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Journal ArticleDOI

Recurrent Network Based Protocol Design for Spectrum Sensing in Cognitive Users

TL;DR: Simulation comparison of RCBCU using the network simulator with the existing channel bonding scheme shows efficient utilization of channel with fairness, delay and starvation ratio.
About: This article is published in Wireless Personal Communications.The article was published on 2022-06-27. It has received None citations till now. The article focuses on the topics: Computer science & Computer science.
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Journal ArticleDOI
TL;DR: A deep learning-based method, combined with two convolutional neural networks trained on different datasets, to achieve higher accuracy AMR, demonstrating the ability to classify QAM signals even in scenarios with a low signal-to-noise ratio.
Abstract: Automatic modulation recognition (AMR) is an essential and challenging topic in the development of the cognitive radio (CR), and it is a cornerstone of CR adaptive modulation and demodulation capabilities to sense and learn environments and make corresponding adjustments. AMR is essentially a classification problem, and deep learning achieves outstanding performances in various classification tasks. So, this paper proposes a deep learning-based method, combined with two convolutional neural networks (CNNs) trained on different datasets, to achieve higher accuracy AMR. A CNN is trained on samples composed of in-phase and quadrature component signals, otherwise known as in-phase and quadrature samples, to distinguish modulation modes, that are relatively easy to identify. We adopt dropout instead of pooling operation to achieve higher recognition accuracy. A CNN based on constellation diagrams is also designed to recognize modulation modes that are difficult to distinguish in the former CNN, such as 16 quadratic-amplitude modulation (QAM) and 64 QAM, demonstrating the ability to classify QAM signals even in scenarios with a low signal-to-noise ratio.

489 citations

Journal ArticleDOI
TL;DR: In this paper, an end-to-end learning framework for spectrum data is presented, which can automatically learn features directly from simple wireless signal representations, without requiring design of hand-crafted expert features like higher order cyclic moments and train wireless signal classifiers in one end to end step without the need for complex multi-stage machine learning processing pipelines.
Abstract: This paper presents end-to-end learning from spectrum data—an umbrella term for new sophisticated wireless signal identification approaches in spectrum monitoring applications based on deep neural networks. End-to-end learning allows to: 1) automatically learn features directly from simple wireless signal representations, without requiring design of hand-crafted expert features like higher order cyclic moments and 2) train wireless signal classifiers in one end-to-end step which eliminates the need for complex multi-stage machine learning processing pipelines. The purpose of this paper is to present the conceptual framework of end-to-end learning for spectrum monitoring and systematically introduce a generic methodology to easily design and implement wireless signal classifiers. Furthermore, we investigate the importance of the choice of wireless data representation to various spectrum monitoring tasks. In particular, two case studies are elaborated: 1) modulation recognition and 2) wireless technology interference detection. For each case study three convolutional neural networks are evaluated for the following wireless signal representations: temporal IQ data, the amplitude/phase representation, and the frequency domain representation. From our analysis, we prove that the wireless data representation impacts the accuracy depending on the specifics and similarities of the wireless signals that need to be differentiated, with different data representations resulting in accuracy variations of up to 29%. Experimental results show that using the amplitude/phase representation for recognizing modulation formats can lead to performance improvements up to 2% and 12% for medium to high SNR compared to IQ and frequency domain data, respectively. For the task of detecting interference, frequency domain representation outperformed amplitude/phase and IQ data representation up to 20%.

225 citations

Journal ArticleDOI
TL;DR: The factors that influence the selection of the transmission power, including the important interplay between the routing (network) and the medium access control (MAC) layers are discussed, and protocols that account for such interplay are presented.
Abstract: Recently, power control in mobile ad hoc networks has been the focus of extensive research. Its main objectives are to reduce the total energy consumed in packet delivery and/or increase network throughput by increasing the channel's spatial reuse. In this article, we give an overview of various power control approaches that have been proposed in the literature. We discuss the factors that influence the selection of the transmission power, including the important interplay between the routing (network) and the medium access control (MAC) layers. Protocols that account for such interplay are presented.

221 citations

Journal ArticleDOI
25 Mar 2009
TL;DR: The Java Modelling Tools (JMT) suite is presented, an integrated framework of Java tools for performance evaluation of computer systems using queueing models that offers a rich user interface that simplifies the definition of performance models by means of wizard dialogs and of a graphical design workspace.
Abstract: We present the Java Modelling Tools (JMT) suite, an integrated framework of Java tools for performance evaluation of computer systems using queueing models. The suite offers a rich user interface that simplifies the definition of performance models by means of wizard dialogs and of a graphical design workspace.The performance evaluation features of JMT span a wide range of state-of-the-art methodologies including discrete-event simulation, mean value analysis of product-form networks, analytical identification of bottleneck resources in multiclass environments, and workload characterization with fuzzy clustering. The discrete-event simulator supports several advanced modeling features such as finite capacity regions, load-dependent service times, bursty processes, fork-and-join nodes, and implements spectral estimation for analysis of simulative results. The suite is open-source, released under the GNU general public license (GPL), and it is available for free download at: http://jmt.sourceforge.net.

219 citations

Journal ArticleDOI
TL;DR: A cautionary perspective on drawing strong conclusions based on the often limited amount of data gathered is proposed, especially regarding spatial domain considerations and the impact of the sampling interval on the results.
Abstract: In order to provide meaningful data about spectrum use, occupancy measurements describing the utilization rate of a specific frequency band should be conducted over a specific area instead of a single location. This paper presents a comprehensive methodology for the measurement and analysis of spectrum occupancy. This paper surveys spectrum measurement campaigns and associated interference maps, introducing the latter as a tool for spectrum analysis and management based on measurement data. An interference map characterizes the spectrum use by defining the level of interference over an area of interest in a certain frequency band. Building on findings from practical measurement studies, guidelines for spectrum occupancy measurements are given. While many scientific spectrum occupancy measurement papers tend to be too optimistic about the significance and generality of the results, we propose a cautionary perspective on drawing strong conclusions based on the often limited amount of data gathered. The different phases of the spectrum occupancy measurement and analysis process are described and a thorough discussion of interpolation methods is provided. Means to improve the measurement accuracy are discussed, especially regarding spatial domain considerations and the impact of the sampling interval on the results. A practical example of an improved measurement system design covering all the phases of the measurement process and used at the Turku, Finland; Blacksburg, VA, USA; and Chicago, IL, USA, spectrum observatories is given. Using the improved design, more realistic spectrum occupancy data can be obtained to lay the foundation for spectrum management decisions.

199 citations