Mobile Cognitive Radio Networks Research Articles

Nonuniform Sampling Pattern Design for Compressed Spectrum Sensing in Mobile Cognitive Radio Networks

Nonuniform Sampling Pattern Design for Compressed Spectrum Sensing in Mobile Cognitive Radio Networks

PUE Attack Detection by Using DNN and Entropy in Cooperative Mobile Cognitive Radio Networks

The primary user emulation (PUE) attack is one of the strongest attacks in mobile cognitive radio networks (MCRN) because the primary users (PU) and secondary users (SU) are unable to communicate if a malicious user (MU) is present. In the literature, some techniques are used to detect the attack. However, those techniques do not explore the cooperative detection of PUE attacks using deep neural networks (DNN) in one MCRN network and with experimental results on software-defined radio (SDR). In this paper, we design and implement a PUE attack in an MCRN, including a countermeasure based on the entropy of the signals, DNN, and cooperative spectrum sensing (CSS) to detect the attacks. A blacklist is included in the fusion center (FC) to record the data of the MU. The scenarios are simulated and implemented on the SDR testbed. Results show that this solution increases the probability of detection (PD) by 20% for lower signal noise ratio (SNR) values, allowing the detection of the PUE attack and recording the data for future reference by the attacker, sharing the data for all the SU.

Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

Cognitive Radio Networks (CRNs) have become a successful platform in recent years for a diverse range of future systems, in particularly, industrial internet of things (IIoT) applications. In order to provide an efficient connection among IIoT devices, CRNs enhance spectrum utilization by using licensed spectrum. However, the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel selection. Specifically, the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User (PU) activity and create a robust routing path. This study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT domain. Thus, a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method, namely, Channel Availability Probability. Moreover, a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation technique. This protocol combines lower layer (Physical layer and Data Link layer) sensing that is derived from the channel estimation model. Also, it periodically updates and stores the routing table for optimal route decision-making. Moreover, in order to achieve higher throughput and lower delay, a new routing metric is presented. To evaluate the performance of the proposed protocol, network simulations have been conducted and also compared to the widely used routing protocols, as a benchmark. The simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio (with an improved margin of around 5–20% approximately) under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks (MCRNs). Moreover, the cross-layer routing protocol successfully achieves high routing performance in finding a robust route, selecting the high channel stability, and reducing the probability of PU interference for continued communication.

Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks.

Mobile cognitive radio networks (MCRNs) have arisen as an alternative mobile communication because of the spectrum scarcity in actual mobile technologies such as 4G and 5G networks. MCRN uses the spectral holes of a primary user (PU) to transmit its signals. It is essential to detect the use of a radio spectrum frequency, which is where the spectrum sensing is used to detect the PU presence and avoid interferences. In this part of cognitive radio, a third user can affect the network by making an attack called primary user emulation (PUE), which can mimic the PU signal and obtain access to the frequency. In this paper, we applied machine learning techniques to the classification process. A support vector machine (SVM), random forest, and K-nearest neighbors (KNN) were used to detect the PUE in simulation and emulation experiments implemented on a software-defined radio (SDR) testbed, showing that the SVM technique detected the PUE and increased the probability of detection by 8% above the energy detector in low values of signal-to-noise ratio (SNR), being 5% above the KNN and random forest techniques in the experiments.

Performance of Machine Learning-Based Techniques for Spectrum Sensing in Mobile Cognitive Radio Networks

Communication technologies are evolving drastically in recent years. However, the scarcity of spectrum began to appear with the accelerating usage of various communication technologies, as well as the preservation of traditional channel access methods. Cognitive Radio (CR) is an innovative solution for spectrum scarcity. Spectrum sensing is a key task of the CR life-cycle that gains significance as spectrum holes can be detected during this task. This paper studies and compares the performance of the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KMeans-based spectrum sensing technique</b> with the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">non-cooperative spectrum sensing technique</b> , the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">And-based spectrum sensing technique</b> , and the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Or-based spectrum sensing technique in stationary and mobile CR networks (CRNs).</b> The effect of the fading channel type has also been considered. Small-scale CRNs were simulated using the third version of the network simulator. In this context, two models have been developed. The first was built based on the well-known <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa -\mu $ </tex-math></inline-formula> general fading model to simulate the fading effects. The latter is the noise model to simulate different noise conditions. The results reveal that spectrum sensing techniques provide better performance in stationary networks as compared to mobile networks. Further, our experimental results show that at least three secondary users and about 1500 samples are needed to reach acceptable performance. In addition, the results show that the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">KMeans-based technique</b> slightly outperforms the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Or-based technique</b> , especially in highly noisy environments and under severe fading channels.

Joint Clustering Method and Reinforcement Learning-Based Spectrum Management in Mobile Cognitive Radio Networks

The spectrum management scheme is a crucial issue to utilize the limited resource and secure a higher quality of performance in mobile cognitive radio networks. We propose a spectrum management method to utilize the spectrum effectively and minimize the communication cost in mobile cognitive radio networks. We consider the following parts such as statistical information about spectrum access patterns and the number of spectrum hand-off. In addition, this paper proposes to make the parameters to decide the spectrum access of the secondary user during the primary user doesn’t use it. Those parameters are based on dynamic clustering and reinforcement learning. However, both the dynamic clustering method and reinforcement learning haven’t been studied much to minimize communication costs in mobile cognitive radio networks. Firstly, our study is to improve the opportunity of spectrum access through the analysis based on the spectrum patterns and the number of both the secondary user and the primary user in mobile cognitive radio networks. Secondly, we decrease the number of hand-offs because it checks the status of the available channel and the patterns of the spectrum usage in the clustering. Above all, the improvement of this paper is to cut down the cost of communication by utilizing the way of the optimal spectrum access in mobile cognitive radio networks.

Detection of Malicious Primary User Emulation Based on a Support Vector Machine for a Mobile Cognitive Radio Network Using Software-Defined Radio

Mobile cognitive radio networks provide a new platform to implement and adapt wireless cellular communications, increasing the use of the electromagnetic spectrum by using it when the primary user is not using it and providing cellular service to secondary users. In these networks, there exist vulnerabilities that can be exploited, such as the malicious primary user emulation (PUE), which tries to imitate the primary user signal to make the cognitive network release the used channel, causing a denial of service to secondary users. We propose a support vector machine (SVM) technique, which classifies if the received signal is a primary user or a malicious primary user emulation signal by using the signal-to-noise ratio (SNR) and Rényi entropy of the energy signal as an input to the SVM. This model improves the detection of the malicious attacker presence in low SNR without the need for a threshold calculation, which can lead to false detection results, especially in orthogonal frequency division multiplexing (OFDM) where the threshold is more difficult to estimate because the signal limit values are very close in low SNR. It is implemented on a software-defined radio (SDR) testbed to emulate the environment of mobile system modulations, such as Gaussian minimum shift keying (GMSK) and OFDM. The SVM made a previous learning process to allow the SVM system to recognize the signal behavior of a primary user in modulations such as GMSK and OFDM and the SNR value, and then the received test signal is analyzed in real-time to decide if a malicious PUE is present. The results show that our solution increases the detection probability compared to traditional techniques such as energy or cyclostationary detection in low SNR values, and it detects malicious PUE signal in MCRN.

Adaptive quantization for spectrum exchange information in mobile cognitive radio networks

To reduce the detection failure of the exchanging signal power onto the OFDM subcarrier signal at uniform quantization, dynamic subcarrier mapping is applied. Moreover, to addressing low SNR’s wall-less than pre-determine threshold, non-uniform quantization or adaptive quantization for the signal quantization size parameter is proposed. μ-law is adopted for adaptive quantization subcarrier mapping which is deployed in mobility environment, such as Doppler Effect and Rayleigh Fading propagation. In this works, sensing node received signal power then sampled into a different polarity positive and negative in μ-law quantization and divided into several segmentation levels. Each segmentation levels are divided into several sub-segment has representing one tone signal subcarrier number OFDM which has the number of quantization level and the width power. The results show that by using both methods, a significant difference is obtained around 8 dB compared to those not using the adaptive method.

Rényi Entropy-Based Spectrum Sensing in Mobile Cognitive Radio Networks Using Software Defined Radio.

A very important task in Mobile Cognitive Radio Networks (MCRN) is to ensure that the system releases a given frequency when a Primary User (PU) is present, by maintaining the principle to not interfere with its activity within a cognitive radio system. Afterwards, a cognitive protocol must be set in order to change to another frequency channel that is available or shut down the service if there are no free channels to be found. The system must sense the frequency spectrum constantly through the energy detection method which is the most commonly used. However, this analysis takes place in the time domain and signals cannot be easily identified due to changes in modulation, power and distance from mobile users. The proposed system works with Gaussian Minimum Shift Keying (GMSK) and Orthogonal Frequency Division Multiplexing (OFDM) for systems from Global System for Mobile Communication (GSM) to 5G systems, the signals are analyzed in the frequency domain and the Rényi-Entropy method is used as a tool to distinguish the noise and the PU signal without prior knowledge of its features. The main contribution of this research is that uses a Software Defined Radio (SDR) system to implement a MCRN in order to measure the behavior of Primary and Secondary signals in both time and frequency using GNURadio and OpenBTS as software tools to allow a phone call service between two Secondary Users (SU). This allows to extract experimental results that are compared with simulations and theory using Rényi-entropy to detect signals from SU in GMSK and OFDM systems. It is concluded that the Rényi-Entropy detector has a higher performance than the conventional energy detector in the Additive White Gaussian Noise (AWGN) and Rayleigh channels. The system increases the detection probability (PD) to over 96% with a Signal to Noise Ratio (SNR) of 10dB and starting 5 dB below energy sensing levels.

Performance optimisation of cooperative spectrum sensing in mobile cognitive radio networks

Cooperative spectrum sensing is a key technology of cognitive radio networks (CRNs) to the reliability of spectrum sensing but is prone to be affected by a series of user characteristics, which results in a serious decline in the throughput of CRNs and the harmful interference to the primary user network. The performance analysis and optimisation of existing cooperative spectrum-sensing schemes do not completely cover key user characteristics. In this study, the joint effects of key user characteristics are studied with the objective to determine the parameters that affect the cooperative spectrum-sensing functionality. To this aim, the mobile CRN model and spatial–temporal spectrum-sensing model are formulated. Furthermore, the authors propose a dynamic double threshold energy detection (DDTED) scheme to derive the miss-detection probability and false alarm probability involving user characteristics. Moreover, the performance optimisation is carried out by a dynamic threshold factor. Finally, simulation results show that a variety of user characteristics have different effects on the performance of cooperative spectrum sensing, and the proposed DDTED scheme can achieve better performance than the existing approach.

Detection of dynamic location primary user emulation on mobile cognitive radio networks using USRP

The study and the detection of possible network attacks are essential for wireless networks, in particular for mobile cognitive radio networks due to its characteristics such as the dynamic spectrum allocation and constant frequency hopping. The primary user emulation attack is one of the most significant attacks in cognitive radio, because it hazards the complete cognitive cycle. The techniques used for the detection of primary user emulation found in the literature are based on a fixed attacker location. However, in a mobile environment, the attacker usually has dynamic locations and this compromises the current applied security techniques and generates inefficient attack detection. Therefore, our work proposes a novel technique using cross-layer design for the detection of primary user emulation with mobility. This attack detection technique was tested with experiments using software-defined radio equipment and mobile phones at indoor scenarios with dynamic locations and with a mobile phone base station built up also with software-defined radio. The obtained results show that the combination of the three utilized techniques, energy detection, motion estimation, and application information analysis, are able to optimize the detection with around 100% of effectiveness for the primary user emulation attack with dynamic location. The proposed technique shows that the energy detection time is around 100 ms and for the processing time of the information analysis in the mobile phone is about 30 s. This result shows a practical and effective approach to detect primary emulation attacks. The proposed technique, to the best of the authors’ knowledge, has not been presented before in the literature with experiments neither with mobility conditions of the attacker as presented in our proposed work.

Primary user emulation characterization in mobile cognitive radio networks

This paper presents the results of the characterization of the attack known as the "primary user emulation" in mobile cognitive radio networks performing the implementation and testing. The tools and their configuration to carry out the attack are presented and their effects on the network are analyzed. The results show how to generate the attack with a software-defined radio equipment (SDR) using GNU-Radio and OpenBTS. The effects of the possible configurations of the attack on the network are shown, the malicious type generates constant interference on the primary or cognitive network, the selfish type allows to imitate a licensed or primary user generating interference to the primary network and inability to access the Cognitive Network while active. If the emulator's power level is fixed, the services it provides are stable. If the power is variable the services suffer intermittency. Primary user emulation is the attack that most affects the cognitive radio network so its effects are analyzed in order to propose ways of detecting or applying countermeasures.

Smart Routing Management Framework Exploiting Dynamic Data Resources of Cross-Layer Design and Machine Learning Approaches for Mobile Cognitive Radio Networks: A Survey

The concept of Cognitive Radio (CR) has emerged as a practical solution to solve the issue of the fixed spectrum and bandwidth scarcity in wireless communication. However, the nature of dynamic Mobile Cognitive Radio Networks (MCRNs) drives to the emergence of new challenges, especially concerning the routing protocol operations. Applying a cross-layer design is considered a sufficient remedy to overcome routing protocol challenges such (e.g. channel diversity, integration route discovery with spectrum decision, mobility, etc.). Consequently, the cross-layer design has a magic solution to overwhelm routing challenges in MCRNs due to the ability to be free from the strict boundary and share the information and services with other layers in a manner that contributes to enhancing routing performance. Thus, the scope of this survey is to review and taxonomy numerous routing protocols in MCRNs according to methods of design to highlight the strength and weakness points. Also, machine learning has acquired much interest in this literature. A cross-layer framework for smart routing protocol in MCRNs has been proposed by exploiting machine learning mechanisms. Finally, the open research issues of routing protocol in MCRNs are summed up.

Intermittently Associated Mobile Cognitive Radio Networks Using Portability Assisted Routing

Intermittently Associated Mobile Cognitive Radio Networks Using Portability Assisted Routing

Intermittently Associated Mobile Cognitive Radio Networks using Portability Assisted Routing

In versatile specially appointed intellectual radio systems, with accessible range groups for optional clients may or may not exist incidentally, because of the dynamism of the essential client exercises. Customary CRN steering calculations, which normally overlook the discontinuous availability of system topology, and customary portability helped directing calculations, which for the most part disregard the range accessibility, are clearly unacceptable. To handle this test, aAssisted Mobility Routing calculation with Spectrum Awareness (MARSA)is proposed to choose transfers in light of not just the likelihood that a hub meets the goal additionally the possibility that exists no less than one accessible channel. This dissertation is proposed to bring the possibility of versatility helped steering to manage the irregularly associated characteristic of versatile impromptu CRNs, and the first to improve the portability helped directing by considering the worldly, spatial, and range spaces in the meantime. Our reenactment comes about show the predominance of MARSA over customary calculations in discontinuously associated portable CRNs.

Hybrid spectrum access model using game theory approach for multi-channel heterogeneous mobile cognitive radio wireless sensor network

&lt;p&gt;An efficient spectrum access and medium access control (MAC) design is required to enable wireless sensor network (WSN) with cognitive Internet of Things (IoT) that enables presence of sensor network with existing wireless infrastructure. Designing spectral, energy efficient and fair spectrum access design for IoT in CR-WSN is challenging especially for heterogeneous mobile CR-WSN. Existing model induces high collision and energy overhead. As a result, they are not efficient in utilizing spectrum. To overcome the research issues, this paper proposes a hybrid spectrum access model (HSAM) for multi-channel heterogeneous mobile CR-WSN. Next it elaborates on an accurate feasible channel accessible likelihood estimation. Further, we present a game theory model for HSAM for existence of fine-grained Nash equilibrium. Experiments are conducted to evaluate the performance of HSAM-CRWSN over existing model. The outcome shows HSAM-CRWSN attains significant performance improvement over state-of-art technique in terms of energy efficiency, throughput, successful packet transmission and packet collision. The HSAM-CRWSN improves the overall spectrum efficiency of CR-WSN.&lt;/p&gt;&lt;p&gt;&lt;span style="font-size: small;"&gt;&lt;br /&gt;&lt;/span&gt;&lt;/p&gt;

Full-duplex mobile cognitive radio network under Nakagami-m fading environment

Energy detection based non-cooperative and cooperative spectrum sensing methods are considered in this paper for a full-duplex (FD) cognitive radio (CR) network. The CRs are assumed to be mobile and the wireless channels between CR(s) and primary transmitter (PT) are considered as time-selective. Such time-selective channels are characterized using Nakagami-m fading. The effect of mobility is captured using Jake’s model. The expressions for the false alarm and detection probabilities are derived under the influence of Nakagami-m distributed residual self-interference (RSI). Moreover, with double threshold at CR, we analyze the receiver operating characteristics (ROC), area under ROC curve (AUC) and the total error rate performances. All the analytical results are validated through Monte Carlo simulations.

Prediction-Based Channel Selection Prediction in Mobile Cognitive Radio Network

The emerging 5G wireless communications enabled diverse multimedia applications and smart devices in the network. It promises very high mobile traffic data rates, quality of service as in very low latency and improvement in user’s perceived quality of experience compared to current 4G wireless network. This encourages the increasing demand of significant bandwidth which results a significant urge of efficient spectrum utilization. In this paper, modelling, performance analysis and optimization of future channel selection for cognitive radio network by jointly exploiting both CR mobility and primary user activity to provide efficient spectrum access is studied. The modelling and prediction method is implemented by using Hidden Markov Model algorithm. The movement of CR in wireless network yields location-varying spectrum opportunities. The current approaches in most literatures which only depend on reactive selection spectrum opportunities result of inefficient channel usages. Moreover, conventional random selection method tends to observe a higher handoff and operation delays in network performance. This inefficiency can cause continuous transmission interruptions leading to the degradation of advance wireless services. This work goal is to improve the performance of CR in terms number of handoffs and operation delays. We perform simulation on our prediction strategy with a commonly used random sensing method with and without location. Through simulations, it is shown that the proposed prediction and learning strategy can obtain significant improvements in number of handoffs and operation delays performance parameters. It is also shown that future CR location is beneficial in increasing mobile CR performance. This study also shows that the number of primary user in the network and the PU protection range affect the performance of mobile CR channel selection for all methods.

L-CAQ: Joint link-oriented channel-availability and channel-quality based channel selection for mobile cognitive radio networks

Channel availability probability (CAP) and channel quality (CQ) are two key metrics that can be used to efficiently design a channel selection strategy in cognitive radio networks. For static scenarios, i.e., where all the users are immobile, the CAP metric depends only on the primary users' activity whereas the CQ metric remains relatively constant. In contrast, for mobile scenarios, the values of both metrics fluctuate not only with time (time-variant) but also over different links between users (link-variant) due to the dynamic variation of primary- and secondary-users' relative positions. As an attempt to address this dynamic fluctuation, this paper proposes L-CAQ: a link-oriented channel-availability and channel-quality based channel selection strategy that aims to maximize the link throughput. The L-CAQ scheme considers accurate estimation of the aforementioned two channel selection metrics, which are governed by the mobility-induced non-stationary network topology, and endeavors to select a channel that jointly maximizes the CAP and CQ. The benefits of the proposed scheme are demonstrated through numerical simulation for mobile cognitive radio networks.

Anti-Shadowing Resource Allocation for General Mobile Cognitive Radio Networks

Resource allocation (RA) for mobile secondary users is considered one of the most important techniques for designing the next-generation cognitive radio network (CRN). In this paper, effective capacity (EC) is proposed to improve the RA performance for an underlay-based mobile CRN. By optimizing EC, an efficient resource allocation scheme is developed. First, we consider a moving secondary system, where the channel state information can be predicted by location-awareness techniques. According to the prediction result, we set protecting parameters for both the secondary and the primary performance targets to minimize the prediction error introduced by the decorrelated shadowing. Second, the computed sum average EC of the cognitive system is maximized. To solve the optimization RA problem, a low-complexity stepwise algorithm is proposed based on four procedures aimed at access, initialization, subchannel, and power. Moreover, the speeds of the primary users are taken into account, and a general system model is built. The corresponding resource allocation solutions can be induced easily through extending the originally proposed solution. Finally, simulation results are provided to confirm the EC-based algorithm. The proposed approach can not only improve the total secondary capacity but also achieve higher energy efficiency and spectrum efficiency for the mobile secondary system.