Issue In Cognitive Radio Networks Research Articles

Fuzzy logic based authentication in cognitive radio networks

<p>Security is a critical issue in cognitive radio networks because the cognitive node enters and variably leaves the spectrum, so it is difficult to process communication secretly. We suggested a fuzzy logic-based implicit authentication mechanism to be a solution for the confusion if there were any cognitive node doubts it to be unauthentic, and to improve user privacy in cognitive radio networks. Using a fuzzy logic technique, the proposed scheme computed certification based on proposed feedback. When a cognitive node needs to join the network, it is verified by using fuzzy logic if the node was authenticated or not. Our proposed fuzzy logic's results implicit authentication proved that it was a very successful and applicable scheme on cognitive radio networks, and it will be able to make an effective final decision in the context of incompleteness, ambiguity, and heterogeneity</p>

Performance Analysis of Efficient Spectrum Utilization in Cognitive Radio Networks by Dynamic Spectrum Access and Artificial Neuron Network Algorithms

Efficient spectrum utilization is a prominent issue in cognitive radio networks. Owing to this, power allocation policies are proposed which underlay cognitive radio networks together among all prime nodes, secondary nodes, eavesdropper and secondary sender powered by renewable energy that is harvested from primary sender to acquire improved energy efficiency to enhance transmission rate, throughput, and Spectrum Utilization (SU). As a result, there is a need for combination of Dynamic Spectrum Access (DSA) algorithm, Artificial Neuron Network (ANN) algorithm which will make an allotment of obtainable network assets for various elements challenging for their resources. The prime objective of this paper is to intend a route control based multi-path Quality Of Service (QoS) and to find substitute paths between Secondary User (SU) source and SU destination fulfilling QoS metrics, specifically providing maximal throughput and minimal delay. In order that primary substitute channels along the paths are used completely to reduce data packets loss by using Network Simulator 2 (NS2) software tool.

Joint resource optimization in spectrum sharing decode and forward relaying networks.

This paper proposes an adaptive power allocation and subcarrier pairing algorithm for orthogonal frequency division multiplexing based decode and forward cognitive radio networks, where primary and secondary users achieve spectrum sharing in the same frequency band. The secondary network tries to maximize its sum rate while ensuring that the interference introduced to the primary network is below an acceptable level. Although similar problems have been investigated in traditional cooperative communication networks, it’s still an open issue in cognitive radio networks due to interference thresholds. The power consumed by the secondary network is not only limited by its own power peak, but also by the interference threshold of the primary user. Our proposed algorithm not only allocates power and pairs subcarriers reasonably, but also specifies the conditions under which the relaying link is superior to the direct transmission. Simulation results show that the sum rate of the proposed algorithm exceeds other methods and obtains a significant performance gain.

A novel fast and fair asynchronous channel hopping rendezvous scheme in cognitive radio networks for internet of things

Recent years have witnessed significant growth in the area of Internet of things (IoT) and is further expected to increase manifolds in the foreseeable future. Such a massive usage of IoT comes with spectrum scarcity issues. In the last few years, Cognitive Radio Network (CRN) has been widely researched because of its potential to be an effective solution to the problem of spectrum scarcity. The unlicensed users in CRN need to rendezvous on a commonly available channel for the purpose of necessary information exchange. Channel hopping (CH) is one of the representative technique to achieve rendezvous in CRNs in which users hop among available channels according to a pre-defined sequence. Providing guaranteed rendezvous which is fair and having full rendezvous diversity in minimum possible time is a challenging issue in CRN. In this paper, we propose an asymmetric asynchronous CH algorithm that provides guaranteed, fair and full rendezvous diversity among the users. The idea is to construct sender and receiver matrices to generate CH sequences based on the number of available channels of sender and receiver. Analytical and simulation results verify that the proposed algorithm outperform some existing state of the art rendezvous algorithms in terms of maximum time to rendezvous (MTTR), provide full rendezvous diversity and rendezvous fairness among the users.

A Comprehensive Survey on Effective Spectrum Sensing in 5G Wireless Networks through Cognitive Radio Networks

Spectrum sensing is a challenging issue in cognitive radio network. In particular, wideband spectrum sensing gains more attention due to emerging 5G wireless networks characterized by high data rates in the order of hundreds of Gbps. Conventional sensing techniques uses samples for its observations based on Nyquist rate. Due to hardware cost and sampling rate limitations those techniques can sense only one band at a time. In spite of this issue secondary users have to sense multiple frequency bands using frontend technologies which leads into increased cost, time and complexity. Considering the facts and issues, compressive sensing was introduced to minimize the computation time by improving the sensing process even for high dimensional resources. Holding the essential information and reduces the sample size which is related to high dimensional data acquisition is performed in compressive sensing. In the last decade various researchers paid more attention to improve the performance of compressive sensing in cognitive radio networks based on sensing matrix, sparse representation and recovery process. This survey paper provides an in-depth analysis of conventional models and its sensing strategies in cognitive radio networks along with its merits and demerits to obtain a detailed insight about compressive sensing.

Blockchain and extreme learning machine based spectrum management in cognitive radio networks

AbstractIn recent times, spectrum sensing and spectrum management become a crucial design issue in cognitive radio networks (CRN). To improve the spectrum utilization in CRN, the secondary users (SUs) will try to utilize the spectrum resource when it is unoccupied by the authorized primary users (PUs). At the same time, blockchain principle has been introduced to efficiently identify the legitimate SUs and allocate the spectrum resource as per the demand specified by the SUs. In this view, this article presents a new machine learning (ML) with blockchain‐based spectrum management technique in CRN. The proposed model undergoes three processes, namely spectrum sensing, blockchain‐based spectrum access, and malicious user (MU) identification. Initially, ML‐based extreme learning machine (ELM) technique is applied for spectrum sensing. Then, the presented blockchain approach provides secured spectrum allocation for SUs. Finally, the MUs are identified and to be blocked from accessing the available spectrum resource. An extensive simulation analysis is carried out to ensure the goodness of the proposed model. The obtained results indicated that the proposed model has offered better performance compared with other methods. The experimental outcome stated that under the presence of −20 dB SNR, the proposed method has attained a maximum detection rate of 0.68, whereas the KNN and OR rule methods have demonstrated a minimum detection rate of 0.58 and 0.5, respectively.

Error rate detection due to primary user emulation attack in cognitive radio networks

Security threat is a crucial issue in cognitive radio network (CRN). These threats come from physical layer, data link layer, network layer, transport layer, and application layer. Hence, security system to all layers in CRN has a responsibility to protect the communication between among Secondary User (SU) or to maintain valid detection to the presence of Primary User (PU) signals. Primary User Emulation Attack (PUEA) is a threat on physical layer where malicious user emulates PU signal. This paper studies the effect of exclusive region of PUEA in CRN. We take two setting of exclusive distances, 30m and 50m, where this radius of area is free of malicious users. Probability of false alarm (Pf) and miss detection (Pm) are used to evaluate the performances. The result shows that increasing distance of exclusive region may decrease Pf and Pm.

Sensing Coverage-Based Cooperative Spectrum Detection in Cognitive Radio Networks

With the advancement of the Internet of Things (IoT), conventional principles of spectrum allocation cannot mitigate spectrum depletion so that a cognitive radio technology is proposed as a solution. The hidden primary user (PU) problem, however, is a critical issue in cognitive radio networks, because spectrum sensing nodes (SNs) can misclassify spectrum occupancy. To cope with this, machine learning-based cooperative spectrum sensing schemes (CSSs) have been proposed. The CSSs without considering the node placement, however, are still faced with the hidden PU problem. In this paper, we present how to place SNs to guarantee the performance of machine learning-based CSSs. We verify that the hidden PU problem makes the overlap of data distribution, which deteriorates the spectrum sensing ability. Based on the Kullback–Leibler divergence, analytical expressions for the spectrum sensing coverage of a single SN are derived. Then, we propose a strategy on how to place a few SNs to cover the whole area of the PU and prove the feasibility of our proposal by the experiment results.

Countermeasure with Primary User Emulation Attack in Cognitive Radio Networks

One of the most challenging issues in Cognitive Radio Networks (CRN) is to detect Primary User Emulation Attack (PUEA). In the absence of Primary Users (PU), the attackers mimic PUs’ signal characteristics to fool legitimate Secondary Users (SU) that evacuate the channel for them, in order to use the channel selfishly. Many works have been done to detect PUEA; among them, localization and encryption and so on are the examples. Recently, game theory has been used to detect PUEA. In this paper, a method based on game theory is proposed, that without using any complex calculation and second methods (RSS, GPS and so on), PUEA can be detected. This method is especially proposed for MANET and can be used in any circumstance of CRNs (ad-hoc, centralized, distributed…). It is reliable, with minimum miss detection and no false alarm of PU. Simulation results show that the proposed method has good operations even in dense networks and ultra-dense networks.

Cost-Minimized Crowdsourced Spectrum Sensing

Cooperative spectrum sensing which enhances the sensing accuracy is an important research issue for cognitive radio networks, especially in complicated environment. Considering the extensive use of mobile intelligent terminals such as smart phones and tablets, crowdsourced spectrum sensing, which assigns spectrum sensing tasks to mobile terminals, can take advantage of mobile terminals' cooperation and obtain the accurate sensing results. In this paper, crowdsourced spectrum sensing is studied to propose assignment scheme of spectrum sensing tasks in large geographical areas. There may be several kinds of terrains affecting sensing in large-scale regions. Hence, according to the terrains, we divide a large region into several sub-regions and introduce sensing effect function to evaluate the sensing accuracy based on the number of sensing sub-regions. Furthermore, considering energy consumption is an important issue which mobile terminals focus on, we use the relative energy consumption to evaluate the cost of mobile terminals during spectrum sensing. Then, we formulate the crowdsourced sensing problem to minimize the total cost while keeping sensing effect not lower than the predefined threshold to maintain sensing accuracy. Since the problem is NP-hard, a heuristic algorithm is proposed to solve the crowdsourced sensing problem. At first, our algorithm arranges all sensing tasks in a priority queue based on their urgency. Then, sensing tasks are sequentially assigned to terminals with higher energy to prolong their survival time under makespan and energy constraints. To obtain the lowest system cost, we introduce remaining time and reassign sensing tasks from high-cost terminals to low-cost terminals based on the remaining time. Simulation results show our algorithm achieves higher performance than the other algorithms.

Cooperative spectrum estimation over large‐scale cognitive radio networks

Spectrum sensing is a significant issue in cognitive radio networks which enables estimation of the frequency spectrum and hence provides frequency reuse. In the large-scale cognitive radio networks, secondary users cannot share a common spectrum since the coverage area of primary users is limited. In this study, the authors suggest a diffusion adaptive learning algorithm based on correntropy cooperation policy, which first categorises received data of secondary users into several groups, and then learns a common spectrum inside each group. The mean-square performance of proposed algorithm is analysed and supported by simulations. Experimental results show that, in a multitask cognitive network, the proposed algorithm can achieve a better mean-square deviation learning performance both in transient and steady-state regimes in comparison with other conventional algorithms.

Fuzzification supported spectrum decision framework for cognitive radio networks

AbstractEfficient decision support framework and swift occupation of the available licensed spectrum slot by secondary users (SUs) are the challenging issues in cognitive radio networks. An SU has to sense multiple target frequency spectrum slots in the shortest possible time before deciding to select and occupy the best idle slot for its transmission. A spectrum decision framework for the SUs in cognitive radio networks based on fuzzy logic is proposed. The activity time, possession, and quality of service of the spectrum slot lead to the decision of occupying the available spectrum slot(s). The proposed decision framework ensures that the SU's transmission in the particular spectrum slot does not cause any interference with the licensed user of the slot. The proposed method yields enhanced throughput as compared with the existing channel assignment schemes.

Wideband Spectrum Sensing Based on Riemannian Distance for Cognitive Radio Networks.

Detecting the signals of the primary users in the wideband spectrum is a key issue for cognitive radio networks. In this paper, we consider the multi-antenna based signal detection in a wideband spectrum scenario where the noise statistical characteristics are assumed to be unknown. We reason that the covariance matrices of the spectrum subbands have structural constraints and that they describe a manifold in the signal space. Thus, we propose a novel signal detection algorithm based on Riemannian distance and Riemannian mean which is different from the traditional eigenvalue-based detector (EBD) derived with the generalized likelihood ratio criterion. Using the moment matching method, we obtain the closed expression of the decision threshold. From the considered simulation settings, it is shown that the proposed Riemannian distance detector (RDD) has a better performance than the traditional EBD in wideband spectrum sensing.

A Collaborative Approach for Monitoring Nodes Behavior during Spectrum Sensing to Mitigate Multiple Attacks in Cognitive Radio Networks

Spectrum sensing is the first step to overcome the spectrum scarcity problem in Cognitive Radio Networks (CRNs) wherein all unutilized subbands in the radio environment are explored for better spectrum utilization. Adversary nodes can threaten these spectrum sensing results by launching passive and active attacks that prevent legitimate nodes from using the spectrum efficiently. Securing the spectrum sensing process has become an important issue in CRNs in order to ensure reliable and secure spectrum sensing and fair management of resources. In this paper, a novel collaborative approach during spectrum sensing process is proposed. It monitors the behavior of sensing nodes and identifies the malicious and misbehaving sensing nodes. The proposed approach measures the node’s sensing reliability using a value called belief level. All the sensing nodes are grouped into a specific number of clusters. In each cluster, a sensing node is selected as a cluster head that is responsible for collecting sensing-reputation reports from different cognitive nodes about each node in the same cluster. The cluster head analyzes information to monitor and judge the nodes’ behavior. By simulating the proposed approach, we showed its importance and its efficiency for achieving better spectrum security by mitigating multiple passive and active attacks.

Distributed Schemes for Crowdsourcing-Based Sensing Task Assignment in Cognitive Radio Networks

Spectrum sensing is an important issue in cognitive radio networks. The unlicensed users can access the licensed wireless spectrum only when the licensed wireless spectrum is sensed to be idle. Since mobile terminals such as smartphones and tablets are popular among people, spectrum sensing can be assigned to these mobile intelligent terminals, which is called crowdsourcing method. Based on the crowdsourcing method, this paper studies the distributed scheme to assign spectrum sensing task to mobile terminals such as smartphones and tablets. Considering the fact that mobile terminals’ positions may influence the sensing results, a precise sensing effect function is designed for the crowdsourcing-based sensing task assignment. We aim to maximize the sensing effect function and cast this optimization problem to address crowdsensing task assignment in cognitive radio networks. This problem is difficult to be solved because the complexity of this problem increases exponentially with the growth in mobile terminals. To assign crowdsensing task, we propose four distributed algorithms with different transition probabilities and use a Markov chain to analyze the approximation gap of our proposed schemes. Simulation results evaluate the average performance of our proposed algorithms and validate the algorithm’s convergence.

Sequence-Based Channel Hopping Algorithms for Dynamic Spectrum Sharing in Cognitive Radio Networks

Cognitive radio network (CRN) is a promising solution to spectrum scarcity that uses the dynamic spectrum access mechanism to increase the efficiency of the underutilized licensed spectrum. In a CRN, a pair of users exchanges their information at a common unused licensed channel to rendezvous. The rendezvous in all available channels and within the bounded time cycle is a challenging issue in CRNs. In this paper, the primary idea is to construct the channel hopping sequences by using primitive roots of the prime number. For guaranteed rendezvous in CRNs, we design three channel hopping protocols for the symmetric and asymmetric environment in synchronous and asynchronous scenarios of the CRN. Extensive simulation is performed to analyze the throughput, maximum time to rendezvous (MTTR), and average time to rendezvous (ATTR). Simulation results show that our protocols can outperform over the existing protocols and can give significant improvements in terms of MTTR, ATTR, and throughput.

A Nonmonetary QoS-Aware Auction Framework Toward Secure Communications for Cognitive Radio Networks

This paper investigates the secure communication issue for cognitive radio networks with nonaltruistic users. The design objective is to improve the secrecy rate of the primary user (PU) and, at the same time, create transmission opportunities for the secondary users (SUs) to satisfy their diversified quality-of-service (QoS) demands. To achieve this goal, we propose a novel nonmonetary trading model, where the users are incentivized to participate in the market by a barter-like resource-to-resource exchange. Specifically, the PU leverages the assistance of the SU in the form of cooperative forwarding or friendly jamming and yields part of the spectrum accessing time to the aided SU. The proposed spectrum auction framework jointly formulates the optimal cooperator selection and the corresponding resource allocation problems by taking into consideration the QoS demands of individual users. The proposed framework ensures that bidding truthfully is the dominant strategy for all bidders and, thus, is invulnerable to market manipulation and eliminates the overhead of strategizing over other bidders. Simulation results reveal that the proposed framework could provide substantial gains for both the PU and the aided SU.

A Spectrum Access Based on Quality of Service (QoS) in Cognitive Radio Networks.

The quality of service (QoS) is important issue for cognitive radio networks. In the cognitive radio system, the licensed users, also called primary users (PUs), are authorized to utilize the wireless spectrum, while unlicensed users, also called secondary users (SUs), are not authorized to use the wireless spectrum. SUs access the wireless spectrum opportunistically when the spectrum is idle. While SUs use an idle channel, the instance that PUs come back makes SUs terminate their communications and leave the current channel. Therefore, quality of service (QoS) is difficult to be ensured for SUs. In this paper, we first propose an analysis model to obtain QoS for cognitive radio networks such as blocking probability, completed traffic and termination probability of SUs. When the primary users use the channels frequently, QoS of SUs is difficult to be ensured, especially the termination probability. Then, we propose a channel reservation scheme to improve QoS of SUs. The scheme makes the terminated SUs move to the reserved channels and keep on communications. Simulation results show that our scheme can improve QoS of SUs especially the termination probability with a little cost of blocking probability in dynamic environment.

A Collaborative Approach towards Securing Spectrum Sensing in Cognitive Radio Networks

Cognitive radio (CR) is introduced to accommodate the steady increment in the spectrum demand. Spectrum sensing is used to detect the unexploited sub-bands in the radio environment. In order to improve the accuracy of the spectrum sensing, the cooperative spectrum sensing method is assumed to be the best method to be used. However, misbehaving sensing nodes might falsify the spectrum sensing data to prevent legitimate nodes from utilizing the spectrum. Hence, wireless security has become an important issue in cognitive radio networks to ensure a reliable spectrum sensing and a fair resource allocation and management. A node misbehaves during the spectrum sensing phase by sending false sensing data to other cooperative sensing nodes. In this paper we propose a novel collaborative approach during spectrum sensing phase to monitor sensing nodes’ behavior and identify the misbehaving sensing nodes. The proposed approach measures the node's reliability through a value called belief level (BL). The clustering method is used to divide all the sensing nodes in a specific number of clusters. Each cluster has a cluster head (CH) which is responsible for collecting sensing reports from different cognitive nodes in the same cluster about each other and identifying the misbehaving sensing nodes. Simulation results show the added value and the effectiveness of the proposed approach.

Discrete time analysis of cognitive radio networks with imperfect sensing and saturated source of secondary users

Sensing is one of the most challenging issues in cognitive radio networks. Selection of sensing parameters raises several tradeoffs between spectral efficiency, energy efficiency and interference caused to primary users (PUs). In this paper we provide representative mathematical models that can be used to analyze sensing strategies under a wide range of conditions. The activity of PUs in a licensed channel is modeled as a sequence of busy and idle periods, which is represented as an alternating Markov phase renewal process. The representation of the secondary users (SUs) behavior is also largely general: the duration of transmissions, sensing periods and the intervals between consecutive sensing periods are modeled by phase type distributions, which constitute a very versatile class of distributions. Expressions for several key performance measures in cognitive radio networks are obtained from the analysis of the model. Most notably, we derive the distribution of the length of an effective white space; the distributions of the waiting times until the SU transmits a given amount of data, through several transmission epochs uninterruptedly; and the goodput when an interrupted SU transmission has to be restarted from the beginning due to the presence of a PU.