Performance Of Cognitive Radio Networks Research Articles

Spectrum and Power Efficient Anti-Jamming Approach for Cognitive Radio Networks Based on Reinforcement Learning

Background: Spectrum scarcity, spectrum efficiency, power constraints, and jamming attacks are core challenges that face wireless networks. While cognitive radio networks (CRNs) enable the sharing of licensed bands when they are unoccupied, the spectrum should be used efficiently by the SU to ensure a high data rate transmission. In addition, the mobility of the secondary users (SUs) makes power consumption a matter of concern in wireless networks. Because of the open environment, the jamming attack can easily deteriorate the performance and disrupt the connections. background: Various anti-jamming schemes have been proposed to mitigate the attacker's impact on Cognitive Radio Networks (CRNs), some of the proposed schemes aim to increase channel capacity or improve spectrum-efficient gain. However, few of them have considered the secondary user's (SU’s) power consumption. Objectives: We aim to enhance the performance of CRN and establish more reliable connections for the SU in the presence of smart jammer by ensuring efficient spectrum utilization and extending the network lifetime. Methods: To achieve our objectives, we propose an anti-jamming approach that adopts frequency hopping. Our approach assumes that SUs observe spectrum availability and channel gain. Then, SU learns the jammer behaviour and goes for the appropriate policy in terms of the number of data and control channels that optimize jointly spectrum efficiency and power consumption. Within, the interaction between the SU and the jammer is modelled as a zero-sum stochastic game, and we employ reinforcement learning to address this game. Results: SUs learn the optimal policy that maximizes the spectrum efficiency and minimizes the power consumption in the presence of a smart jammer. Simulation results show that the low channel gain leads the SU to select a high number of data channels. However, when the channel gain is high, the SU increases the number of control channels to guarantee a more reliable connection. Taking into account the spectrum efficiency, SUs save their energy by decreasing the number of used channels. The proposed strategy achieves better performance in comparison with myopic learning and the random strategy. Conclusion: Under a jamming attack, considering the gain of utilized channels, SUs select the appropriate number of control and data channels to ensure a reliable, efficient, and long-term connection.

AFHO‐based link reliable routing and power‐assisted channel allocation protocol in cognitive radio networks for healthcare applications

AbstractIn the realm of cognitive networks, accommodating the increasing need for radio spectrum to facilitate both secondary and primary users stands as an intricate and demanding task. This study introduces a tailored framework for healthcare applications in Cognitive Radio (CR) networks for link reliable routing and power‐assisted channel allocation. Through the integration of Adaptive Fire Hawk Optimization for routing, the strategy addresses optimization complexities while prioritizing robust link quality via optimal hop selection for effective communication. A new routing optimization challenge is formulated, aiming to establish reliable communication pathways and extend link reliability. Moreover, the framework incorporates power‐assisted channel allocation to strategically enhance resource management and overall network performance. The rigorous evaluation demonstrates significant enhancements across key performance metrics, which showcase percentage improvements of 9.86%, 14.56%, 3.85%, 5.06%, 6.94%, and 17.80% for active nodes, delay, Packet Delivery Ratio (PDR), residual energy, and throughput, respectively. These advancements underscore the proposed approach's efficacy in enhancing CR network performance, particularly for healthcare applications.

Performance analysis of cyclostationary spectrum sensing with dynamic thresholding using artificial neural network under varying signal to noise ratio and noise variance conditions

A key component of cognitive radio technology is spectrum sensing, which finds and accesses unused frequency bands to efficiently use the underutilized spectrum. A potential method for spectrum sensing called cyclostationary feature detection (CFD) uses the cyclostationary characteristics of signals to distinguish between the signal and noise. Artificial neural networks (ANNs) have been suggested in recent years as a method for CFD based spectrum detection, which increases detection accuracy and decreases complexity. However, the variable signal to noise ratio (SNR) and noise variance have an impact on the effectiveness of ANNs for CFD-based spectrum sensing. The effectiveness of ANNs for CFD based spectrum sensing under different SNR and noise variance conditions is evaluated in this work for the determination of threshold value in a dynamic way. We look into how SNR and noise variance affect the precision of probability of detection (Pd) and system complexity. Out analysis show how well ANNs work for CFD based spectrum detection with dynamic threshold value in the presence of changing SNR and noise variation. The findings demonstrate that ANNs may still obtain high Pd values with low SNR and large noise variance while maintaining a modest level of system complexity. According to our research, for a variety of SNR and noise variance situations, ANNs may be a viable option for CFD based spectrum detection in cognitive radio (CR) networks. The proposed approach can significantly improve the detection accuracy and reduce the complexity of the system, thereby enhancing the overall performance of cognitive radio networks. Based on the proposed work, it is determined that MPSK modulation function well with additive white Gaussian noise (AWGN), Rayleigh, and Rician channels up to a lower SNR value of – 30 dB and MQAM supports a lower SNR value of up to – 20 dB.

Secrecy Analysis of Underlay CRN in the Presence of Correlated and Imperfect Channel

The presence of correlated and imperfect channel state information (CSI) affects the secrecy performance of cognitive radio networks (CRN). In this paper, we propose a physical layer security analysis of underlay CRN in terms of secrecy outage probability (SOP), average secrecy rate (ASR), and amount of secrecy loss (ASL). Closed forms of SOP, ASR, and ASL are provided over imperfect CSI of Rayleigh fading environment where the legitimate channel is correlated with the eavesdropper channel. The analytical results are compared with the simulation results. The work is also compared with existing work. It has been observed that the secrecy performance of the network improves as peak interference power increases, and the correlation positively impacts the secrecy performance at a medium-to-high signal-to-noise ratio (SNR); however, it degrades the data security capability of the network at low SNR. Furthermore, asymptotic analysis shows that the secrecy performance of the network improves with the growing correlation between legitimate and eavesdropper channels when the SNR of the legitimate channel is much higher than that of the eavesdropper channel, whereas secrecy performance decreases with a reducing correlation when the SNR of the legitimate channel is approximately equal to SNR of eavesdropper channel.

CR-IOT based selfish attack detection via RSSI-LSTM

Internet of Things-based technologies rely on cooperating between nodes to increase network capacity. A selfish or malicious node is a node that does not cooperate with other nodes in the network. Selfish nodes raise their interests by using facilities provided by other nodes. Selfish cognitive radio attacks significantly degrade the performance of Cognitive Radio networks, which pose a serious security threat and malicious nodes often abuse the network and damage its facilities. To overcome this issue, a novel Cognitive radio on-demand distance vector (CRODV) is proposed. The proposed CRODV technique utilize (Received Signal Strength Indicator) RSSI-based Long Short-Term Memory (LSTM) to detect attacks. This is accomplished by counting the number of active channels to and from the test node in a specific area. If the values don't match, the test node in question is self-centered or introverted. If both values are the same, the test node is normal. Simulations were carried out in MATLAB to test the proposed CRODV. A comparison is made between the suggested framework and existing methods in terms of packet delivery ratio, route dis-connectivity ratio, throughput, detection time, and end-to-end delay. The experimental results demonstrates that the CRODV technique reduce the end-to-end delay by 27.4%, 33.24%, and 40.14% when compared with UKF, HMM, and ALAD techniques.

Security and Energy Management in Cooperative Spectrum Sensing

The revolutionary concept of cognitive radio (CR) has been developed to address the issue of spectrum scarcity in wireless networks. Cooperative spectrum sensing is seen as a promising strategy to dramatically increase spectrum sensing performance in cognitive radio networks, but it is vulnerable to Spectrum sensing data falsification (SSDF) attacks. Existing security references have concentrated on ways to lessen the impact of a SSDF attacking, but these approaches have relied heavily on assumptions such as the attackers being a numerical minority and there being a trustworthy node for data fusion. This realisation inspires us to conduct a thorough examination of the SSDF attack and fusion centre (FC) techniques outside of these constraints. In particular, analysing complex harmful actions allows us to think about a generic SSDF attack model that is more general than the existing methods. According to this overview of attacks, the resultant condition renders the FC clear to attack. Based on these considerations, the ideal attack approach to maximise Bayes risk is examined for both the unknown and known fusion rule scenarios. Second, by casting it as a combinatorial optimisation issue, energy efficiency of cooperative sensing is discussed. In order to enhance the overall energy efficiency of system, a deep learning framework is built based on this formulation by combining graph neural network with reinforcement learning. The efficacy of this method has been demonstrated through simulation tests at varying network scales.

Centralized Monitored Spectrum Management using Multi-resource Parallel Sensing in Cognitive Radio Networks

Spectrum sensing has a very important role in the operation of cognitive radio network. A proper sensing and utilization improve the performance of cognitive radio network (CRN). Different methodologies were developed in past to improve the sensing and utilization performance in CRN. Parallel sensing is used as an optimal approach in spectrum sensing with reduced delay of operation but restraint with multiple requests for spectrum sensing. A random generation of sensing requests results in large time delay and interference in the network. To minimize the interference effect and requesting overhead, this paper presents a new approach of spectrum sensing by centralized monitoring of spectrum engagement of Primary User (PU). The proposed approach operates on a repository monitoring of channel engagement to control the request granting for spectrum sensing in PUs. In addition, the sharing of spectrum usage is controlled by monitoring the spectral efficiency and interference in the network. Simulation results obtained illustrates an enhancement in network throughput, spectrum sensing and spectrum utilization performance where an average network throughput is observed to improve by 80% for varying number of primary channels and varying load conditions. The spectrum sensing is improved by 70% in average for varying primary channels and load conditions and Ideal channel utilization for the proposed approach is observed to improve by 90%.

Unsupervised Learning-Inspired Power Control Methods for Energy-Efficient Wireless Networks Over Fading Channels

Energy-efficiency (EE) is a critical metric within wireless optimization. Power control over fading channels is considered as a promising EE-improving technique, but requires optimization of a series of fractional functional optimization problems which are hard to handle by existing optimization techniques. In this paper, we propose a novel EE power control method with unsupervised learning. Firstly, the original fractional problems are decomposed into sub-problems by Dinkelbach and quadratic transformations. Then, these sub-problems are reformulated into unconstrained forms through Lagrange dual formulation. Furthermore, unsupervised primal-dual learning method is applied to handle these unconstrained problems with strong duality. Finally, The unsupervised primal-dual learning is implemented by the deep neural network (DNN) with low computational complexity. Simulation results verify the effectiveness of the proposed approach on a number of typical wireless optimizing scenarios. It is shown that compared to conventional algorithms our method achieves better performance in cognitive radio networks, interference networks, and OFDM networks.

Performance Improvement of Next-Generation Network with Cognitive Radio Network and Coordinated Multipoint Joint Transmission

Cognitive Radio Network (CRN) is a favourable technology for the future of wireless networks. It aims to seamlessly exploit spectrum resources by opportunistically using the unused or underused radio spectrum that is licensed. Cognitive Radio Network is a technology that is planned to be used in the 5th Generation of wireless communication. This technology is belied to be the answer to the unused and underused radio spectrum in the Radio environment. Whereas Coordinated Multipoint Joint Transmission (CoMP JT) is a technology that has already been used, first used in 3GPP LTE-Advanced. Coordinated Multipoint Joint Transmission aims to improve the performance of the cellular network. In this report, Cognitive Radio Network is being implemented along with Coordinated Multipoint Joint Transmission techniques. An inclusive and layered architecture of Cognitive Radio Network along with Coordinated multipoint Joint Transmission is being designed in this report, showing different technologies and different steps involved in the concept of this combination. Then the impact of CoMP JT on CRN is being studied. By implementing CoMP JT on CRN, it has been able to show how the performance of CRN is changing when implemented alone and when implemented with CoMP JT and some of the significant results are being depicted to better understand the changes in CRN with CoMP JT. Lastly, a study is performed on the combination of Cognitive Radio Network and Coordinated Multipoint Joint Transmission. As far as the investigation is being performed, two factors have been considered, which is: Signal to Interference plus Noise Ratio (SINR) and Total Average Throughput (TAT). The detailed study is being performed in this report and is being implemented in three different environments. Hence the results are being compared and a conclusion is being drawn based on these results.

Outage Performance of Cognitive Radio Networks With a Coverage-Limited RIS for Interference Elimination

In this letter, considering the reconfigurable intelligent surface’ (RIS’) service coverage limitation, we investigate the outage probability (OP) of an RIS-aided cognitive radio network where the RIS is used only to eliminate the interference from the secondary transmitter to the primary receiver. Specifically, a closed-form expression for the OP is derived and the asymptotic OP is obtained at high signal-to-noise ratio (SNR). We also present the asymptotic OP when the number of reflecting elements is large. Simulation results validate our analysis, and show that at low tolerable interference-to-noise ratio (INR), the considered scheme can achieve significant performance gains over the coverage-neglected scheme and the signal enhancement scheme.

On Reliable Key Performance Indicators in Cognitive Radio Networks

Network serveability (NS), which considers both channel availability (CA) and service retainability (SR), is a key indicator to concisely express the performance of cognitive radio networks (CRNs). However, CA cannot guarantee connection setup if receiver’s accessibility ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RxA</i> ) is neglected. Likewise, SR yields imperfect results if <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RxA</i> is ignored. As such, the two rather incomplete performance indicators misrepresent NS and lead to an overestimated satisfaction level of users. Aiming at reliable and concise analysis of CRNs’ performance, this letter introduces the concept of connection availability (CoA), which encompasses both CA and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RxA</i> for a user. Besides, it introduces service maintainability (SM), which incorporates the impact of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RxA</i> into SR. Consequently, the performance is represented more concisely through network serviceability (NeS), a concept we introduce to consider both CA and SM. We show that CoA, SM, and NeS diverge with various proportions from CA, SR, and NS, respectively, under variable traffic loads and channel failure rates. This indicates the degree of performance difference <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RxA</i> introduces, and provides network designers the basis for dependable CRN budgeting.

Performance of cognitive radio networks using reconfigurable intelligent surfaces with RF energy harvesting for Nakagami channels

In this paper, we derive the performance of cognitive radio networks (CRNs) with energy harvesting using reconfigurable intelligent surfaces (RISs) for Nakagami fading channel with m-fading figure M. We derive the detection probability when the primary source (PS) harvests energy using radio frequency (RF) signals. A RIS is located between PS and secondary source (SS) where spectrum sensing is performed. We also derive the primary and secondary throughput and optimise harvesting duration to maximise the throughput. We observed significant performance enhancement in detection probability and throughput with respect to CRN without RIS.

Enhanced Primary User Emulation Attack Inference in Cognitive Radio Networks Using Machine Learning Algorithm

Cognitive Radio (CR) is a competent technique devised to smart sense its surroundings and address the spectrum scarcity issues in wireless communication networks. The Primary User Emulation Attack (PUEA) is one of the most serious security threats affecting the performance of CR networks. In this paper, machine learning (ML) principles have been applied to detect PUEA with superior decision-making ability. To distinguish the attacking nodes, Reinforced Learning (RL) and Extreme Machine Learning (EML-RL) algorithms are proposed to be based on Reinforced Learning (EML). Various dynamic parameters like estimation error, attack detection efficiency, attack estimation rate, and learning rate have been examined with the Network Simulator 2 (NS2) tool.

Evaluation of the performance of a collaborative proposal of multiple access in cognitive radio networks

Cognitive radio networks (CRN) allow for an increase in spectral efficiency and performance of today's wireless networks. Currently, multiple proposals exist in the area of spectral decision-making and mobility; however, very few evaluate the impact of collaboration between secondary users and the performance of spectrum access by many secondary users. Unlike existing works, this article provides a comprehensive quantitative analysis of the performance of CRN taking into account access to the spectrum simultaneously by multiple users and decision making based on collaboration through the exchange of information between nearby secondary users. This proposal is developed through the implementation of four modules: Input Module, Multi-user Module, Collaborative module and Decision-making module, where the results are evaluated comparatively through the handoff rate generated with two multicriteria techniques: Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Multi-Criteria Optimization and Compromise Solution (VIKOR). The evaluation is carried out taking into account three levels of collaboration, three multi-user access scenarios, and two multi-criteria techniques for a total of 18 simulation scenarios. The results obtained show the importance of implementing collaboration strategies, as for multi-user access, the number of handoffs increases as the number of serial users increases. TOPSIS presented the best results in 76 % of the analyzed cases where VIKOR generated a smaller number of handoffs; TOPSIS maintained good performance with differences not exceeding 90 handoffs.

Throughput Maximization of Collaborative Spectrum Sensing Under SSDF Attacks

In cognitive radio networks (CRNs), secondary users (SUs) can opportunistically access the licensed bands through spectrum sensing while primary users (PUs) are inactive. While collaborative spectrum sensing (CSS) exploits the benefits of cooperation, the spectrum sensing data falsification (SSDF) attackers are subsequently introduced, which aim at undermining the performance of CRNs. We thus are motivated to maximize the throughput of the CRNs under SSDF attacks. First, to better discern SSDF attackers, we designate a double reputation based algorithm to differentiate the SSDF attackers and honest secondary users (HSUs). Then the trade off problem is formulated to find the optimal network parameters including, sensing duration, global decision threshold, energy detection threshold, reputation thresholds for maximizing the throughput of the secondary users network (SUN) subject to limited interference with the PUs. To address the non-convexity of the optimization problem, dummy variables are introduced and the alternative optimization method is adopted. Numerical results show that the proposed algorithm is effective in increasing throughput of the SUN with sufficient protection to the PUs.

Channel Selection Algorithm Optimized for Improved Performance in Cognitive Radio Networks

A major concern in the recent past was the traditional static spectrum allocation which gave rise to spectrum underutilization and scarcity in wireless networks. In an attempt to solve this challenge, cognitive radios technology was proposed. It allows a spectrum to be accessed dynamically by Cognitive radio users or secondary users (SU). Dynamic access can efficiently be achieved by making necessary adjustment to some Medium access control (MAC) layer functionalities such as sensing and channel allocation. MAC protocols play a central role in scheduling sensing periods and channel allocation which ensure that the interference is reduced to a tolerable level. In order to improve the accuracy of sensing algorithm, necessary adjustments should be made at MAC layer. Sensing delays and errors are major challenges in the design of a more accurate spectrum sensing algorithm. This study focuses on designing a channel selection algorithm to efficiently utilize the spectrum. Channels are ordered and grouped to allow faster discovery of channel access opportunities. The ordering is based on descending order of channel’s idling probabilities. Grouping of channels ensured that channels are sensed simultaneously. These two techniques greatly reduce delays and maximized throughput of SU. Hence, Extended Generalized Predictive Channel Selection Algorithm, a proposed scheme has significantly performed better than its counterpart (Generalized Predictive Channel Selection Algorithm). Matlab simulation tool was used to simulate and plot the results of the proposed channel selection algorithm.

UAV-Assisted Secure Communications in Terrestrial Cognitive Radio Networks: Joint Power Control and 3D Trajectory Optimization

This paper considers secure communications for an underlay cognitive radio network (CRN) in the presence of an external eavesdropper (Eve). The secrecy performance of CRNs is usually limited by the primary receiver's interference power constraint. To overcome this issue, we propose to use an unmanned aerial vehicle (UAV) as a friendly jammer to interfere Eve in decoding the confidential message from the secondary transmitter. Our goal is to jointly optimize the transmit power and UAV's trajectory in the three-dimensional space to maximize the average achievable secrecy rate of the secondary system. The formulated optimization problem is nonconvex due to the non-convexity of the objective and non-convexity of constraints, which is very challenging to solve. To obtain a suboptimal but efficient solution to the design problem, we first transform it into a more tractable form and then develop an iterative algorithm for its solution by leveraging the inner approximation (IA) framework. Combining tools from IA framework and S-procedure, we further extend the proposed algorithm to a more realistic scenario, where the imperfect location information of ground nodes (including Eve, secondary receiver and primary receiver) is considered, resulting in the average worst-case secrecy rate. Extensive numerical results are provided to demonstrate the merits of the proposed algorithms over existing approaches.

Intelligent Secure Networking in In-band Full-duplex Dynamic Access Networks: Spectrum Management and Routing Protocol

Recent developments in self-interference cancellation (SIC) techniques have made in-band full-duplex (IBFD) transmission systems possible. IBFD technology can enhance, or theoretically speaking double, spectrum utilization by allowing simultaneous transmission and reception using the same radio-frequency channel. Integrating the IBFD technology in cognitive radio (CR) systems can further enhance spectrum utilization and provide huge spectrum opportunities for efficient deployment of next-generation multi-hop wireless systems. Routing protocols for typical CR networks (CRNs) assume that CR users are equipped with half-duplex receivers. Limited number of protocols have been designed for FD-based CRNs. However, these protocols do not account for security threats in wireless environment, which could significantly affect system performance. In this paper, we develop a security-aware intelligent routing scheme that aims at mitigating the effects of jamming attacks on IBFD CRNs. This scheme considers the unique characteristics of the CRN environment while being IBFD-aware. Specifically, it considers the primary user’s channel-availability time, channel quality and jamming strategy. For a source-destination pair, our protocol collects a set of loop-free paths. Then, it attempts to assign channels for the hops along each path with the goal of achieving the highest Probability of Success (PoS) of each path. The per-hop channel selection for each path is derived as an optimization problem, for which sub-optimal solutions are found using a polynomial-time approximate method. Based on the computed channel assignment and PoS over each path, the path with the highest PoS between the source and destination is selected. Simulation results show that our secure-aware IBFD-based routing can significantly mitigate the jamming effects on CRN performance by improving the packet-delivery ratio, and consequently network throughput compared with existing FD-based routing protocols.

A Multi-Objective Particle Swarm Optimization Based Algorithm for Primary User Emulation Attack Detection

The cognitive radio network (CRN) has been proposed to overcome the spectrum scarcity and the massive demand on the radio frequencies through efficient utilization and smart management of the free channels. Using a spectrum sensing process, the secondary users (SUs) share the spectrum of the primary users (PUs) without causing any interference to these latter. One of the main threats affecting the channel utilization in CRN is the primary user emulation (PUE) attack. The PUE attack utilizes a sophisticated technique to craft the same signal of a legitimate PU to gain unauthorized access to the unused channels, forcing the SUs to immediately free up vacant spectrum space, resulting in a denial of service (DoS), degradation of service, and causing a noticeable impact on CRN performance. To circumvent this attack, the anchor nodes must accurately estimate the coordinates of the PU signal source. In the literature, most of the localization of unknown signal source, in our case the PU/PUE, are based on the ranging schemes which measure the distance between the blind node and the anchors. Those anchors are aware of their location and situated in optimized positions to the signal source in order to permit an accurate PU/PUE position detection. The detection rate depends tightly on the distance separating the anchors to the signal source as well as the signal-to-noise ratio (SNR). In this paper, we demonstrate the impact of the distance on the detection error while highlighting the particle swarm optimization’s (PSO) advantage in optimizing the anchors positioning. Furthermore, we illustrate the SNR impact on the probability of detection, particularly in the situation of low SNR and the attacker in the vicinity to a real PU. The main contribution in this work is the proposition of an approach with the aim of protecting an area containing more than a single PU with a limited number of anchor nodes while providing a higher detection rate to stop any eventual PUE attack. This approach is based on a multi-objective particle swarm optimization (MOPSO) algorithm for PU/PUE position detection. It minimizes concurrently the probability of detection error related to the received signal strength (RSS)/trilateration and the SNR, with the main objective of finding all optimized positions for the mobile anchor nodes and obtains the most accurate PUE attack detection.

Performance Analysis of Cognitive Radio Networks With Burst Dynamics

The impact of the traffic characteristics of secondary users (SUs) on the performance of cognitive radio networks (CRNs) should be understood for designing operation rules. This paper focuses on multi-type burst services and network congestion problem in CRNs and evaluates the QoS of SUs based on multiple cross-layer considerations. A two-state Markov-modulate Bernoulli process (MMBP-2) is adopted to model packet flows of SUs with different burst degrees in CRNs. We propose a two-dimensional discrete queuing model to consider spectrum access, burstiness of traffic, network congestion, channel environment, user activity and finite buffer. We construct an iterative algorithm to compute the steady-state distribution of the proposed queuing model and determine the performance metrics like the throughput, the average delay, the average queue length and the total packet loss probability. Numerical analysis evaluates the QoS of SUs under different burst environments.