Approach For Cognitive Radio Networks Research Articles

IRS assisted spectrum sensing in cognitive radio network with grey wolf optimization

Cognitive radio (CR) is of crucial importance in providing efficient management of limited spectrum resources. However, its performance relies on efficient spectrum sensing. This paper investigates a novel approach for CR networks that leverages intelligent reflecting surface (IRS) specifically for spectrum sensing and non-orthogonal multiple access (NOMA) for data transmission. We propose a Grey-Wolf Optimization (GWO) based IRS optimization approach to maximize spectrum sensing performance. Independent of the IRS, NOMA is employed to improve spectral efficiency during data transmission. The performance is evaluated in terms of throughput and spectrum sensing parameters, namely probability of false alarm and missed detection. Numerical and simulation results demonstrate that GWO-based IRS optimization significantly outperforms conventional nature-inspired algorithms, achieving approximately 97% improvement in spectrum sensing accuracy. Based on the improved spectrum sensing results, the effective data transmission throughput is evaluated and validated through extensive simulation.

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.

Adaptive fusion based clustering approach in cognitive radio networks

Recently, the cognitive radio (CR), which is a technology to effectively use the radio band, has been introduced to allow unlicensed users to use the spectrum efficiently. The key problem of the CR system is the overhead issue in control channel as the number of secondary users (SUs) is very high in the cooperative spectrum sensing (CSS) scenario. To alleviate this problem, the Cluster-based CSS solution is useful to prevent crowding on the control channel. Therefore, this paper uses a neural network (NN) approach based on a supervised learning algorithm at the fusion center (FC) for the suggested clustering technique. Scheme suggests a combination of hard decisions, using NN approach to increase the efficiency of CRNs with multiple clusters and to decrease the overhead issue between the FC to cluster head (CH). Also, under the Rayleigh fading channel, the proposed scheme is examined and simulated to test their detection efficiency and compare their performance with conventional schemes. The simulation results prove that proposed clustering approach enhances the accuracy of the system. Also, it is more robust than other conventional schemes.

Adaptive cluster‐based heuristic approach in cognitive radio networks for5Gapplications

AbstractThe main objective of cognitive radio network is to provide flexible spectrum management, by permitting the secondary users (SUs) to temporarily access the licensed spectrum in the absence of a primary user. In the existing backward induction approach, each SU performs spectrum sensing and consequently reports the sensing details to the centralized controller. In the proposed work, an adaptive cluster‐based heuristic approach (ACBHA) with cooperative spectrum sensing (CSS) scheme has been proposed for cognitive radio medium access control networks for 5G applications. To identify the availability of unused spectrum, the proposed CSS‐based ACBHA algorithm adopts a cluster‐based selection scheme. Simulation results show that the proposed CSS‐based ACBHA provides a better result compared with existing techniques. The performance parameters such as false alarm probability, the probability of detection, spectrum sensing time, slot length, noise density have been analyzed using MATLAB R2012a.

A Cooperative Spectrum Sensing With Multi-Agent Reinforcement Learning Approach in Cognitive Radio Networks

Cognitive radio networks (CRNs) can greatly improve the temporal and spatial spectrum utilization by identifying and exploring spectrum holes of the licensed primary users (PUs). However, since the occupation of primary channels changes dynamically, a swift and accurate spectrum sensing is crucial especially in the multi-channel multi-secondary users (SUs) environment, where the number of channels is much larger than that of SUs. To improve the sensing accuracy, a cooperative sensing algorithm is proposed in this letter, where multiple SUs can share their spectrum detection results for a more effective spectrum holes search. This letter further employs multi-agent deep deterministic policy gradient (MADDPG) algorithm with the feature of centralized training and decentralized execution to reduce the synchronization and communication overhead caused by the sensing cooperation of SUs. The numerical simulation demonstrates that with the combination of cooperative sensing and multi-agent reinforcement learning, the proposed algorithm can greatly enhance the sensing accuracy in comparison to other non-cooperative learning or centralized learning approaches.

Deep Transfer Cooperative Sensing in Cognitive Radio

In this letter, we propose a deep transfer cooperative sensing (DTCS) approach in cognitive radio networks, where multiple secondary users (SUs) cooperate to detect the presence of signals from a primary user (PU) in a shared frequency band. DTCS is a cooperative spectrum sensing (CSS) framework based on unsupervised deep transfer learning. It operates on energy vectors, whose each element is a sensing result by an energy detector from individual SU. It learns the knowledge by combining the sensing results from all SUs in one radio frequency environment and transfers it to another one. This approach is applicable for detecting the presence of arbitrary unknown signals, which enhances the generalization ability and robustness of the framework. Simulation results demonstrate the effectiveness of DTCS.

Review of Opportunistic Spectrum Access Approach in Cognitive Radio Networks

Review of Opportunistic Spectrum Access Approach in Cognitive Radio Networks

Energy aware resource allocation and complexity reduction approach for cognitive radio networks using game theory

Nowadays, the demand for mobile wireless communication systems has increased drastically due to its significant use for various real-time applications. This increased demand for communication causes heavy utilization of the radio spectrum to improve ubiquitous computing services. However, systems providing high-speed communication fail to achieve the desired performance due to unsystematic spectrum utilization and resources. The problem addressed by Cognitive Radio Network (CRN) architecture has attracted research and industrial community to enhance the real-time communication systems. Although CRN based real-time communication systems suffer from resource allocation, spectrum sensing, and power consumption issues. In this paper, we introduce a novel approach for resource allocation and sharing based on cooperative game theory, and cooperative node selection ensures maximized payoff. The proposed method optimizes the overhead, energy consumption, and resource utilization. Further, energy consumption and resource allocation issues transformed into an optimization problem. A backtracking search algorithm is applied to reduce the computation complexity and to find the optimal solution for resource utilization. The simulation result obtained achieves better performance compared to the existing energy-aware scheduling approach in CRN.

A Software Defined Radio Cross-Layer Resource Allocation Approach for Cognitive Radio Networks: From Theory to Practice

Software Defined Radio (SDR)-enabled cognitive radio network architectures are expected to play an important role in the future 5G networks. Despite the increased research interest, the current implementations are of small-scale and provide limited functionality. In this paper, we contribute towards the alleviation of the limitations in SDR deployments by developing and evaluating a resource allocation approach for cognitive radios implemented with SDR technology over two testbeds of the ORCA federation. Resource allocation is based on a Markov Random Field (MRF) framework realizing a distributed cross-layer computation for the secondary nodes of the cognitive radio network. The proposed framework implementation consists of self-contained modules developed in GNU Radio realizing cognitive functionalities, such as spectrum sensing, collision detection, etc. We demonstrate the feasibility of the MRF based resource allocation approach and provide extensive results and performance analysis that highlight its key features. The latter provide useful insights about the advantages of our framework, while allowing to pinpoint current technological barriers of broader interest.

Mitigating SSDF attack using distance-based outlier approach in cognitive radio networks

Collaborative spectrum sensing is employed in cognitive radio networks for improving the spectrum sensing accuracy. The collaborating cognitive radios send their individual sensing results to the f...

Mitigating SSDF Attack using Distance-based Outlier approach in Cognitive Radio Networks

Collaborative spectrum sensing is employed in cognitive radio networks for improving the spectrum sensing accuracy. The collaborating cognitive radios send their individual sensing results to the f...

Spectrum sensing in cognitive femtocell network based on near‐field source localisation using genetic algorithm

Cognitive femtocell has emerged as a promising technique for indoor users in wireless communication systems since they opt the cognitive and self-configuration capabilities. Spectrum sensing is a non-trivial approach in cognitive radio networks. A number of methods have been used for spectrum sensing in femtocells with a cognitive engine. In this work, the authors have proposed a near field source localisation technique through which the cognitive femtocell will be able to detect the active femtocells in the licensed band and will get additional information including the range, amplitude, angle and operating frequency of a certain femtocell.

Collision avoidance in TV white spaces: a cross-layer design approach for cognitive radio networks

One of the most promising applications of cognitive radio networks (CRNs) is the efficient exploitation of TV white spaces (TVWSs) for enhancing the performance of wireless networks. In this paper, we propose a cross-layer design (CLD) of carrier sense multiple access with collision avoidance (CSMA/CA) mechanism at the medium access control (MAC) layer with spectrum sensing (SpSe) at the physical layer, for identifying the occupancy status of TV bands. The proposed CLD relies on a Markov chain model with a state pair containing both the SpSe and the CSMA/CA from which we derive the collision probability and the achievable throughput. Analytical and simulation results are obtained for different collision avoidance and SpSe implementation scenarios by varying the contention window, back off stage and probability of detection. The obtained results depict the achievable throughput under different collision avoidance and SpSe implementation scenarios indicating thereby the performance of collision avoidance in TVWSs-based CRNs.

A new SIR-based sigmoid power control game in cognitive radio networks.

Interference resulting from Cognitive Radios (CRs) is the most important aspect of cognitive radio networks that leads to degradation in Quality of Service (QoS) in both primary and CR systems. Power control is one of the efficient techniques that can be used to reduce interference and satisfy the Signal-to-Interference Ratio (SIR) constraint among CRs. This paper proposes a new distributed power control algorithm based on game theory approach in cognitive radio networks. The proposal focuses on the channel status of cognitive radio users to improve system performance. A new cost function for SIR-based power control via a sigmoid weighting factor is introduced. The existence of Nash Equilibrium and convergence of the algorithm are also proved. The advantage of the proposed algorithm is the possibility to utilize and implement it in a distributed manner. Simulation results show considerable savings on Nash Equilibrium power compared to relevant algorithms while reduction in achieved SIR is insignificant.

An urn occupancy approach for cognitive radio networks in DTVB white spaces

The paradigm of cognitive radio recently received considerable interest to address the so called `spectrum scarcity' problem. In the USA, the Federal Communications Commission issued the regulatory for the use of cognitive radio in the TV white space spectrum. The primary objective is the design of cognitive devices able to combine the use of spectrum sensing and GEO-location information with the concept of the cognitive control channel to manage the cognitive devices. The recent standard ECMA-392 defines physical layer techniques and medium access control protocols to enable a cognitive network managed in a fully distributed fashion. In this work, we pursue the design of an efficient medium access control protocol for the cognitive control channel to flexibly and reliably exchange messages inside the cognitive radio network. In particular, we explore how the cognitive devices can raise their awareness of spectrum vacancies of spectrum vacancies by means of sensing when the distributed beaconing defined by ECMA-392 is used. Our main contributions are the following: (1) we propose a proprietary medium access control protocol based on the Standard ECMA-392; (2) we model the behavior of the cognitive radio network by means of an innovative urn model approach, (3) we investigate the access of the cognitive devices to the frequency channels with and without spoofing attacks and (4) we investigate the ability of the cognitive devices to identify frequency holes accounting for perfect and imperfect spectrum sensing, as well as we study the network throughput.

Wideband Spectrum Sensing With Sub-Nyquist Sampling in Cognitive Radios

Multi-rate asynchronous sub-Nyquist sampling (MASS) is proposed for wideband spectrum sensing. Corresponding spectral recovery conditions are derived and the probability of successful recovery is given. Compared to previous approaches, MASS offers lower sampling rate, and is an attractive approach for cognitive radio networks.

A bio-inspired approach for cognitive radio networks

The recent increasing interest in cognitive radio networks has motivated the study and development of new approaches capable of coping with the intrinsic challenges of this kind of network, such as dynamic spectrum availability, distributed and heterogeneous network architectures, and soaring complexity. The bio-inspired approaches, with appealing characteristics such as autonomy, adaptation and collective intelligence of collaborative individuals, have been extensively studied. This paper presents a comprehensive survey of bio-inspired approaches for cognitive radio networks, emphasizing their domains of application. Specifically, ant colony optimization and particle warm optimization are further investigated with examples and numerical simulation.

Bio-inspired distributed beamforming for cognitive radio networks in non-stationary environment

Distributed power control by beamforming approach in cognitive radio networks requires a precise analysis of the impacts of the transmission parameters, tolerable interference and guarantees the quality of service of both the primary users and secondary users. In this paper, we propose an improved performance to solve the constrained nonlinear multi-object optimization and coherent power assignment by beamforming problem based on particle swarm optimization. This method is invoked to solve the constrained nonlinear optimization problem in order to reach to maximum capacity. A numerical study is performed to show the convergence behavior of the proposed algorithm and the efficiency of the proposed technique with a dynamic cost function.