Cognitive Radio Network Environment Research Articles

A Binary Artificial Rabbit Optimization Algorithm for Improved Cognitive Radio Spectrum Allocation

A binary artificial rabbit optimization algorithm is proposed to maximize the cognitive radio (CR) network performance and fairness among users by addressing the issue of low spectrum resource utilization during the CR spectrum allocation process. Building upon the graph coloring spectrum allocation model, Sigmoid is introduced to transform the algorithm solution space. Incorporating the Lévy flight strategy for adaptive step size adjustment enhances the algorithm’s flexibility and convergence accuracy. Moreover, a selective opposition strategy based on Spearman’s coefficient is integrated into the algorithm to improve population diversity and convergence accuracy through reverse learning. Applying the binary artificial rabbit optimization algorithm to the CR spectrum allocation problem in the same CR network environment, we compare network efficiency and inter-user fairness through simulation experiments with the artificial rabbits optimization algorithm seagull optimization algorithm and particle swarm optimization algorithms. The experimental results show that the binary artificial rabbit optimization algorithm has higher convergence performance and global exploration ability, improves the overall network efficiency and user fairness of CR networks, and alleviates the problem of low spectrum resource utilization.

Analysis of Secure Cognitive Radio Network with Optimal Resource Management

During the last decade, Cognitive Radio Network (CRN) technology has been a significant advance in addressing the ever-increasing spectrum demand. As the number of licensed and unlicensed users in a network rises to complete a certain activity, the information exchange between various types of traffic becomes more complex and difficult. Congestion in CRN is also caused by the conflict among several users for channel access (PUs and SUs). In a very crowded network, many applications perform badly owing to packet collisions and, as a result, packet loss before significant buffer queue building. This circumstance is aggravated by an increase in network users. Congestion control is a vital and essential aspect of the present research issue in communication networks. Several recent reviews in the literature indicate that the congestion issue in the CRN has not been thoroughly studied. Thus, effective and efficient congestion control strategies are sought to optimize network resource usage and management. To prevent congestion, it is crucial for CRN to do research on the creation of an efficient congestion management system. This will improve the network's resource consumption and performance. "Performance improvement via efficient spectrum management through optimum resource management and congestion control in the CRN by mitigating different threats" is the primary target of this project. This research also focused on enhancing performance by addressing security issues in an IoT-based CRN environment. This study provides a comprehensive review of several similar studies and their limitations, which may be used to formulate a new research target.

Group mobility assisted network selection framework in 5G vehicular cognitive radio networks

The heterogeneity nature of networks is the most eminent characteristic in 5G vehicular cognitive radio networks across complex radio environments. Since multiple communicating radios may be in motion at the same time in a vehicle. So, group mobility is the most prominent characteristic that requires to be a deep investigation. Therefore, different communication radios that are moving on a train/bus needed to select the networks simultaneously. Without considering the group mobility feature, there is a possibility that the same network may be selected by each moving node and cause congestion in a particular network. To overcome this problem, a novel network selection technique considering the group mobility feature is proposed to improve the throughput of the network. In this work, a 5G vehicular cognitive radio network scenario is also realized using USRP-2954 and LabVIEW communications system design suite testbed. The performance metrics like transmission delay, packet loss rate, reject rate and, channel utilization for vehicular nodes, are gained to analyze the proposed technique in vehicular cognitive radio networks environment. The proposed technique demonstrates a remarkable improvement in channel utilization for vehicular nodes and outperformed conventional schemes.

Performance Analysis of FISSER Model in Rural-Urban Cognitive Radio Networks

In the past few decades, Cognitive Radio network (CRN) has been regarded in the literature as the most promising technology for performing dynamic spectrum management. One of the major aspects of spectrum management is referred to as the spectrum-reconfiguration decision-making ability of CR users. Dynamic spectrum reconfiguration has previously been reported to improve the spectrum utilisation in CRN. In exploiting spectrum reconfiguration to improve spectrum utilisation, various approaches have been adopted to develop models. However, none of these research works has been evaluated in the urban and rural settlement context. In a CRN environment, the frequency availability is not static like that of traditional networks. There are resource-rich regions such as in rural areas, where many frequencies are available for the Secondary Users (SUs) and resource-poor regions such as urban areas, where there are only a few frequencies available for SUs. Hence, this paper proposed a novel Foraging Inspired Spectrum Selection and Reconfiguration (FISSER) model. The performance of the FISSER model has been analysed through computer simulations both in the rural and urban CRN, using high and low perceptual radii. A high perceptual radius has been shown in the literature to improve energy efficiency and data communication performance in ad hoc networks. However, the simulation results reported in this paper show that even though the high perceptual radius improves the nodes’ energy efficiency it does not achieve optimal data communication performance compared to when the nodes use a low perceptual radius. Also, the efficacy of FISSER model was tested by comparing it against the reinforcement learning model. Based on the obtained results, it was shown that the FISSER model achieved better results in both the network throughput and the channel switching time.

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.

Application aware networks' resource selection decision making technique using group mobility in vehicular cognitive radio networks

Nowadays detonation of holding vehicle has wrought overcrowded traffic. The US Federal Communication Commission officially allocated nearly 75 MHz spectrum in 5.8 GHz band to support vehicular communication and many studies found this band incapable to handle the demand for future data traffic. The application of Cognitive Radio (CR) technology will be utilized to make the Internet of Vehicles (IoV) environment in order to increase the spectrum resource opportunities available for vehicular communication. In the CR networks environment, each network supporting multiple attributes likes mobility, Quality of Service, data rates and bandwidth. Therefore, new challenges including link failures, network volatility, group mobility and decision making for the fair selection of optimal networks' resource in vehicular CR networks have become the focus of the global concern. Contrarily, future applications require the vehicular CR networks to support safety and non-safety applications and services. The contribution of this article is two folds. First, the group mobility feature of vehicular communication is considered in this work and two techniques (GRA based Vehicular CR node Assisted Networks' resource Selection (VANS) and Network Assisted Networks' resource Selection (NANS)) for vehicular CR networks are proposed. Second, a testbed using coded scripting programme and LabVIEW communications system design suite software to universal software radio peripheral (USRP-2954) is considered to analyze the practical realization issues in vehicular CR networks. Furthermore, the performance of the proposed techniques is analyzed and validated through experimental testbed and compared with the conventional decision-making techniques. Results show that the proposed techniques have outperformed traditional techniques.

Wireless transport protocol variants for cognitive radio networks

Scarce wireless resources, lead to development of cognitive radio network as a solution to unlicensed users communication in the licensed frequency band. In response to the behavior of licensed users communication, unlicensed users communication need to change from one frequency band to another band. In this communication paradigm, the performance of unlicensed users transmission control protocol gets degraded due to the features of cognitive radio network. To overcome this, several authors suggested quite a few modifications in the existing wireless transport protocol for cognitive radio network environment. This paper gives an overview of different transport protocols used for unlicensed users’ communication in cognitive radio networks.

A research survey on scheduling techniques in lte-based network and its state of art

Scarce wireless resources, lead to development of cognitive radio network as a solution to unlicensed users communication in the licensed frequency band. In response to the behavior of licensed users communication, unlicensed users communication need to change from one frequency band to another band. In this communication paradigm, the performance of unlicensed users transmission control protocol gets degraded due to the features of cognitive radio network. To overcome this, several authors suggested quite a few modifications in the existing wireless transport protocol for cognitive radio network environment. This paper gives an overview of different transport protocols used for unlicensed users’ communication in cognitive radio networks.

Standard Condition Number Based Spectrum Sensing Under Asynchronous Primary User Activity

Cognitive radio (CR) is a promising technology which enables the secondary user (SU) to sense and detect the presence or absence of the primary user (PU) in the frequency band of interest. Therefore, high detection probability is needed to ensure that primary user is adequately protected, while low false-alarm probability provides the opportunity of using free channel and a better throughput performance of secondary user. Most of the studies on spectrum sensing techniques were conducted assuming a perfect synchronization between the secondary and the primary users. However, in practice, the synchronous assumption is very hard to satisfy in the context of real cognitive radio networks environment. In this paper, we present a theoretical formulation of the standard condition number (SCN) based spectrum sensing technique under an asynchronous cognitive radio networks. We assume the case where the primary users generate asynchronous slotted traffic. The standard condition number distributions under null and alternative hypotheses are derived where the secondary user is equipped with two receive antennas. Under asynchronous primary user traffic, we establish the existence of a lower limit of the false-alarm probability below which we are unable to derive a decision threshold for the SCN-based detector.

The Design and the implementation of a robust scheme to combat the effect of malicious nodes in Cognitive Radio Ad Hoc networks

Cognitive radio network, which enables dynamic spectrum access, addresses the scarcity of radio spectrum caused by ever-increasing demand for spectrum. Cognitive radio technology ensures the efficient utilisation of underutilised licenced spectrum by secondary users (SU). Secondary users, sense the radio environment before utilising the available spectrum to avoiding signal interference. The SU cooperatively sense the spectrum to ensure global view of the network. unfortunately, cooperative sensing is vulnerable to Byzantine attacks whereby SU falsify the spectrum reports for selfish reasons. Hence, this study proposes the implementation of a scheme to combat the effects of Byzantine attack in cognitive radio network. The proposed scheme, known as the extreme studentized cooperative consensus spectrum sensing (ESCCSS), was implemented in an ad hoc cognitive radio networks environment where the use of a data fusion centre (DFC) is not required. Cognitive radio nodes perform their own data fusion making spectrum access decisions. They fuse their own reports with reports from neighbouring nodes. To evaluate the performance of our scheme and its effectiveness in combating the effect of byzantine attack, comparative results are presented. The comparative results show that the ESCCSS outperformed the Attack-Proof Cooperative Spectrum Sensing scheme.

A Trust Framework to Detect Malicious Nodes in Cognitive Radio Networks

Cognitive radio is considered as a pioneering technique in the domain of wireless communication as it enables and permits the Cognitive Users (CU) to exploit the unused channels of the Primary Users (PU) for communication and networking. The CU nodes access the vacant bands/channels through the Cognitive Radio Network (CRN) cycle by executing its different phases, which are comprised of sensing, decision making, sharing (accessing) and hand-off (mobility). Among these phases, hand-off is the most critical phase as the CU needs to switch its current data transmissions to another available channel by recalling all the previous functions upon the emergence of a PU. Further, from the security perspective, a Malicious User (MU) may imitate the PU signal with the intention to never allow the CU to use its idle band, which ultimately degrades the overall network performance. Attacks such as the Cognitive User Emulation Attack (CUEA) and Primary User Emulation Attack (PUEA) may be encountered by the handoff procedure, which need to be resolved. To address this issue, a secure and trusted routing and handoff mechanism is proposed specifically for the CRN environment, where malicious devices are identified at the lower layers, thus prohibiting them from being part of the communication network. Further, at the network layer, users need to secure their data that are transmitted through various intermediate nodes. To ensure a secure handoff and routing mechanism, a Trust Analyser (TA) is introduced between the CU nodes and network layer. The TA maintains the record of all the communicating nodes at the network layer while also computing the rating and trust value of the Handoff Cognitive User (HCUs) using the Social Impact Theory Optimizer (SITO). The simulation results suggest that the proposed solution leads to 88% efficiency in terms of better throughput of CRN during data communication, the packet loss ratio, the packet delivery ratio and the maximum and average authentication delay and clearly outperforms the prevailing mechanisms in all the parameters.

Parallel concatenated Gallager codes for reliable data transmission in cognitive radio networks

Cognitive radio technology addresses the problem of spectrum scarcity by providing an opportunity for secondary users to exploit the unutilised licenced spectrum. However, constant interference from primary users makes reliable communication extremely challenging for secondary users in cognitive radio networks. Forward error correction codes ensure the reliability of transmitted data, among which low-density parity check codes are the most appropriate. Lengthy low-density parity check codes, however, have certain drawbacks specifically high encoding and decoding complexity. Parallel concatenated Gallager code addresses these issues. This paper evaluates the performance of various parallel concatenated Gallager codes in a dynamic and fragile cognitive radio network environment where the secondary users are prone to constant interference from the primary users. Simulation results affirms that proposed parallel concatenated Gallager codes outperform a dedicated low density parity check code of same frame length and code rate.

Performance Analysis of a New MAC Protocol for Wireless Cognitive Radio Networks

In this study, network performance analysis of a newly proposed cognitive radio wireless network (CRWN) medium access control (MAC) protocol is investigated in order to improve the performance of wireless cognitive radio networks (CRNs). Several MAC protocols have been used in the literature in order to increase the performance of wireless CRNs. In this context, a cognitive radio based MAC protocol is proposed, which is also a solution to the needs of wireless CRNs with high performance results. We propose an autonomous control strategy that recognizes both the primary network and the CRN environment considering the service quality requirements of the wireless CRN environment. Simulation of the network environment is performed with Riverbed Modeler software for more realistic performance evaluation. By selecting various workloads for secondary users, the analysis of different network performance parameters such as throughput ratios, end-to-end delays, packet loss ratios, bit error rates, and energy consumptions reveal the originality of our study. In addition, Slotted Aloha, TDMA, and CSMA/CA based MAC protocols for CRNs used in different studies in the literature are compared with CRWN. As a result, proposed protocol is given about 40% higher performance than Slotted Aloha in terms of throughput and about 36% higher performance than TDMA in terms of delay. Moreover, the proposed CRWN consumes less energy than CSMA/CA.

Wireless Transport Protocol Variants for Cognitive Radio Networks

Scarce wireless resources, lead to development of cognitive radio network as a solution to unlicensed users communication in the licensed frequency band. In response to the behavior of licensed users communication, unlicensed users communication need to change from one frequency band to another band. In this communication paradigm, the performance of unlicensed users transmission control protocol gets degraded due to the features of cognitive radio network. To overcome this, several authors suggested quite a few modifications in the existing wireless transport protocol for cognitive radio network environment. This paper gives an overview of different transport protocols used for unlicensed users’ communication in cognitive radio networks

Analysis of Active Spectrum Handoff Effect Based on Cooperative Spectrum Prediction

In the cognitive radio network environment, because the sub-user opportunistic use of “spectrum hole” to communicate, resulting in the inevitable uninterrupted system for spectrum switching, and frequent spectral switching system performance significantly reduced. In this paper, an active spectrum switching method based on improved cooperative spectrum prediction is proposed based on the existing active spectrum switching based on single user prediction. This method can effectively improve the performance of spectrum switching by introducing the cooperative prediction module into the frame structure and the idea of dividing the frame structure frequency domain. Based on the experimental simulation, it is proved that the performance of the method is improved compared with the traditional single-user prediction active spectrum switching method and the periodic spectrum sensing switching method, the average switching delay, channel switching frequency and collision rate and other performance indicators have been effectively improved.

A Game Theory Based Solution for Security Challenges in CRNs

Cognitive radio networks (CRNs) are being envisioned to drive the next generation Ad hoc wireless networks due to their ability to provide communications resilience in continuously changing environments through the use of dynamic spectrum access. Conventionally CRNs are dependent upon the information gathered by other secondary users to ensure the accuracy of spectrum sensing making them vulnerable to security attacks leading to the need of security mechanisms like cryptography and trust. However, a typical cryptography based solution is not a viable security solution for CRNs owing to their limited resources. Effectiveness of trust based approaches has always been, in question, due to credibility of secondary trust resources. Game theory with its ability to optimize in an environment of conflicting interests can be quite a suitable tool to manage an ad hoc network in the presence of autonomous selfish/malevolent/malicious and attacker nodes. The literature contains several theoretical proposals for augmenting game theory in the ad hoc networks without explicit/detailed implementation. This paper implements a game theory based solution in MATLAB-2015 to secure the CRN environment and compares the obtained results with the traditional approaches of trust and cryptography. The simulation result indicates that as the time progresses the game theory performs much better with higher throughput, lower jitter and better identification of selfish/malicious nodes.

A hardware testbed for learning-based spectrum handoff in cognitive radio networks

A real-time cognitive radio network (CRN) testbed is implemented by using the universal software radio peripheral (USRP) and GNU Radio to demonstrate the use of reinforcement learning and transfer learning schemes for spectrum handoff decisions. By considering the channel status (idle or occupied) and channel condition (in terms of packet error rate), the sender node performs the learning-based spectrum handoff. In reinforcement learning, the number of network observations required to achieve the optimal decisions is often prohibitively high, due to the complex CRN environment. When a node experiences new channel conditions, the learning process is restarted from scratch even when the similar channel condition has been experienced before. To alleviate this issue, a transfer learning based spectrum handoff scheme is implemented, which enables a node to learn from its neighboring node(s) to improve its performance. In transfer learning, the node searches for an expert node in the network. If an expert node is found, the node requests the Q-table from the expert node for making its spectrum handoff decisions. If an expert node cannot be found, the node learns the spectrum handoff strategy on its own by using the reinforcement learning. Our experimental results demonstrate that the machine learning based spectrum handoff performs better in the long term and effectively utilizes the available spectrum. In addition, the transfer learning requires less number of packet transmissions to achieve an optimal solution, compared to the reinforcement learning.11DISTRIBUTION STATEMENT A: Approved for Public Release; distribution unlimited 88ABW-2017–6274; 13 Dec 2017.

Monopolistic Models for Resource Allocation: A Probabilistic Reinforcement Learning Approach

Under the environment of cognitive radio networks, users are equipped with intelligent capabilities so that they can sense the conditions of networks and act optimally to maximize their revenues. Thus, dynamic spectrum access (DSA), which focuses on the management and distribution of the resources, is a critical problem, especially when only limited resources are available. Currently, this problem is handled by the game theory and auction theory, since this is a problem involving multiple agents. In this paper, we propose agent-based modeling method to model this multi-agent environment and probabilistic reinforcement learning to learn the optimal strategies. We focus on a simple scenario with only one primary user (PU) and multiple secondary users (SUs), and try to maximize the revenues of both sides, which may be further extended to other scenarios with different number of agents. First, we model this environment as a monopolistic market from the perspective of economics, where a PU acts as the monopoly and the SUs are passive buyers. Then, we propose probabilistic reinforcement learning methods to handle DSA so that both the PU and SUs can behave optimally by learning from the feedback of the others. Experimental results prove the flexibility and superior performance of our proposed methods.

Flexible Queuing Model for Number of Active Users in Cognitive Radio Network Environment

This work presents a Soft Queuing Model (SQM) for number of active users present in a cognitive radio network (CRN) at some given instant. Starting with the existing cellular network where the upper limit for the number of channels and active users is well defined. The idea is then extended to the complicated scenario of CRNs where the upper limit is not deterministic for both the number of channels and the active users. Accordingly a probabilistic SQM is proposed under the condition that the number of channels and active cognitive users are both random variables. The proposed model will be useful to offer the level of reliability to the clients connected with CRN and hence to offer secure communication even on a cooperative CRN. The proposed model has been verified theoretically and simulations have been carried out in diversified set ups to evaluate the performance.

Optimal Spectrum Handoff Control for CRN Based on Hybrid Priority Queuing and Multi-Teacher Apprentice Learning

An optimal spectrum handoff scheme for cognitive radio networks (CRNs) is presented in this paper. This scheme has two novel features: 1) Hybrid rule-based priority queuing model : To overcome the limitations of preemptive resume priority and nonpreemptive resume priority (PRP/NPRP) queuing models, a hybrid queuing model with discretion rule is proposed to characterize the spectrum access priority among secondary users (SUs). This hybrid queuing model is then used to calculate the channel waiting time during spectrum handoff; and 2) Multiteacher apprentice learning : Unlike existing CRN cognition engine designs that focus on spectrum adaptation through SU self-learning (i.e., an SU learns how to adapt to the dynamic CRN environment by itself), we propose the concept of multiteacher knowledge transfer , wherein the multiple SUs that already have mature spectrum adaptation strategies share their knowledge with an inexperienced SU. Our simulation results show that the proposed new designs improve the spectrum handoff accuracy for the complex CRN environments.