Mobile Cognitive Radio 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

An Improved Selfish Node Detection Algorithm for Cognitive Radio Mobile Ad Hoc Networks

An Improved Selfish Node Detection Algorithm for Cognitive Radio Mobile Ad Hoc Networks

An Efficient Hybrid Analysis to Improve Data Rate Signal Transmission in Cognitive Radio Networks Using Multi- Hop

Spectrum scarcity problems can be resolved with the emerging communiqué technologies known as cognitive radio (CR). Cognitive radio networks (CRNs) will give mobile users greater bandwidth via wirelessly heterogeneity design and dynamic spectrum acquisition methods. The Cognitive Radio Mobile Ad-Hoc Network (CR-MANET) idea of Adaptive Routing a new network paradigm may be realized by using the functions of spectrum management to overcome such difficulties. Secondary users (SUs) have the freedom to opportunistically explore and make use of the open spaces on licensed channels. When a primary user (PU) interferes with a licensed channel, this forces the SU to leave it and switch to an open channel. Because of the constant channel switching those results, SUs degrades as a result. In this result recommends a number of channels, number of hop CRN that uses a fuzzy decision-making system that is genetically optimized for channel selection, channel switching, and spectrum allocation. According to study, the suggested architecture achieves higher PDR, throughput, latency, and transmission time than fuzzy and genetic algorithms. Through simulations the result demonstrates significant improvements in data rate performance, making it a promising solution for enhancing communication efficiency in 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.

High-isolated, low correlation and wide frequency reconfigurable (2.1–4.2 GHz) MIMO antenna for cognitive radio applications

ABSTRACT This paper proposes a unique and small (2508 mm2) penta-band frequency agile MIMO antenna based on RF switches for cognitive radio mobile, WLAN, WiMAX, and wireless sensing applications. Three RF switches generate five resonant bands at 2.1, 3, 3.5, 4, and 4.2 GHz in four usable states-100, 011, 111, 100, and 000, respectively. The proposed antenna's fractional bandwidth was 4.95 % (simulated) and 5.5 % (measured) in state-000, 3.09 % (simulated) and 2.8 % (measured) at a lower frequency (2.1 GHz), 2.12 % (simulated) and 2 % (measured) at higher frequency (4.2 GHz) in state-100, 12.5 % (simulated) and 12 % (measured) in state-011, and 4.6 % (simulated) and 4.7 % (measured) in state-111. A large peak gain of 3.6 dB at 4 GHz, maximum isolation of 32.2 dB at 3.5 GHz, significant radiation efficiency of 93.7% at 4GHz is obtained. At 4 GHz, a strong peak gain of 3.6 dB, maximum isolation of 32.2 dB at 3.5 GHz, and considerable radiation efficiency of 93.7 % are realised. For this work, MIMO parameters such as ECC (approx. 0), DG (approx.10dB), TARC (<0.5), MEG (−12 dB < MEG (dB) < −3 dB) and CCL (< 0.5 bit/s/Hz) were achieved. The suggested antenna was simulated, constructed, and tested, and the simulated results were found to be very close to the experimental findings.

A Deep Reinforcement Learning-Based QoS Routing Protocol Exploiting Cross-Layer Design in Cognitive Radio Mobile Ad Hoc Networks

In this paper, we propose a novel deep reinforcement learning-based quality-of-service (QoS) routing protocol, namely DRQR, exploiting cross-layer design to establish efficient QoS (EQS) routes in cognitive radio mobile ad hoc networks. An EQS route is a route with minimum end-to-end (E2E) queuing delay subject to QoS constraints such as link stability, residual energy, number of hops and avoiding licensed channels of primary users. Particularly, we propose an NP-complete optimization problem which has a feasible solution as an EQS route. To tackle this problem, we design a new deep reinforcement learning model which supports the DRQR protocol to establish EQS routes in real time by offline training instead of online training like most of literature studies. Moreover, the DRQR protocol guarantees to have high system performance. A mathematical analysis of the E2E queuing delay with random waypoint mobility model also provides to verify simulation results. Numerical results show that the DRQR protocol outperforms state-of-the-art routing protocols in terms of routing delay, queuing delay, control overhead, PDR and energy consumption.

Microstrip Rotman Lens for Mobile Base Station Backbone in Disaster Area Networks

Mobile Cognitive Radio Base Station (MCRBS) is a technology to recover network communication temporarily in disaster areas. The communications can be built by constructing the backbone network, where it is formed by connecting multiple MCRBSs with point-to-point radio communication link considering the best routing algorithm. The MCRBS antenna system should has an ability on producing the beam into specific direction to establish point-to-point radio communication link. However, there is no capable system to support the MCRBS antenna system for directing the beam. Therefore, it requires a system that can assist in focusing the beam of antenna system for the MCRBSs connectivity. In this paper, we propose Rotman Lens that has beamforming capability to support radio backbone communication for multiple MCRBSs connectivity in post-disaster networks. The Rotman Lens is chosen to generate amplitude and phase shift in performing beamforming technique with simultaneous signal transmissions from MCRBS to others without moving the antenna system. In this research, the Rotman Lens was evaluated through a series of computer simulations and laboratory experiments to investigate the lens performance. The final proposed lens provides the experiment results for all beam ports with an average value both return loss and mutual coupling of less than −15 dB and −20 dB, where the magnitude deviation in average value and the maximum error of phase difference are 1.172 dB and 6.014º , respectively. The proposed Rotman Lens is also confirmed to be capable of controlling beam with scanning capability of −27º , −14º , 0º , 14º , 27º to support wireless communication backbone link among MCRBSs in disaster recovery networks

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.

On Sensing Performance of Multi-Antenna Mobile Cognitive Radio Conditioned on Primary User Activity Statistics

In limited space scenarios, the antennas in a multiantenna cognitive radio (CR) system are closely spaced and often experience correlation among them. In this paper, the sensing performance of arbitrarily correlated antennas over the Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading channel for the mobile CR user is analysed. In particular, the analytical expression for the average detection probability for a mobile CR user employing the selection combining with arbitrarily correlated triple diversity branches is derived as a special case. Moreover, to characterize the performance of an energy detector under mobility, the area under the curve of a receiver operating characteristic is analysed. Furthermore, as the sensing decisions can be utilized to improve the sensing performance, a comprehensive analysis of sensing performance of the mobile secondary user conditioned on the primary user (PU) activity statistics is carried out. We assume the PU traffic to be following the Discrete-Time Markov Chain (DTMC) model, and its <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$idle$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$busy$ </tex-math></inline-formula> periods to be following generalised Pareto distribution. The analytical framework is substantiated by Monte Carlo simulations. Results indicate that antenna correlation deteriorates detection performance. Moreover, the detection performance deteriorates further due to the mobility of CR users, especially in the deep fading channel scenarios. Furthermore, findings also suggest that under mobility, the sensing performance improves if the duty cycle of the PU channel is low. This work provides a realistic sensing framework for CR enabled vehicles.

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.

Multi-Adaptive Routing Protocol for Internet of Things based Ad-hoc Networks

Internet of Things (IoT) based applications are being evolved in multiple fields to provide enhanced service to the world. IoT is a recent computing concept interconnecting the wired and wireless networks through the internet. Most mobile devices function only in an ad-hoc manner. Infrastructureless networks are called ad-hoc networks. IoT is an effective technology to utilize in Cognitive Radio Mobile Ad-hoc Network (CRMANET) instantaneously. The protocols that are developed for common ad-hoc networks will never suit for IoT-based-CRMANET because the delay they face is inversely proportional with real-time applications. Hence, there exists a need for designing and developing a better routing protocol that suits IoT-based ad-hoc networks. Multi adaptive route indicates the optimum cum efficient path which is selected when the priority of the node gets changed or failed, it may be due to problems that arise in nodes or network components. Multi-adaptive routes make sure the connectivity of the network and its operations before sending the data packet. This paper focuses on developing a Multi-Adaptive Routing Protocol (MARP) inspired by natural characteristics of fish for IoT-based ad-hoc networks to minimize the delay and the energy consumption to extend a network lifetime. NS3 simulation results indicate that MARP gives its best performance than other routing protocols in terms of Throughput, Packet Delivery Ratio, Packet Drop Ratio, Delay and Energy Consumption.

A New Deep Q-Network Design for QoS Multicast Routing in Cognitive Radio MANETs

In this paper, we propose a new deep Q-network (DQN) design for quality-of-service (QoS) multicast routing (DQMR) protocol to establish efficient QoS multicast (EQM) trees in cognitive radio mobile ad hoc networks (CR-MANETs). An EQM tree is a shortest-path multicast tree with minimum end-to-end (E2E) cost (a combination of queuing size ratio and link stability) subject to QoS constraints such as queuing size ratio, link stability, number of hops, number of time slots and avoiding the licensed channel of primary users. Particularly, we propose an NP-complete optimization problem such that its feasible solution is an EQM tree. To address this problem, we design a new DQN model and a new game-based model to form EQM trees in real-time by offline training instead of online training as done in previous papers. Moreover, the DQMR protocol is also guaranteed to have high stability, low routing delay, low control overhead, and high packet delivery ratio (PDR). Furthermore, one more new contribution of the paper is that exact closed-form expressions for the E2E queuing delay of a multicast routing tree are also derived assuming random waypoint mobility and the reference point group mobility models to compare with simulation results of routing delay. Simulation results show that the DQMR protocol outperforms multicast ad hoc on-demand distance vector routing protocol in terms of routing delay, control overhead, and PDR.

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.

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.

A Game Theory Based Clustering Protocol to Support Multicast Routing in Cognitive Radio Mobile Ad Hoc Networks

The frequent movement of nodes in cognitive radio mobile ad hoc networks (CRAHNs) causes challenges in scalability, stability, channel sensing and channel access problems that can be solved by using clustering technique. Game theory is a feasible approach to solve such problems by casting clustering problems as distributed optimization problems. The main contributions of this article are as follows. Firstly, we propose a minimum connected weighted inner edge spanning tree (MWIEST) game to find an approximate solution of a MWIEST problem in CRAHNs. In this game, a link-weight function of each link is designed based on a combination of link-stability and link-connectivity ratio functions. Secondly, we prove that the MWIEST game is an exact potential game that exists at least one Nash equilibrium ( NE ) point which is an approximate solution of the MWIEST problem. Besides, we also prove that best responses ( BR s) of the game converge to a NE in finite iterations. Thirdly, based on the MWIEST game, we propose four algorithms including the node information exchange (NIE), the best response selection (BRS), the intermediate nodes selection (INS) and the forming cluster (FC). Specifically, the algorithms NIE, BRS and INS provide a set of intermediate nodes (SetIN) which supports the FC algorithm to form clusters. Finally, we propose the game theory based clustering (GBC) protocol which is combination of the FC algorithm and the proposed cluster maintenance algorithm to construct high stable clusters supporting multicast routing in CRAHNs. Moreover, each obtained cluster includes most members having the same receiving channel which avoids the affected regions of licensed channels. For the performance evaluation, we implement the GBC protocol in OMNET++ platform to demonstrate its performance improvement over the state-of-the-art protocols in terms of network stability and control overheads.

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.

Cooperative Learning for Spectrum Management in Railway Cognitive Radio Network

High-speed rail public broadband wireless access requires communication terminals with high mobility to perform seamless cell switching in a harsh and fast time-varying environment. However, frequent switching caused by fast motion inevitably leads to unpredictable high volatility of the available spectrum, resulting in inefficient wireless communication. The application of highly mobile cognitive radio technology may address this challenge. This paper first analyzes the physical structure of the existing railway wireless communication network, and determines the characteristics of chainlike distribution and cascade operation of the base stations along the railway. Furthermore, using Bayesian reinforcement learning and multi-agent theory, a distributed cognitive base station model suitable for railway wireless environment is constructed. Based on the joint spectrum management of cascaded base station groups, this model proposes a multi-agent coordination algorithm with the goal of global optimal communication performance of the entire rail line. Finally, this paper evaluates the network performance of various test scenarios, and proves that the cognitive base station multi-agent cascade cooperative system can significantly increase the probability of successful data transmission and greatly reduce wireless spectrum handovers. This proposed scheme provides a brand-new solution for solving the problem of low wireless spectrum efficiency in the typical railway wireless network.

인지 무선 모바일 애드혹 네트워크를 위한 다중 메트릭 기반 멀티 홉 클러스터링 기법

최근 애드 혹 네트워크 환경에서 인지 무선 기술에 대한 연구가 활발히 진행되고 있는데 특히, 효율적인 네트워킹을 위해 가용 주파수 채널을 기반으로 한 클러스터링 기법들이 제안되었다. 그러나, 기존 연구들에서는 모바일애드 혹 네트워크의 특성(단말의 이동성, 홉 수에 따른 성능저하 등)이 크게 고려되지 않은 채 가용 채널의 상태만을 고려하여 클러스터를 형성하기 때문에 빈번한 클러스터 재구성 및 이에 따른 통신 안정성 저하, 네트워크 유지보수 오버헤드 문제가 발생한다. 본 논문에서는 이러한 문제를 해결하기 위해 가용 채널의 개수와 더불어 단말의 위상 정보 및 이동성 정보 등을 바탕으로 클러스터를 구성하도록 하는 기법을 제안한다. 시뮬레이션을 통해 성능검증 결과, 기존 연구들 대비 클러스터링 오버헤드와 통신비용에서 향상된 성능 결과를 보이는 것을 확인하였다.