility of CR users, especially in the deep fading channel scenarios. Furthermore, findings also suggest that under mobility, the sensing performance improves if the duty cycle of the PU channel is low. This work provides a realistic sensing framework for CR enabled vehicles.