Users In Cognitive Radio Networks Research Articles

Passive reputation method with hybrid optimized deep model for spectrum sensing in cognitive radio network based on blockchain

SummaryThe discovery of malevolent users in cognitive radio networks (CRNs) is done by using blockchain‐based technique. For diagnosing the availability of frequency resources, the spectrum sensing (SS) plays an essential role in CRN. It is critical to enable cognitive radio (CR) technology in wireless communication systems for the next generation. Due to the increasing demand of the users, some bands are overloaded and others become underutilized. The CR applied models for vigorously assigning unlicensed users. The missed detection problem occurs in the conventional spectrum sensing schemes, which hampers the proper utilization of the spectrum. These motivated the development of a newly devised passive reputation method with an optimized deep model for spectrum sensing in CRN‐based blockchain. The sensing spectrum in CRN is designed using the cooperative spectrum sensing algorithm. The spectrum sensing is done with the decision made using AlexNet where the weights of AlexNet are trained with a hybrid optimization algorithm, named Honey Badger Remora Optimization (HBRO) algorithm. Thus, the output attained from the Alexnet_HBRO algorithm is considered. Meanwhile, the data are recorded in the blockchain, and then, the passive reputation is performed, and the determined output is considered. At last, the spectrum sensing is carried out by fusing AlexNet and passive reputation output where weights are optimally generated using the proposed Snake Honey Badger Remora Optimization (SHBRO) algorithm. The proposed SHBRO offered a high detection probability of 1, false alarm probability of 0.003, sensing time of 551.567s, and computation time of 210.102s.

Efficient spectrum allocation for secondary users in cognitive radio network using round robin priority algorithm along with reservation channels

Efficient spectrum allocation for secondary users in cognitive radio network using round robin priority algorithm along with reservation channels

ASAA: Multihop and Multiuser Channel Hopping Protocols for Cognitive-Radio-Enabled Internet of Things

The devices of Internet of Things (IoT) are integrated and interconnected by using the traditional wireless communication technology. Unavailability of spectrum sharing occurs in traditional wireless communication due to the presence of a massive number of IoT devices. Thus, Cognitive Radio Network (CRN) is introduced as a promising technology to utilize the spectrum efficiently. Channel Hopping Sequence (CHS) is used to establish communication among CRN users. However, the majority of CHS mechanisms only focus on multi-user single-hop scenarios, which can lead to bottleneck and throughput degradation problems. It is also found that few existing CHS mechanisms still have a low percentage of rendezvous that can cause difficulties for the Secondary Users (SUs) to communicate. Thus, it is a challenge to design efficient CHS for establishing fast communication among SUs under multi-user, multi-hop scenarios and asymmetric asynchronous environments. In this paper, Asymmetric Synchronous and Asymmetric Asynchronous (ASAA) Channel Hopping (CH) algorithms are designed for the multi-user CRN-enabled IoT devices to share the unused spectrum in the multi-hop scenario. The Multi-User Asymmetric Synchronous (MUAS) and Multi-User Asymmetric Asynchronous (MUAA) protocols are designed in the proposed ASAA channel hopping mechanism. The simulation results show that the proposed ASAA CHS algorithms outperform the existing CHS mechanisms in terms of throughput, Channel Loading (CL), Channel Utilization (CU), Maximum Time To Rendezvous (MTTR), Average Time to Rendezvous (ATTR) and Maximum Inter Rendezvous Interval (MIRI).

Cooperative spectrum sensing based hybrid machine learning technique for prediction of secondary user in cognitive radio networks

The spectrum scarcity problem in today’s wireless communication network is addressed through the use of a cognitive radio network (CRN). Detection in the spectrum is made easier by cooperative spectrum sensing (CSS), which is a tool developed by the military. The fusion centre receives the sensing information from each secondary user and uses it to make a global conclusion about the presence of the principal user. Literature has offered several different methods for decision making that lack scalability and robustness. CSS censoring is inspected in the attendance of faded settings in the current study. Rayleigh fading, which affects reporting channels (R), is examined in detail. Multiple antennae and an energy detector (ED) are used by each secondary user (SU). A selection combiner (SC) combines the ED outputs with signals from the primary user (PU), which are established by several antennas on SU, before the joint signal is utilised to make a local result. SUs are expurgated at the fusion centre (FC) using a hybrid Support Vector Machine (SVM) that significantly improves detection performance and reduces the number of false positives. With a minimum false alarm probability of 0.1, error rate of 0.04, spectrum utilization of 99%, throughput of 2.9kbps and accuracy of 99%, proposed model attains better performance than standard SVM and Artificial Neural Network (ANN) models.

GRU-SVM Based Threat Detection in Cognitive Radio Network.

Cognitive radio networks are vulnerable to numerous threats during spectrum sensing. Different approaches can be used to lessen these attacks as the malicious users degrade the performance of the network. The cutting-edge technologies of machine learning and deep learning step into cognitive radio networks (CRN) to detect network problems. Several studies have been conducted utilising various deep learning and machine learning methods. However, only a small number of analyses have used gated recurrent units (GRU), and that too in software defined networks, but these are seldom used in CRN. In this paper, we used GRU in CRN to train and test the dataset of spectrum sensing results. One of the deep learning models with less complexity and more effectiveness for small datasets is GRU, the lightest variant of the LSTM. The support vector machine (SVM) classifier is employed in this study's output layer to distinguish between authorised users and malicious users in cognitive radio network. The novelty of this paper is the application of combined models of GRU and SVM in cognitive radio networks. A high testing accuracy of 82.45%, training accuracy of 80.99% and detection probability of 1 is achieved at 65 epochs in this proposed work.

Effectiveness, fairness and social welfare maximization: service rules for the interrupted secondary users in cognitive radio networks

Effectiveness, fairness and social welfare maximization: service rules for the interrupted secondary users in cognitive radio networks

A novel spectrum handoff switching decision scheme for improved performance of secondary users in cognitive radio network

SummarySpectrum handoff (SH) in the cognitive radio network (CRN) is considered as a key challenging area to enhance the performance of secondary users (SUs) in CRN. If the primary user is detected, the SU may pause and stay on the same channel or may perform SH to another idle channel. An accurate and precise handoff decision improves the overall throughput and quality of experience of end‐users. In this paper, we introduce a new SH algorithm and continuous short‐sensing strategy to improve the overall throughput of SUs. In addition, we have derived the minimum length of the target channel sequence based on network‐specific parameters like desired call dropping probability. Further, an optimum channel search time is obtained to minimize the handoff delay. The simulation result shows that the proposed scheme improves the overall throughput of CRN, and the mean opinion score of different video applications increases by 10%, 4.6%, and 1% for rapid motion, gentle walk, and slight motion types of video applications. In the case of VoIP applications, the maximum simultaneous call is improved by 2 times in the case of G.711, 1.72 times in the case of G.729, and 1.66 times in the case of iLBC.

Mitigation of spectrum sensing data falsification attack using multilayer perception in cognitive radio networks

<p>Cognitive radio network (CRN) is used to solve spectrum scarcity and low spectrum utilization problems in wireless communication systems. Spectrum sensing is a vital process in CRNs, which needs continuous measurement of energy. It enables the sensors to sense the primary signal. Cooperative Spectrum Sensing (CSS) has recommended to sense spectrum accurately and to enhance detection performance. However, Spectrum Sensing Data Falsification (SSDF) attack being launched by malicious users can lead to wrong global decision on the availability of spectrum. It is an extremely challenging task to alleviate impact of SSDF attack. Over the years, numerous strategies have been proposed to mitigate SSDF attack ranging from statistical to machine learning models. Energy measurement through statistical models is based on some predefined criteria. On the other hand, machine learning models have low sensing performance. Therefore, it is necessary to develop an efficient method to mitigate the negative impact of SSDF attack. This paper intends to propose a Multilayer Perceptron (MLP) classifier to identify falsified data in CSS to prevent SSDF attack. The statistical features of the received signals are measured and taken as feature vectors to be trained by MLP. In this manner, measurement of these statistical features using MLP becomes a key task in cognitive radio networks. Trained network is employed to differentiate malicious users signal from honest users’ signal. The network is trained with the Levenberg-Marquart algorithm and then employed for eliminating the effect of attacks due to the SSDF process. Once the simulated results are observed, it can be revealed that the proposed model could efficiently reduce the impact of malicious users in CRN.</p>

Efficient power allocation algorithm in downlink cognitive radio networks

In cognitive radio networks (CRNs), the computational complexity of resource allocation algorithms is a significant problem that must be addressed. However, the high computational complexity of the optimal solution for tackling resource allocation in CRNs makes it inappropriate for use in practical applications. Therefore, this study proposes a power-based pricing algorithm (PPA) primarily to reduce the computational complexity in downlink CRN scenarios while restricting the interference to primary users to permissible levels. A two-stage approach reduces the computational complexity of the proposed mathematical model. Stage 1 assigns subcarriers to the CRN's users, while the utility function in Stage 2 incorporates a pricing method to provide a power algorithm with enhanced reliability. The PPA's performance is simulated and tested for orthogonal frequency-division multiplexing-based CRNs. The results confirm that the proposed algorithm's performance is close to that of the optimal algorithm, albeit with lower computational complexity of O M log M .

Ieee Access Special Section Editorial: Reconfigurable Intelligent Surface Aided Communications for 6G and Beyond

Reconfigurable intelligent surface (RIS)-aided wireless communications is a hot research topic in academic and industry communities since it can enhance both the spectrum and energy efficiency of wireless systems by artificially reconfiguring the wireless propagation environment. An RIS can configure tiny antenna elements or scatterers, which can be judiciously tuned to enhance the signal power for desired users, such as primary users in cognitive radio networks, or suppress the signal power for undesired users, such as eavesdroppers for physical layer security. RISs also find promising applications in dense urban areas or indoor scenarios, where the electromagnetic waves are prone to be blocked by obstacles such as buildings and walls. There are numerous advantages associated with RISs. For instance, since an RIS needs no analog-to-digital converters or radiofrequency chains, it reduces the power consumption, hence improving sustainability and reducing cost. RISs can be fabricated at a limited complexity and cost, and they can be easily deployed on buildings facades, walls, ceilings, and streetlamps. Furthermore, RISs can be readily integrated into current wireless networks (both cellular networks and WiFi). Due to these appealing advantages, RIS is envisioned to be one of the key technologies for the sixth-generation (6G) wireless networks.

Implementation of cognitive radio networks for optimum spectrum utilization through feed forward backpropagation artificial neural network

In wireless Radio Frequency (RF) networks, a huge amount of available bandwidth of the RF spectrum is wasted, because the users are not able to fully utilize the frequency band of the spectrum. The Cognitive Radio Networks provide an effective solution for optimum utilization of the frequency band of the RF spectrum. Cognitive Radio Networks consist of two types of users: primary users, which utilizes their allocated license frequency band of the RF spectrum and secondary users, which uses the license frequency band of the primary users when they are not using the RF spectrum. Primary users pre-empt the secondary users when they want to use the RF spectrum. To know when the spread spectrum is not occupied by the primary users, the secondary users must have some mechanism to sense the environment, learn the parameters and take a decision on the basis of it. The users of Cognitive Radio Networks enhance these capabilities by using Artificial Neural Networks (ANN). This paper discusses how the presence of primary users in the particular frequency band of the spectrum could be detected by the secondary users by using Feed Forward Back Propagation ANN. For this a CRN simulation model is designed and developed which uses a Feed forward back propagation ANN for optimum use of the spectrum.

Clusters-based rendezvousing approach for scheduling the flash crowd transmissions over cognitive radio networks

Rendezvousing the transmissions and radio frequencies is a crucial objective to achieve optimal utilisation of the spectrum bands by secondary users in cognitive radio networks. Many dimensions of research, such as attaining quality of service during transmissions occurring through spectrum bands, optimising security factors to enable fair and trusted transmissions over spectrum bands is happening in the recent past. However, optimising the spectrum band utilisation by rendezvousing the transmissions and radio frequencies of spectrum band as transmission channels is essential against the massive transmissions (flash crowd) framed by multiple users. Concerning this, a batch processing approach called 'clusters-based rendezvousing approach for scheduling the flash crowd transmissions over cognitive radio networks' has proposed in this manuscript. A new distance measuring technique has adapted to perform a clustering process, which has applied on both buffered transmissions and idle radio frequencies. The results obtained from an experimental study indicating the comparatively better performance from the proposed model and, if utilised within the definitive frameworks, can yield better performance results.

Energy conserving relay assistance for reporting users in cognitive radio networks

Energy conserving relay assistance for reporting users in cognitive radio networks

Stackelberg Game Theory for Rectifying Mutual Interferences among Secondary Users of Cognitive Radio Network in Comparison with Cournot Game Theory

Aim: The aim of the study is to solve the problem of mutual interference among SUs based on Stackelberg game theory. Materials and Methods: This paper proposes an innovative algorithm based on Stackelberg game theory to reduce interference among secondary users. MATLAB software is used to implement the algorithm. The algorithm is tested to find Signal to Interference noise ratio (SINR) of about 10 secondary users in CRN using novel Stackelberg game theory and compared with the SINR of Secondary users using Cournot game theory. Results: The Statistical analysis is done by performing Independent Variable test and T-test using SPSS software. The mean SINR is maximum for Stackelberg game theory (100.9±1 mdb), and the least SINR (13.11±1mdb) is seen for Cournot game theory. Conclusion: The algorithm based on Stackelberg game theory shows maximum SINR than the Cournot game theory. Since SINR is maximum Signal quality will be high and Interference will be less among the users due to which the network performance is improved.

Design and implementation of a novel two-phase spectrum handoff scheme for QoS aware mobile users in cognitive radio networks

Quality of Service (QoS) aware spectrum handoff operation is critical for Secondary Users (SUs) executing real-time applications like Voice over IP (VoIP) in Cognitive Radio Networks (CRNs). The problem is aggravated by the inclusion of occasionally mobile SUs where pre-selected target channels are often rendered insignificant during handoff under dynamic conditions. Conventional proactive and reactive schemes may underperform due to channel obsolescence issues and unacceptable handoff latency respectively while a combined approach is not yet explored under the constraints of practical applicability. In view of these challenges and enormous significance of VoIP based CRN in 5G networks, this paper proposes a practically feasible two-phase spectrum handoff methodology with I_Phase and S_Phase operating under alternate conditions. I_Phase deploys a three-level dropping decision scheme followed by a two-tier spectrum handoff policy and is motivated by an integrated approach spanning every CR operation. S_Phase incorporates user mobility and application-oriented aspects where channel drop is governed by a joint two-order Hidden Markov Model — custom Damerau–Levenshtein distance metric based policy and spectrum handoff is monitored by a novel non-cooperative TOPSIS method. These phases are assisted by two proposed prerequisite techniques namely ACT (Adaptive Call Transmission) involving CR timing parameters and VER (VoIP Early Resumption) dealing with early call resumption. Comparative analysis of QoS metrics including per-instance handoff latency, transmission duration per target channel, channel drop and call drop probabilities, etc. in analytical and simulation platforms record significant performance improvement. Novelty of this work further relies on design and execution of SU prototype with the proposed scheme in test-bed that successfully provides QoS guarantees during spectrum handoff.

On the Retrial-Queuing Model for Strategic Access and Equilibrium-Joining Strategies of Cognitive Users in Cognitive-Radio Networks with Energy Harvesting

This article studies the strategic access of single-server retrial queue with two types of customers, where priority is given according to their category. On the basis of this concept, a cognitive-radio network was developed as retrial queue with energy harvesting. Cognitive radio allows for a secondary user to opportunistically access the idle spectrum of a primary user (PU). Upon arrival of a primary user, the service given to the secondary user by the cognitive radio is interrupted, and the PU band is available for the primary user. After completion of service for the primary user, the PU band is again available to secondary users. Performance metrics are derived to study the equilibrium strategies of secondary users. A Stackelberg game was formulated and Nash equilibrium was derived for the noncooperative strategy of the secondary user. Game-theory concepts are incorporated with queuing theory ideas to obtain the net benefit for the noncooperative strategy and social benefit for cooperative strategy. Lastly, analytical results are verified with numerical examples, and the effects of energy-harvesting rate are discussed.

Stackelberg Game Theory for Rectifying Mutual Interferences Among Secondary Users of Cognitive Radio Network in Comparison with Cournot Game Theory

Stackelberg Game Theory for Rectifying Mutual Interferences Among Secondary Users of Cognitive Radio Network in Comparison with Cournot Game Theory

A Deep Learning-Based Power Control and Consensus Performance of Spectrum Sharing in the CR Network

The cognitive radio network (CRN) is aimed at strengthening the system through learning and adjusting by observing and measuring the available resources. Due to spectrum sensing capability in CRN, it should be feasible and fast. The capability to observe and reconfigure is the key feature of CRN, while current machine learning techniques work great when incorporated with system adaption algorithms. This paper describes the consensus performance and power control of spectrum sharing in CRN. (1) CRN users are considered noncooperative users such that the power control policy of a primary user (PU) is predefined keeping the secondary user (SU) unaware of PU’s power control policy. For a more efficient spectrum sharing performance, a deep learning power control strategy has been developed. This algorithm is based on the received signal strength at CRN nodes. (2) An agent-based approach is introduced for the CR user’s consensus performance. (3) All agents reached their steady-state value after nearly 100 seconds. However, the settling time is large. Sensing delay of 0.4 second inside whole operation is identical. The assumed method is enough for the representation of large-scale sensing delay in the CR network.

Deep Q-learning-based resource allocation for solar-powered users in cognitive radio networks

Abstract This paper considers uplink solar-powered cognitive radio networks (CRNs) where multiple secondary users (SUs) transmit data to a secondary base station (SBS) by sharing a licensed channel of a primary system. A deep Q-learning (DQL) algorithm, which combines non-orthogonal multiple access (NOMA) and time division multiple access (TDMA) techniques, is proposed to maximize the long-term throughput of the system. By using our scheme, the agent (i.e. the SBS) can obtain the optimal decision by interacting with the environment to learn about system dynamics. Simulation results validate the superiority of the performance under the proposed scheme, compared with traditional schemes.

Supporting Real-Time Data Transmissions in Cognitive Radio Networks Using Queue Shifting Mechanism

As cognitive radio networks are conceptualized to make use of the opportunistic spectrum access, the users of these networks may face problems in satisfying their quality of service (QoS) requirements. Some services of users like real-time audio and video which cannot tolerate inter-packet delays will be affected more due to this. The problem occurs due to the non-availability of channels to these applications at some instants. This problem can be addressed if the available channels are judiciously distributed among the competing users. One such mechanism that dynamically allocates the competing users to multiple queues, and shifting the users to higher-level queues as the time elapses is introduced in this work. This is found to help the users of cognitive radio networks to communicate reasonably well even when fewer channels are available for opportunistic use. Results are indicated in terms of blocking probabilities of real-time data. Markov chain-based analysis and discrete event simulation studies are carried out.