Cognitive Radio Networks Channel State Estimation Using Machine Learning Techniques

Tarek, Dina; Benslimane, Abderrahim; Darwish, M.; Kotb, Amira

doi:10.1109/iwcmc.2019.8766457

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…The satellite image-based path loss prediction impact is also seen with improved results. Two HMM-based protocols have been proposed in [230]. For the protocols, the radio data is classified into two groups, and each group uses a different HMM model to predict the availability of channels in cognitive radios.…”

Section: Smart Channel Modeling and Channel Predictionmentioning

confidence: 99%

A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions

2022

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The context of this study examines the requirements of Future Intelligent Networks (FIN), solutions, and current research directions through a survey technique. The background of this study is hinged on the applications of Machine Learning (ML) in the networking field. Through careful analysis of literature and real-world reports, we noted that ML has significantly expedited decision-making processes, enhanced intelligent automation, and helped resolve complex problems economically in different fields of life. Various researchers have also envisioned future networks incorporating intelligent functions and operations with the ML. Several efforts have been made to automate individual functions and operations in the networking domain; however, most of the existing ML models proposed in the literature lack several vital requirements. Hence, this study aims to present a comprehensive summary of the requirements of FIN and propose a taxonomy of different network functionalities that needs to be equipped with ML techniques. The core objectives of this study are to provide a taxonomy of requirements envisioned for end-to-end FIN, relevant ML techniques, and their analysis to find research gaps, open issues, and future research directions. The real benefit of machine learning applications in any domain can only be ensured if intelligent capabilities cover all its components. We observed that future generations of networks are heterogeneous, multi-vendor, and multidimensional, and ML can provide optimal results only if intelligent capabilities are used on a holistic scale. Realizing intelligence on a holistic scale is only possible if the ML algorithms can solve heterogeneous problems in a multi-vendor and multidimensional environment. ML models must be reliable and efficient, support distributed learning architecture, and possess the capability to learn and share the knowledge across the network layers and administrative domains to solve issues. Firstly, this study ascertains the requirements of the FIN and proposes their taxonomy through reviews on envisioned ideas by various researchers and articles gathered from reputed conferences and standard developing organizations using keyword queries. Secondly, we have reviewed existing studies on ML applications focusing on coverage, heterogeneity, distributed architecture, and cross-domain knowledge learning and sharing. Our study observed that in the past, ML applications were focused mainly on an individual/isolated level only, and aspects of global and deep holistic learning with cross-layer/domain knowledge sharing with agile ML operations are not explored at large. We recommend that the issues mentioned above be addressed with improved ML architecture and agile operations and propose ML pipeline-based architecture for FIN. The significant contribution of this study is the impetus for researchers to seek ML models suitable for a modular, distributed, multi-domain and multi-layer environment and provide decision-making on a global or holistic rather than individual function level.

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Section: Smart Channel Modeling and Channel Predictionmentioning

confidence: 99%

A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions

2022

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“…Machine learning (ML) refers to the design, analysis and application of techniques that allow machines to move through processes and actions, complex or even impractical, learning using more traditional algorithms [9].…”

Section: Definitionmentioning

confidence: 99%

Cooperative Spectrum Sensing using Rule based Hard Decision and Soft Decision with Bayesian Optimized Support Vector Machine

2022

IJIES

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Cognitive radio technology has emerged as an operational substitute for increasing the number of users of broadband data services in various frequency bands. This article presents the results of an analysis of the effectiveness of the fusion rule for joint spectrum detection in cooperative spectrum sensing (CSS). The energy detectors-based feature vector is considered for ML training purpose. The paper proposes support vector machine (SVM) based modelling for training and testing in CR. Further Bayesian optimized SVM is proposed to claim higher detection rate. The proposed method yields 0.84 detection rate at 0.1 probability of false alarm.

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“…We note that the finite-density models, which are extensively discussed in this paper, can provide better guidelines on the OLA protocol design and operation in practice. The OLA techniques presented in this paper can be exploited in wireless sensor networks, IoT, communications and signal processing [49][50][51][52][53][54][55], which are representative topics covered in Sensors. Furthermore, the OLA protocols can be employed in small sensors to improve their energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Opportunistic Large Array Propagation Models: A Comprehensive Survey

Nawaz

Kumar

Hassan

et al. 2021

Sensors

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Enabled by the fifth-generation (5G) and beyond 5G communications, large-scale deployments of Internet-of-Things (IoT) networks are expected in various application fields to handle massive machine-type communication (mMTC) services. Device-to-device (D2D) communications can be an effective solution in massive IoT networks to overcome the inherent hardware limitations of small devices. In such D2D scenarios, given that a receiver can benefit from the signal-to-noise-ratio (SNR) advantage through diversity and array gains, cooperative transmission (CT) can be employed, so that multiple IoT nodes can create a virtual antenna array. In particular, Opportunistic Large Array (OLA), which is one type of CT technique, is known to provide fast, energy-efficient, and reliable broadcasting and unicasting without prior coordination, which can be exploited in future mMTC applications. However, OLA-based protocol design and operation are subject to network models to characterize the propagation behavior and evaluate the performance. Further, it has been shown through some experimental studies that the most widely-used model in prior studies on OLA is not accurate for networks with networks with low node density. Therefore, stochastic models using quasi-stationary Markov chain are introduced, which are more complex but more exact to estimate the key performance metrics of the OLA transmissions in practice. Considering the fact that such propagation models should be selected carefully depending on system parameters such as network topology and channel environments, we provide a comprehensive survey on the analytical models and framework of the OLA propagation in the literature, which is not available in the existing survey papers on OLA protocols. In addition, we introduce energy-efficient OLA techniques, which are of paramount importance in energy-limited IoT networks. Furthermore, we discuss future research directions to combine OLA with emerging technologies.

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Cognitive Radio Networks Channel State Estimation Using Machine Learning Techniques

Cited by 7 publications

References 6 publications

A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions

A Survey on Requirements of Future Intelligent Networks: Solutions and Future Research Directions

Cooperative Spectrum Sensing using Rule based Hard Decision and Soft Decision with Bayesian Optimized Support Vector Machine

Opportunistic Large Array Propagation Models: A Comprehensive Survey

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