Robust Spectrum Sensing Detector Based on MIMO Cognitive Radios with Non-Perfect Channel Gain

Al-Amidie, Muthana; Al-Asadi, Ahmed; Humaidi, Amjad J.; Al-Dujaili, Ayad Q.; Alzubaidi, Laith; Farhan, Laith; Fadhel, Mohammed A.; McGarvey, Ronald G.; Islam, Naz E.

doi:10.3390/electronics10050529

Cited by 7 publications

(11 citation statements)

References 54 publications

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“…Figure 1 depicts the notion of transfer learning in which a trained model that has been used on a bigger dataset may be applied on a smaller dataset and provide a satisfactory result. The transfer learning approach has recently been used to great success in a variety of industries, including medical picture categorization, manufacturing, and baggage scanning [26].…”

Section: Transfer Learning Conceptmentioning

confidence: 99%

Identifying corn leaves diseases by extensive use of transfer learning: a comparative study

Hatem

Altememe

Fadhel

2023

IJEECS

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<span lang="EN-US">Deep learning is currently playing an important role in image analysis and classification. Diseases in maize diminish productivity, which is a major cause of economic damages in the agricultural business throughout the world. Researchers have previously utilized hand-crafted characteristics to classify images and identify leaf illnesses in Maize plants. With the advancement of deep learning, researchers can now significantly enhance the accuracy of object classification and identification. Using the "Corn or Maize Leaf Disease Dataset" from the Kaggle website, four forms of maize leaf diseases were investigated: blight, common rust, gray leaf spot, and healthy. The pictures obtained from these corn leaf illnesses are categorized using four deep convolutional neural network (CNN) models that have been pre-trained (GoogleNet, AlexNet, ResNet50 and VGG16). Accuracy, precision, specificity, recall, F-score, and time are the six metrics used to assess the performance of any transfer learning (TL) model. MATLAB programming software is used to design and train the TL models. The accuracy of each item in the dataset has been checked. It has been determined that GoogleNet, AlexNet, VGG16, and ResNet50 each have an accuracy of 98.57%, 98.81%, 99.05%, and 99.36%, respectively.</span>

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Section: Transfer Learning Conceptmentioning

confidence: 99%

Identifying corn leaves diseases by extensive use of transfer learning: a comparative study

Hatem

Altememe

Fadhel

2023

IJEECS

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“…In WSNs, sensor nodes capable of detecting the required information, performing some processing and communicating with other connected nodes are the main component of these networks. However, the life of these nodes is often restricted by being powered by a battery with a limited life, constraining processing ability, memory, and radio communications [16]. Energy efficiency is one of the most crucial issues for WSNs.…”

Section: Motivation Of the Researchmentioning

confidence: 99%

“…A WSN is similar to a wireless ad hoc network, since both are self-organized and multihop networks [16,56]. WSN is used to monitor and record specific phenomena and cooperatively pass data wirelessly through a gateway (base station/sink) to a central location as shown in Figure 6.…”

Section: Wsn Communication Architecturementioning

confidence: 99%

Energy Efficiency for Green Internet of Things (IoT) Networks: A Survey

Farhan

Hameed

Ahmed

et al. 2021

Network

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The last decade has witnessed the rise of the proliferation of Internet-enabled devices. The Internet of Things (IoT) is becoming ever more pervasive in everyday life, connecting an ever-greater array of diverse physical objects. The key vision of the IoT is to bring a massive number of smart devices together in integrated and interconnected heterogeneous networks, making the Internet even more useful. Therefore, this paper introduces a brief introduction to the history and evolution of the Internet. Then, it presents the IoT, which is followed by a list of application domains and enabling technologies. The wireless sensor network (WSN) is revealed as one of the important elements in IoT applications, and the paper describes the relationship between WSNs and the IoT. This research is concerned with developing energy-efficiency techniques for WSNs that enable the IoT. After having identified sources of energy wastage, this paper reviews the literature that discusses the most relevant methods to minimizing the energy exhaustion of IoT and WSNs. We also identify the gaps in the existing literature in terms of energy preservation measures that could be researched and it can be considered in future works. The survey gives a near-complete and up-to-date view of the IoT in the energy field. It provides a summary and recommendations of a large range of energy-efficiency methods proposed in the literature that will help and support future researchers. Please note that the manuscript is an extended version and based on the summary of the Ph.D. thesis. This paper will give to the researchers an introduction to what they need to know and understand about the networks, WSNs, and IoT applications from scratch. Thus, the fundamental purpose of this paper is to introduce research trends and recent work on the use of IoT technology and the conclusion that has been reached as a result of undertaking the Ph.D. study.

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“…The term generalized likelihood ratio test (GLRT) is appearing in [1], [14], [15]. This algorithm uses the statistic test that describes the probability of assuming the presence and absence of the PU using threshold level for decision.…”

Section: Introductionmentioning

confidence: 99%

“…The concept of MIMO CSS has since been considered [1]. The FC uses the GLRT performed in which the unknown noise correlation matrix with CSI is imperfect and perfect at the SUs [14]. Approaches for signal acquisition from STs are given in [15], where sensing systems are based on the likelihood ratio test (LRT) optimally assuming that perfect CSI between the SU and the FC.…”

Section: Introductionmentioning

confidence: 99%

Use Hierarchical Codebook to Improve the Primary User Detection in the Cognitive Radio’s Cooperative Spectrum Sensing

Tran

2021

EJECE

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Currently, the cognitive network is receiving much attention due to the advantages it brings to users. An important method in cognitive radio networks is spectrum sensing, as it allows secondary users (SUs) to detect the existence of a primary user (PU). Information of probability of false detection or warning about the PU is sent to a fusion center (FC) by the SUs, from which the FC will decide whether or not to allow the SUs to use the PU spectrum to obtain information. The transmission of information with a high signal to noise ratio (SNR) will increase the FC's ability to detect the existence of the PU. However, researchers are currently focusing on probabilistic formulas assuming that the channel is known ideally or there is nominal channel information at the FC; moreover, one model where the FC only knows the channel correlation matrix. Furthermore, studies are still assuming this is a simple multiple input – multiple output (MIMO) channel model but do not pay much attention to the signal processing at the transmitting and receiving antennas between the SUs and the FCs. A new method introduced in this paper when combining beamforming and hierarchical codebook makes the ability to detect the existence of the PU at the FC significantly increased compared to traditional methods.

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Robust Spectrum Sensing Detector Based on MIMO Cognitive Radios with Non-Perfect Channel Gain

Cited by 7 publications

References 54 publications

Identifying corn leaves diseases by extensive use of transfer learning: a comparative study

Identifying corn leaves diseases by extensive use of transfer learning: a comparative study

Energy Efficiency for Green Internet of Things (IoT) Networks: A Survey

Use Hierarchical Codebook to Improve the Primary User Detection in the Cognitive Radio’s Cooperative Spectrum Sensing

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