Machine Learning for Spectrum Access and Sharing

Cohen, Kobi

doi:10.1002/9781119562306.ch1

Cited by 3 publications

(5 citation statements)

References 76 publications

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“…Fortunately, the emergence of the paradigm of integrated sensing and communication (ISAC), that is regarded as an emerging technology towards 6G and next generation WLAN [24], provides potential solutions for coexistence and information exchange between heterogeneous systems. On the other hand, the decentralized machine learning theory provides a mathematical tool for studying scenarios with incomplete information in wireless environments, thereby avoiding the heavy signaling overhead brought on by the frequent exchange of global information [25][26][27]. Therefore, ISAC and decentralized learning approaches can be applied to the shared use of unlicensed bands for obtaining efficient solutions.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Sharing Unlicensed Millimeter-Wave Bands in Heterogeneously Integrated Sensing and Communication Networks

Tang,

Liu

2023

Electronics

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Due to the increasing demand of high data rate, spectrum scarcity is a key problem for providing unprecedented capacity in diversified applications for future wireless networks. Therefore, the efficiently shared use of unlicensed bands is one of the promising solutions for addressing the spectrum scarcity issue. We study decentralized machine learning approaches using the paradigm of integrated sensing and communication (ISAC) for the shared use of unlicensed millimeter-wave bands. We first present a 5G–WiFi fusion protocol stack for sharing unlicensed millimeter-wave bands, and then design an ISAC-based access protocol and an ISAC based coexistence protocol integrated with decentralized learning function to achieve the efficiently shared use of unlicensed bands. Using the coexistence protocol, we propose promising decentralized machine learning approaches to share unlicensed millimeter-wave bands. Finally, simulations are provided to verify the performance of the proposed scheme, where the results have shown that the proposed scheme greatly reduces the search space of the solution and effectively protects the communication performance of the WiFi system compared to traditional schemes, which indicates that simultaneous transmissions of 5G-U and WiFi at the 60 GHz band are feasible under the proposed scheme.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches for Sharing Unlicensed Millimeter-Wave Bands in Heterogeneously Integrated Sensing and Communication Networks

Tang,

Liu

2023

Electronics

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“…A result close to zero means discarding the information, while a result close to one means keeping the information. The forget gate F t equation at the time (t) is as follows [62]:…”

mentioning

confidence: 99%

“…Hence, the input gate has been used to update the cell state. The input gate I t at the time (t) is computed as [62]:…”

mentioning

confidence: 99%

“…The output gate identified the output values that should be in the next hidden state. The equation for computing the output gate O t is [62]:…”

mentioning

confidence: 99%

“…where σ is sigmoid function, X t denotes the input at a time (t), W O and U O represents the weight matrix of forget gate, the bias is represented by b O while the hidden activation is described by H t−1 at time (t−1). Moreover, the cell state C t at the time (t) and hidden activation H t at time (t) can be computed as [62]:…”

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confidence: 99%

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Arabic Tweets-Based Sentiment Analysis to Investigate the Impact of COVID-19 in KSA: A Deep Learning Approach

Alqarni

Rahman

2023

BDCC

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The World Health Organization (WHO) declared the outbreak of Coronavirus disease 2019 (COVID-19) a pandemic on 11 March 2020. The evolution of this pandemic has raised global health concerns, making people worry about how to protect themselves and their families. This has greatly impacted people’s sentiments. There was a dire need to investigate a large amount of social data such as tweets and others that emerged during the post-pandemic era for the assessment of people’s sentiments. As a result, this study aims at Arabic tweet-based sentiment analysis considering the COVID-19 pandemic in Saudi Arabia. The datasets have been collected in two different periods in three major regions in Saudi Arabia, which are: Riyadh, Dammam, and Jeddah. Tweets were annotated with three sentiments: positive, negative, and neutral after due pre-processing. Convolutional neural networks (CNN) and bi-directional long short memory (BiLSTM) deep learning algorithms were applied for classifying the sentiment of Arabic tweets. This experiment showed that the performance of CNN achieved 92.80% accuracy. The performance of BiLSTM was scored at 91.99% in terms of accuracy. Moreover, as an outcome of this study, an overwhelming upsurge in negative sentiments were observed in the dataset during COVID-19 compared to the negative sentiments of the dataset before COVID-19. The technique has been compared with the state-of-the-art techniques in the literature and it was observed that the proposed technique is promising in terms of various performance parameters.

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Electromagnetic emission measurement prediction of buck-boost converter circuits using machine learning methods

Sakacı

Yener

2023

Journal of Electromagnetic Waves and Applications

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Machine Learning for Spectrum Access and Sharing

Cited by 3 publications

References 76 publications

Machine Learning Approaches for Sharing Unlicensed Millimeter-Wave Bands in Heterogeneously Integrated Sensing and Communication Networks

Machine Learning Approaches for Sharing Unlicensed Millimeter-Wave Bands in Heterogeneously Integrated Sensing and Communication Networks

Arabic Tweets-Based Sentiment Analysis to Investigate the Impact of COVID-19 in KSA: A Deep Learning Approach

Electromagnetic emission measurement prediction of buck-boost converter circuits using machine learning methods

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