Unlicensed Users Research Articles

Securing Privacy Using Optimization and Statistical Models in Cognitive Radio Networks

Cognitive Radio Networks (CRN) are the possible and ideal solution for meeting the spectrum needs of next-generation communication systems. CRN is a promising alternative approach that allows spectrum sharing in many applications. The licensed users considered Primary Users (PU) and unlicensed users as Secondary Users (SU). Time and power consumption on security issues are considered degrading factors in performance for improving the Quality of Service (QoS). Irrespective of using different optimization techniques, the same methodology is to be updated for the task. So that, learning and optimization go hand in hand. It ensures the security in CRN, risk factors in spectrum sharing to SU for secure communication. The objective of the proposed work is to preserve the location of the SU from attackers and attain the clustering of SU to utilize the resource. Ant Colony Optimization (ACO) is implemented to increase the overall efficiency and utilization of the CRN. ACO is used to form clusters of SUs in the co-operative spectrum sensing technique. This paper deals with threat detection and classifying threats using parameters such as unlikability, context privacy, anonymity, conditional traceability, and trade-off. In this privacy-preserving model, overall accuracy is 97.4%, and it is 9% higher than the conventional models without Privacy-Preserving Architecture (PPA).

Optimal Resource Allocation for GAA Users in Spectrum Access System Using Q-Learning Algorithm

Spectrum access system (SAS) is a three-tier layered spectrum sharing architecture proposed by the Federal Communications Commission (FCC) for Citizen broadband radio service (CBRS) 3.5 GHz band. The available 150 MHz spectrum is dynamically shared among Incumbent Access (IA), Primary Access Licensees (PAL) and General Authorized Access (GAA) users. IA users are the highest priority federal military users, PAL users are the licensed users and the GAA users are the least priority unlicensed users. In this scenario, PAL operators are willing to give access to their idle spectrum to GAA users to generate extra revenue. SAS will ensure to protect IA users and PAL users from interference caused by lower-tier users. It is the responsibility of SAS to allocate resources to GAA users but the method to do so is left open. In this article, a novel auction algorithm based on Q-learning for dynamic spectrum access (SAS-QLA) is proposed. In SAS-QLA, multiple GAA users dynamically and intelligently bid using Q-learning to access PAL reserved idle channels. SAS will decide to allocate the channels to GAA users with maximum bidding offers. GAA users have their own quality of service (QoS) demands i.e., transmission rate, packet loss, bidding efficiency, and maintain the preference of available PAL reserved idle channels based on Q-learning considering the available QoS. The proposed scenario is also modeled as a knapsack NP-hard problem and solved using dynamic programming and distributed relaxation method. Numerical results demonstrate the effectiveness of the SAS-QLA algorithm in improving the bidding efficiency, maximizing the data rate per unit cost and spectrum utilization.

Spectrum Allocation Techniques for Cognitive Radio Networks

The remarkable growth in the demand for multimedia streaming over wireless networks is challenged by spectrum scarcity. To mitigate the effect of such a challenge, Cognitive Radio Networks (CRNs) were introduced as a promising technology since CRNs offer a great advantage to unlicensed users, also known as secondary users (SUs), by allowing them to opportunistically access the licensed bands when these bands are not used by their primary users (PUs). In this work, an LTE-based CRN is proposed with the objective of guaranteeing continuous video playback at the SUs end at acceptable perceptual quality. To accomplish this objective, different resource allocation schemes are introduced to adaptively assign the available channels to SUs while considering the quality of these channels as well as the buffer occupancies of the different SUs. In addition, a streaming algorithm is proposed to ensure the delivery of the base and enhancement layers of the scalable video frames within the delay constraints with priority given to the base-layers of the video frames to guarantee the continuity of video playback. Furthermore, adaptive modulation is used based on the channel state information (CSI) as fed-back by the SUs. The performance of the proposed schemes is evaluated through extensive Monte-Carlo simulations.

A hybrid spectrum access approach for efficient channel allocation and power control in cognitive radio networks

SummaryRadio spectrum is the backbone for wireless communication. Many recent studies show that most of the available spectrum has already been allocated. Cognitive radio (CR) provides a new dimension in the field of wireless communication and is considered as a life line for the spectrum scarcity problem as seen all around the world nowadays. CR enables the use of dynamic spectrum access, where secondary users (SUs) or unlicensed users are allowed to opportunistically use the spectrum when it is not being used by the primary users (PUs) or licensed users. Medium Access Control (MAC) protocols use the sensing capability and the primary users behavior to generate the transmission opportunities for SUs. This article proposes two Markov based hybrid access methods namely Hybrid‐P1 and Hybrid‐P2, where a secondary user (SU) dynamically decides it's transmission mode between overlay and underlay depending upon the primary users's (PU's) past and present behavior. Analytical results based on the Markov chain concept are very encouraging and verified through OMNeT++ simulator. This work also analyses the performance of overlay only and underlay only methods, which are well known data transmission techniques in the cognitive radio networks (CRNs). Simulation results show the 47% and 59% performance improvement in Hybrid‐P1 access method, when compared to the overlay only and underlay only access methods respectively. Furthermore simulation results also show the 54% and 65% performance improvement in Hybrid‐P2 access method, when compared to the overlay only and underlay only access methods respectively.

Deep Cooperative Spectrum Sensing Based on Residual Neural Network Using Feature Extraction and Random Forest Classifier.

Some bands in the frequency spectrum have become overloaded and others underutilized due to the considerable increase in demand and user allocation policy. Cognitive radio applies detection techniques to dynamically allocate unlicensed users. Cooperative spectrum sensing is currently showing promising results. Therefore, in this work, we propose a cooperative spectrum detection system based on a residual neural network architecture combined with feature extractor and random forest classifier. The objective of this paper is to propose a cooperative spectrum sensing approach that can achieve high accuracy in higher levels of noise power density with less unlicensed users cooperating in the system. Therefore, we propose to extract features of the sensing information of each unlicensed user, then we use a random forest to classify if there is a presence of a licensed user in each band analyzed by the unlicensed user. Then, information from several unlicensed users are shared to a fusion center, where the decision about the presence or absence of a licensed user is accomplished by a model trained by a residual neural network. In our work, we achieved a high level of accuracy even when the noise power density is high, which means that our proposed approach is able to recognize the presence of a licensed user in of the cases when the evaluated channel suffers a high level of noise power density ( dBm/Hz). This result was achieved with the cooperation of 10 unlicensed users.

Power Control for Cognitive Radio Networks: A Game Theoretic Approach

In communication industry one of the most rapidly growing area is wireless technology and its applications. The efficient access to radio spectrum is a requirement to make this communication feasible for the users that are running multimedia applications and establishing real-time connections on an already overcrowded spectrum. In recent times cognitive radios (CR) are becoming the prime candidates for improved utilization of available spectrum. The unlicensed secondary users share the spectrum with primary licensed user in such manners that the interference at the primary user does not increase from a predefined threshold. In this paper, we propose an algorithm to address the power control problem for CR networks. The proposed solution models the wireless system with a non-cooperative game, in which each player maximize its utility in a competitive environment. The simulation results shows that the proposed algorithm improves the performance of the network in terms of high SINR and low power consumption.

Machine learning supervised antenna for software defined cognitive radios

ABSTRACT Software-defined radio (SDR) deals with the opportunistic detection and allocation of the radio frequency (RF) signals in the cognitive radio network (CRN). The opportunistic spectrum use between primary licenced and secondary unlicensed user depends upon the adequate detection of the spectrum hole. This paper presents a design of a smart compact wideband patch antenna for SDR in cognitive radios. The fractal slot antenna with electromagnetic bandgap structure is designed on 12 × 18 mm2 FR4 substrate as a primary perception module in the cognitive radio sensor network. The wide operating range of antenna 0.68–12.1 GHz is highly compatible with adaptive learning of SDR to deal with dynamic spectrum variation over a large bandwidth. The antenna exhibits high radiation efficiency with consideration of tangential losses of FR4 and sustainable gain 2.5 dBi over the complete impedance bandwidth. The proposed five-layered ANN-ML supervised SDR comprises multispectral resolution via coefficient estimation for primary and secondary user and elicits high throughput of proposed antenna during white space spectrum sensing. The data analytics for ANN-ML has modelled on Python 3.0 IDE and the results are validated to justify the candidature of the antenna for wideband sensing as per the Federal Communications Commission standards for CRN.

Detecting Byzantine attacks in Cognitive Radio Networks : A two-layered approach using Hidden Markov Model and machine learning

Cognitive Radio Networks (CRN) are gaining popularity due to their ability to harness the TV whitespace. CRNs are expected to usher in a new wireless technology to cater to the ever growing population of wireless mobile devices while the current ISM range of wireless technologies is increasingly becoming insufficient. CRNs uses the principle of collaborative spectrum sensing (CSS) where unlicensed users, called Secondary Users (SU) keep sensing a licensed band belonging to the incumbent user called the Primary User (PU). Such a collaborative sensing mechanism enables an increased detection accuracy of the incumbent’s presence (or absence) as all the SUs send their local sensing reports to a Fusion Centre (FC), where these sensing reports are aggregated to arrive at a final sensing decision. However, this collaborative sensing introduces vulnerabilities which can be used to carry out an attack called the Byzantine Attack (a.k.a. Spectrum Sensing Data Falsification (SSDF) attack). We present a two-layer model framework to classify Byzantine attackers in a CRN. The first layer, Processing layer, uses Hidden Markov Model (HMM) to get a probabilistic relationship between the PU’s states and the SUs’ sensing reports. This generates the required dataset for the next layer. The second layer, Decision layer, uses several ML algorithms to classify the SUs into Byzantine attackers and normal SUs. Extensive simulation results confirm that the learning classifiers perform well across various testing parameters. Finally, a comparison analysis of the proposed method with an existing non-ML technique shows that the ML approach is more robust especially under high presence of malicious users.

Performance Analysis of Cognitive Radios in a Cooperative Scheme over Nakagami Fading Channels

Radio spectrum is a primary requisite for wireless technologies and sensor networks. Due to the high demand and expense of the radio spectrum, it is guaranteed to extend its efficient utilization it. To expand the effective operation and serviceability of the radio spectrum in wireless communications, the notion of Cognitive Radio (CR) is presented in where the licensed spectrum of Primary User (PU) is used opportunistically by unlicensed CR users without interfering with the prioritized PU data transmission. Usually, a CR system is applied to detect empty radio bands by exploiting well-known spectrum sensing schemes and then unused spectrum is opportunistically used by the CR system. Various channels fading of the radio environment are to be considered while introducing different spectrum sensing approaches. In this regard, sensing time to find a vacant radio spectrum should be maintained minimum to reliably get the desired throughput. In this paper, an agreement issue is addressed between the time required for effective spectrum sensing and the achievable throughput of the CR network. Our proposed model illustrates the achievable throughput of CR node in cooperation provides better performance than stand-alone CR node. This is achieved by addressing the variation of the number of nodes under the Nakagami fading distribution. In conclusion, the maximum throughputs of the cooperative CR nodes are guaranteed as per simulation and data analysis.

A Novel MAC Protocol with Fusion Center and Adaptive Full-Duplex Communication for Cognitive Radio Networks

Cognitive radio network is an intelligent and adaptive communication technology used to effectively utilize the radio spectrum to provide better communication. It enables the secondary (unlicensed) users to communicate over vacant channels without disrupting the communication of the primary (licensed) users. This paper proposes a novel contention-free MAC protocol for full-duplex and half-duplex cognitive radio networks. A novel concept of a fusion center-based channel allocation scheme is proposed to provide better channel allocation and reduced energy consumption. The proposed technique is compared with the existing techniques in terms of the average number of channels being sensed by each user, average wait time, collisions, throughput, and average energy consumption. The number of collisions increases as the maximum allotted time of communication increases in existing approaches. The experiment is performed with 20, 40, 60, and 80 s. The existing approaches show for 20 s, the number of collisions is 65, 61, 68, and 62; for 40 s, the number of collisions is 93, 96, 95, and 92; for 60 s, the number of collisions is 122, 126, 133, and 130; for 80 s, the number of collisions is 176, 170, 173, and 175. The proposed method approaches the number of collisions will become zero as the maximum allotted time of communication increases. The experimental results show that the proposed approach performs better than existing approaches. Moreover, it can be observed that the average waiting time of secondary users when a collision occurs is reduced with the application of a fusion center-based channel allocation scheme.

A Novel Spectrum Decision Pattern for The Favorable Cognitive Radio Based Internet of Things in 5G System

The Radio Frequency (RF) Spectrum decision in Cognitive Radio (CR) allows unlicensed users of wireless communication schemes to conquer the unoccupied spectrum slots as a resolution for barely spectrum. The Internet of Things (IoT) is a broad-arriving network and its object is linked by wireless communication technologies, donating cost- efficiency and generously opens to remote users. When IoT is applied, it is injured by challenges of susceptibility in the forceful surrounding situations, allotment and use of bandwidth, ease of approach and expense to buy RF spectrum. The object is permeated with cognitive capacity and is capable to model RF spectrum decisions to attain interference-release and wireless connectivity due to their Quality of Service (QoS) demands. Therefore the spectrum decision through an unlicensed user on CR influences an importance in CR-based IoT in 5G system and further network. This article depicts a systematic sustainment of the spectrum decision structure to CR Network. So, this can be mainly achieved by applying a favorable spectrum sensing technique and a novel spectrum decision framework. Presently, the wireless connectivity is intended to greater capacity, immense machine connectivity, and greater data range, low end-to-end latency, low cost and coherent Quality of Experience (QoE) condition. Hence, 4G is being substituted by 5G. Being in this 5G, the vast connectivity can be properly inspired by using the novel techniques, which is applied under the Energy detection spectrum sensing system. Also, a novel spectrum decision framework is designed for the optimum application of applying the IoT in 5G system ie., the effective use of an allocated RF spectrum is differently underutilized because of the standard handling with the licensed users are called as Primary Users (PUs).

A Model to Improve the Effectiveness and Energy Consumption to Address the Routing Problem for Cognitive Radio Ad Hoc Networks by Utilizing an Optimized Cuckoo Search Algorithm

A cognitive radio ad hoc network (CRAHN) is a mobile network that can communicate without any form of centralized infrastructure. The nodes can learn about the environment and make routing decisions. Furthermore, distributed computing, spectrum mobility, and the Internet of Things have created large data sets, which require more spectrum for data transmission. Unfortunately, the spectrum is a scarce resource that underutilized by licensed users, while unlicensed users are overcrowding the free spectrum. The CRAHNs technology has emerged as a promising solution to the underutilization of the spectrum. The focus of this study is to improve the effectiveness and energy consumption of routing in order to address the routing problem of CRAHNs through the implementation of the optimized cuckoo search algorithm. In CRAHNs, the node and spectrum mobility cause some frequent link breakages within the network, which degrades the performance of the routing protocols. This requires a routing solution to this routing problem. The proposed scheme was implemented in NS2 installed in Linux operating system, with a cognitive radio cognitive network (CRCN) patch. From the experimental results, we observed that the proposed OCS-AODV scheme outperformed CS-DSDV and ACO-AODV schemes. It obtained at least 3.87% packet delivery ratio and 2.56% and lower packets lost. The scheme enabled the mobile nodes to adjust accordingly to minimize energy consumption. If not busy, they switch to an idle state to save battery power.

Mitigation of Spectrum Sensing Data Falsification Attack in Cognitive Radio Networks using Trust Based Cooperative Sensing

One of most emerging technology in the recent years in the field of wireless communication is the Cognitive Radio (CR) technology, which reduces spectrum scarcity significantly. The main function of CR technology is detecting spectrum holes or unused spectrum of primary users (PUs), also called as licensed users, and assigning this unused spectrum to the secondary users (SUs), also called unlicensed users. As the CR technology is open to every user, there are many security issues such as Primary User Emulsion Attack (PUEA), Jamming Attack, Spectrum Sensing Data Falsification (SSDF) Attack, Lion Attack, and Sink Hole Attack and so on. SSDF attack is the one of major security attack in cognitive radio in which a malicious user sends false data intentionally to the other secondary users. The main aim of the SSDF attack is to disturb the communication between the secondary users or to gain more channel resources. One of the solutions to mitigating SSDF attack is the cooperative spectrum sensing. In this paper, we propose a new algorithm of cooperative sensing based on trust values of secondary users, and compares with the conventional cooperative spectrum sensing with the proposed algorithm. The simulation of cooperative sensing also performed in both time variant channel and time invariant (Rayleigh) channel. The authors also compare the three basic hard fusion techniques such as AND, OR, MAJORITY rule

Analytical Model for Mitigating Primary User Emulation Attack using Hypothesis Testing in Cognitive Radio Networks

The Cognitive Radio (CR) is the key technology to deal with spectrum scarcity by allowing unlicensed CR users to coexist with existing users in licensed spectrum bands without interfering with binding communications. Cognitive technology provides the spectrum ability to be shared with licensed networks. This radio system can adjust its transmitter and recipient parameters on the basis of interaction with the current conditions in the environment. Due to this flexibility cognitive radios are exploring different types of threats and security attacks. In this paper, we focus primarily on the Primary User Emulation Attack (PUEA) which is a major attack in the cognitive radio network. PUEA is similar to a Denial-of-service attack that can seriously disrupt the spectrum sensing process and deny other legitimate secondary users to access the available spectrum. We proposed a Neyman-Pearson composite hypothesis test (NPCHT) based analytical model to study the impact of PUEA in a fading wireless communication environment. Simulation results show that the proposed techniques can substantially reduce the impact of the malicious attack on the network

Efficient Channel Allocation for Cognitive Radio Internet of Things

Recently, multiple wireless technology has arisen to provide effective and flexible IoT networking. Cognitive radio (CR) technology that facilitates software-defined radio is one of the key technology that gives IoT connectivity to a multitude of linked IoT devices in an opportunistic way. Unlicensed bandwidth has congested latest creation and study in the area of networking technology. The effect is unchecked and unregulated intrusion in the Internet of Things with low-powered wireless sensors (IoT). On the other hand, these technologies allowed low-energy IoT architecture at low cost, low energy usage and efficient spectrum use. Cognitive radio (CR) network, a low-cost approach for effective spectrum use, discusses the issue of spectrum use. Unlicensed users use underused bandwidth in CR networks. Due to its opportunistic existence, these networks' output relies on the observed spectrum pattern of the primary consumer. In these networks, frequency detection and usage must be accurately modelled. In this article, we propose the main Cognitive Radio Internet of Things (CR IoT) user identity mechanism using Markov's secret model. We implemented two algorithms: one for free channel identification and one for successful channel allocation. Simulation results suggest that CR-IoT surpassed traditional networking systems.

An efficient adaptive threshold‐based dragonfly optimization model for cooperative spectrum sensing in cognitive radio networks

SummaryEffective utilization of spectrum resources is an important factor in wireless communication which reduces spectrum scarcity. Over the years, communication systems use different frequency bands, and the users are categorized into licensed and unlicensed users. Most of the wireless bands are typically licensed; as a result, accommodation of new technologies such as Internet of Things and machine to machine communication becomes difficult. So it is essential to obtain a wireless spectrum to adopt new technologies. Cognitive radio technology is introduced to improve such spectrum utilization. Reports state that most of the licensed spectrums are underutilized, and few spectrums are overutilized. Cognitive radio networks help to exploit the licensed spectrum and access the spectrum without any interference to the licensed user. Through its spectrum sensing and spectrum sharing process, cognitive radio network gains more attention in wireless communication. This research work proposed an efficient optimized spectrum sensing technique for cognitive radio networks through dragonfly optimization algorithm along with the adaptive threshold process. Proposed work performs better in terms of detection accuracy and efficiency when compared to conventional spectrum sensing schemes such as linear support vector machine and particle swarm optimization models.

Implementation and Performance Analysis of Cognitive Radio with Frequency Updating Algorithm on Software-defined Radio Platform

Wireless communication is being used in all communication standards. However, with each passing day, the bandwidth scarcity has become a significant concern for the upcoming wireless technologies. In order to address this concern, various techniques based on artificial intelligence have been designed. The basic intelligent radio called cognitive radio has been devised. It works on the basic principle of spectrum sensing and detecting the free frequency for transmission of the secondary user, who is an unlicensed user. This work proposes an efficient technique that has been developed to design cognitive radio based on SDR platform. The frequency updating algorithm has been added for the performance assessment of the proposed technique. The analysis posits that for every 10dB rise in Gaussian Noise, the bit error rate of secondary transmitter and spectrum sensor, cause an increment of 19.59% and 29.39%, respectively. It has been found that spectrum sensor is more prone to noise and that the Gaussian noise degrades the performance of the system. Therefore, it is pertinent that the spectrum sensor should be programmed carefully. This analysis shows that the best range of spectrum sensor under Gaussian noise is 0 to 0.1dB and the bit error rate is within this specified range.

Interference mitigation with spectrum sharing and aggregation for SDN based cognitive networks

The Cognitive Radio based Software Defined Network (CR-SDN) integrated with 5G technology provides a promising solution for improving wireless broadband coverage. Cognitive radio technology allows users to communicate with an unlicensed spectrum when it is empty. The proposed Cognitive Radio based Software Defined Networking architecture (CR-SDN) allows Wireless Fidelity (Wi-Fi) and Long Term Evolution (LTE) users to access the Television White Space (TVWS) network whenever there is no vacant band available in Wi-Fi and LTE networks. Wi-Fi and there is a possibility whenever the LTE band interference between two users communicating in the same frequency band simultaneously. This interference should be mitigated, and the spectrum should be shared efficiently among the available users. In this paper, the proposed Interference Mitigation with Spectrum Sharing and Aggregation (IMSSA) technique mitigates the interference and shares the spectrum effectively among licensed and unlicensed users. This technique also aggregates the vacant spectrum within the licensed band (TV band and LTE band) and shares the aggregated band among LTE and Wi-Fi users for achieving a higher data rate. Adaptive Q Learning also made based on the spectrum management adaptive spectrum management, spectrum and a frequency band selection scheme used include dynamic access. This offers high spectrum efficiency and aggregation efficiency for ensuring network performance. The mobility management framework which is adopted in the proposed algorithm undertakes an efficient handover process whenever link failure occurs. In this proposed method, to evaluate the performance following parameter there are transmission rate, primary user analysis and transmission power.

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.

Investigations on detection and prevention of primary user emulation attack in cognitive radio networks using extreme machine learning algorithm

Cognitive radio is a competent technology which resolves the spectrum scarcity crisis in a network by allocating free spectrum dynamically and establishes coexistence between the users of the network, without interfering with the incumbent transmission. Primary user emulation attack (PUEA) is one of the major threats to the spectrum sensing performance that decreases the spectrum access probability of the unlicensed users of the network. In this paper Detection and prevention of PUEA is realized proficiently using Time-Distance with signal Strength Evaluation (TDSE) and Extreme Machine Learning (EML) algorithm. TDSE implementation makes it feasible to identify the malicious PUE attacker under mobile user stipulation and EML algorithm provides a swift comparative decision regarding the malicious attack. The technique is also proven to improve the sensing ability, energy efficiency and brings down the overall delay of the system, illustrating a considerable improvement in the overall network performance and spectrum utilization in a cognitive radio network.