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Multiple input multiple out (MIMO) cognitive radio offer the spatial degree of freedom that can be used to share the spectrum with less interference via Precoding Technique. Many precoding techniques in the literature assume that the underlying hardware is ideal. In the practical case, hardware adds many impairments at both the transmitter and rece...
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Citations
... The cognitive radio's physical layer security needs are addressed [4] practically. In the recent day, the security issues of a Cognitive Radio Network (CRN) are solved using the machine learning approaches [5][6][7][8]. Fundamentals of CRN is illustrated in Fig. 1. ...
The identification of the presence of primary user enhances the spectrum efficiency in cognitive radio (CR). The studies suggested that the existence of malicious user adversely affects the system performances; especially the primary user emulation attack (PUEA) has a greater influence in spectrum sensing on the CR network. Moreover, the detection of PUEA is a challenging and complex task and involves constructive design with sensing algorithm. In this study, a support vector machine (SVM) along with energy vectors is designed to improve the spectrum sensing mechanism. The presented approach integrates the SVM with the Bayesian optimization algorithm (BOA) in which SVM aims to detect the malicious user by randomly selecting the primary and secondary users. The BOA aims to optimize the hyperparameters of the SVM, thereby improving the detection performances and maximizes the algorithms convergence speed. The experimental analysis illustrate that the presented approach predicts the PUEA with 98% accuracy and reduces the average node power is 9.7. Moreover, the results demonstrated that the system performance does not vary on implementing it with the large-scale CR network. Finally, the system performances are compared and evaluated with existing techniques in terms of accuracy, and average noise power.
... Appropriate techniques are developed and similar techniques are integrated into the Fifth generation (5G) wireless communication system to meet these requirements. This combined effect of technologies fulfills essential requirements, including high achievable rate, user fairness, energy efficiency, high spectral efficiency, low latency and achieving extremely low error rate within a wireless communication system [3,4]. To fulfill these demands of achieving enhanced spectral efficiency and low-latent transmission, Non-Orthogonal Multiple Access (NOMA) is utilized as a channel accessing mechanism in 5G and beyond cellular systems [5,6]. ...
... Routing algorithms for Cognitive Radio Based IoT network analyzed with channel switching cost, end-to-end delay cost, energy efficiency and bandwidth dependent cost [3]. Machine learning and deep learning applied Cognitive IoT network provides much promising solution because it establishes mathematical models using observations, i.e., training data then the model can be used, to predict or make decisions [4][5][6]. Those approaches enable learning and improvement from experience automatically. ...
IoT network-connected devices are increasing day by day. It is impossible to allocate a spectrum for all IoT devices. This spectrum scarcity can be solved by cognitive radio-based dynamic spectrum sharing, which is referred to as Cognitive Radio Internet of Things (CRIoT). The jamming physical layer attack in CRIoT resulting in a denial of cognitive radio services and make spectrum underutilization. Continuous jamming can be detected quickly based on time delay on spectrum access, but discrete random jamming detection is challenging. This article proposes an autoencoder-based jamming attack detection in cognitive radio. The jamming detection problem is modeled as anomaly detection. The autoencoder is used to detect the anomaly signal component and the jammer. The simulation of random jamming attack detecting with time instant of attack is carried out to mitigate it. The proposed mechanism able to detect the jammer with 89% of accuracy.
... In this way, the farmers can also enhance the fertility of the soil and maximize their yield. Machine learning and deep learning methods are applied to solve many problems [20], especially in precision agriculture and plant disease detection with VR [21,22]. In this article, VR handset is used to ensure the farmer hands-free and to make them farming activity conveniently without intervention. ...
Augmented reality system enables effective interaction with field view data and executes a particular task effectively by visual display aid. Precision agriculture involves precision measurement, data generation, analysis for interpretation of data, and decision-making to improve the yield and monitor the plant. Augmented reality will help to systematically acquire the needed data and interpret the required information from the analytical result on the field. This paper presents a low-cost development of the augmented reality system for on-field analysis of plant diseases. The article also presents a framework of deep learning-based cloud data analytic to enable on-field real-time interaction between the farmers and cloud data processing systems using a head-mounted unit. The proposed augmented reality system performance is validated for its accuracy in detecting plant diseases, real-time interaction response time, and ease of usage by the farmer community. The results show that the proposed mechanism will be able to produce real-time augment interaction to the farmer for the task of disease inspection of the plant effectively and accurately.
... From the literature review [3], [5], [7] comparison of various algorithms is presented. Linear regression, SVM[has produced more accuracy than other algorithms [14][15]. In [4] ,prediction of diabetes is worked on SVM with 5-fold validation. ...
... Machine learning and deep learning applied Cognitive IoT network provides much promising solution because it establishes mathematical models using observations, i.e., training data then the model can be used, to predict or make decisions [15,24,25]. Those approaches enable learning and improvement from experience automatically. ...
IoT network-connected devices will be kept on increasing and will cross million, but it is impossible to allocate spectrum for those million and million of the devices. This spectrum scarcity can be handled by incorporating cognitive radio-based dynamic spectrum sharing, which is referred to as Cognitive Radio Internet of Things (CRIoT). But CRIoT sufferers from the Physical layer attack in cognitive radio, which affects the spectrum sensing accuracy and reduces the spectrum utilization. There are various attacks at the physical layer of cognitive radio among jamming attacks resulting in a denial of cognitive radio services and make spectrum underutilization. Continuous jamming can be detected quickly based on time delay on spectrum access, but discrete random jamming detection is challenging. This article proposes an autoencoder deep learning architecture-based jamming attack detection in cognitive radio. The jamming detection problem is modeled as anomaly detection. The autoencoder architecture is used to detect the jammer anomaly of the jammer. The proposed system involves the simulation of a random jamming attack and detecting it at a particular time instant information that may help mitigate the jammer attack .the proposed mechanism able to detect the jammer with 89% of accuracy.
... Machine learning and deep learning are used for wireless communication effectively [14]. Since there is no reported studies on receiver design in cell-free mMIMO based on radio stripes leveraging machine learning. ...
Abstract Cell‐free massive MIMO (mMIMO) has two distinctive advantages: first is, macrodiversity from large numbers of distributed access points and second, interference cancellation from cellular mMIMO is envisioned to be next‐generation wireless technology for beyond 5G. However, its practical deployment is extremely challenging due to large numbers of long cables (economic perspective) and network synchronisation. Cell‐free mMIMO system based on radio stripe (CFMMRS) network is one such architecture of cell‐free mMIMO suitable for practical deployment. This study proposes DNN‐based distributed sequential uplink processing for detecting symbols in the uplink of CFMMRS network architecture. Simulation results show that the proposed algorithm outperforms the traditional iterative soft interference cancellation‐based detection method.
... SDR or SDN implementation-based spectrum sensing and sharing facilitate programmability [14][15][16][17][18][19][20], which can be used for the implementation of the neural network for PUEA detection. ...
The spectrum sensing scheme suffers from a physical layer attack of Primary User Emulation Attack (PUEA). The resolution is to mitigate the cognitive radio user from the PUEA under the physical layer. Detecting the PUEA attack in real-time is a challenging one. The traditional Location-based PUEA detection requires the primary user's location knowledge, which may not be possible practically. This research focuses on developing a reliable spectrum sensing mechanism in the presence of PUEA attack and rapid change in the wireless channel. This reliable spectrum sensing framework is developed using the neural network-based PUEA detector excluding the location information. The Software-Defined Radio (SDR) called Universal Software Radio Peripheral (USRP) 2943R is used to implement the proposed mechanism for analyzing performance in real-time. The real-time experimental results show that PUEA detection can be achieved with 97% accuracy.
... Machine learning, soft computing, and deep learning mechanisms are applied to wireless communication for improved efficiency [19][20][21][22]. Excellent anti-noise efficiency is accomplished by high-order, statistically dependent algorithms, such as signal high-order cumulative and cyclic continuum. ...
The next generation of wireless communications networks will be very complex with the heterogeneous nature of nodes, radio access mechanisms, communication links, and network slices. The network will be more dynamic, which requires an intelligent system to address those complex heterogeneous scenarios and to adapt to the dynamics of wireless channels and environments. Machine Learning (ML) and Deep Learning (DL)-based solutions will be the optimal one for this problem. Machine learning and Deep learning driven algorithms can facilitate to analyze and make efficient resource management. © 2020, World Academy of Research in Science and Engineering. All rights reserved.
The next-generation 6G mobile networks are likely to be intelligent, highly dynamic, and extremely low latency to satisfy the needs of different diversified applications. With the increasing demand for wireless communications, resource management plays an essential role in providing higher data rates and extreme quality of service with the available resources. However, the complexity of allocating resources will become greater in the current ultra-dense heterogeneous infrastructures. With massive data and computing resources, the rapid progress of Artificial Intelligence (AI) eventually lightens the enormous capabilities needed for the future regularization of 6G and beyond. As a result, an AI-enabled network will be the most appropriate and suitable technique for intelligent resource management, automated network operations, and support in future complex 6G networks. This chapter will discuss the different machine learning techniques used for resource management in 6G networks, effective usage of available spectrum, prediction of the spectrum, and dynamic resource allocation.KeywordsResource managementMassive connectivityNetwork management
