cognitive radio research papers

Exploiting 5G Enabled Cognitive Radio Technology for Semantic Analysis in Social Networks

• Published: 22 January 2024
• Volume 133 , pages 1585–1598, ( 2023 )

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• Sumeyye Bayrakdar   ORCID: orcid.org/0000-0002-8148-1090 1 &
• Ibrahim Yucedag 1  

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Cognitive radio is an intelligent communication system that is aware of its environment and can dynamically adapt its operating parameters with the aim of providing an efficient use of the scarce spectrum. The main advantage of cognitive radio technology is its ability to adapt and cooperate with all other wireless technologies such as fifth generation technology, 5G. 5G enabled cognitive radio technology provides accelerated communication performance in accordance with spectrum efficiency and energy efficiency. 5G enabled cognitive radio proposes system interoperability and integration of communication system through cognition. Social networking is a common communication media among internet users connected by one or more relationships. Large numbers of internet users share their experiences and thoughts through social networking web sites. Semantic analysis is defined as the process of drawing meaning from text. In this paper, a fuzzy logic based semantic analysis is performed for the estimation of comment content in 5G enabled cognitive radio based social networks. In social networks, the positive comments posted by the users have the positive influence for the members to examine related comments. The comment content posted by the users is decided to be positive or negative with the help of fuzzy logic based semantic analysis approach. In this regard, the relevant interpretation can be positive or negative based on the input parameters in the fuzzy logic system. Our 5G enabled cognitive radio technology based semantic analysis approach with fuzzy logic system can be utilized in many social networks, taking superior accuracy results of 93% into account.

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Huang, Y., Zhao, P., Zhang, Q., Xing, L., Wu, H., & Ma, H. (2023). A Semantic-enhancement-based social network user-alignment algorithm. Entropy, 25 (1), 172. https://doi.org/10.3390/e25010172

Article   ADS   PubMed   PubMed Central   Google Scholar  

Liu, X., Zhang, Y., Yejun, Xu., Li, M., & Herrera-Viedma, E. (2023). A consensus model for group decision-making with personalized individual self-confidence and trust semantics: A perspective on dynamic social network interactions. Information Sciences, 627 , 147–168. https://doi.org/10.1016/j.ins.2023.01.087

Article   Google Scholar  

Ramakrishna, M. T., et al. (2023). HCoF: Hybrid collaborative filtering using social and semantic suggestions for friend recommendation. Electronics, 12 (6), 1365. https://doi.org/10.3390/electronics12061365

Li, H., Song, J. B., Chen, C.-F., Lai, L., & Qiu, R. C. (2014). Behavior propagation in cognitive radio networks: A social network approach. IEEE Transactions on Wireless Communications, 13 (2), 646–657. https://doi.org/10.1109/TW.2013.121813.121964

Deng, Y., Gou, F., & Wu, J. (2021). Hybrid data transmission scheme based on source node centrality and community reconstruction in opportunistic social networks. Peer-to-Peer Networking and Applications, 14 , 3460–3472. https://doi.org/10.1007/s12083-021-01205-3

Zhang, X., et al. (2021). Effective communication data transmission based on community clustering in opportunistic social networks in IoT system. Journal of Intelligent & Fuzzy Systems, 41 (1), 2129–2144.

Xiaokaiti, A., Qian, Y., & Wu, J. (2021). Efficient data transmission for community detection algorithm based on node similarity in opportunistic social networks. Complexity , 2021 , 9928771, 18 p. https://doi.org/10.1155/2021/9928771

Huang, Z., Li, X., & Wu, J. (2022). An effective data transmission scheme based on IoT system in opportunistic social networks. International Journal of Communication Systems, 35 (4), e5062. https://doi.org/10.1002/dac.5062

Lu, Y., Chang, L., Luo, J., & Wu, J. (2021). Routing algorithm based on user adaptive data transmission scheme in opportunistic social networks. Electronics, 10 (10), 1138. https://doi.org/10.3390/electronics10101138

Chen, S., Chen, Z., Wu, J., & Liu, K. (2020). An adaptive delay-tolerant routing algorithm for data transmission in opportunistic social networks. Electronics, 9 (11), 1915. https://doi.org/10.3390/electronics9111915

Li, X., & Wu, J. (2020). Node-oriented secure data transmission algorithm based on IoT system in social networks. IEEE Communications Letters, 24 (12), 2898–2902. https://doi.org/10.1109/LCOMM.2020.3017889

Xiao, Y., & Wu, J. (2020). Data transmission and management based on node communication in opportunistic social networks. Symmetry., 12 (8), 1288. https://doi.org/10.3390/sym12081288

Article   ADS   Google Scholar  

Yan, Y., Chen, Z., Wu, J., Wang, L., Liu, K., & Wu, Y. (2019). Effective data transmission strategy based on node socialization in opportunistic social networks. IEEE Access, 7 , 22144–22160. https://doi.org/10.1109/ACCESS.2019.2898895

Wu, J., Chen, Z., & Zhao, M. (2020). An efficient data packet iteration and transmission algorithm in opportunistic social networks. Journal of Ambient Intelligence and Humanized Computing, 11 , 3141–3153. https://doi.org/10.1007/s12652-019-01480-2

Article   CAS   Google Scholar  

Yan, Y., Chen, Z., Wu, J., & Wang, L. (2018). An effective data transmission algorithm based on social relationships in opportunistic mobile social networks. Algorithms, 11 (8), 125. https://doi.org/10.3390/a11080125

Wu, J., Zou, W., & Long, H. (2021). Effective path prediction and data transmission in opportunistic social networks. IET Communications, 15 , 2202–2211. https://doi.org/10.1049/cmu2.12254

Khalaf, O. I., Ogudo, K. A., & Singh, M. (2021). A fuzzy-based optimization technique for the energy and spectrum efficiencies trade-off in cognitive radio-enabled 5G network. Symmetry, 13 (1), 47. https://doi.org/10.3390/sym13010047

Article   ADS   CAS   Google Scholar  

Mavromoustakis, C. X., Bourdena, A., Mastorakis, G., et al. (2015). An energy-aware scheme for efficient spectrum utilization in a 5G mobile cognitive radio network architecture. Telecommunication Systems, 59 , 63–75. https://doi.org/10.1007/s11235-014-9885-4

Zhang, W., Wang, C.-X., Ge, X., & Chen, Y. (2018). Enhanced 5G cognitive radio networks based on spectrum sharing and spectrum aggregation. IEEE Transactions on Communications, 66 (12), 6304–6316. https://doi.org/10.1109/TCOMM.2018.2863385

Li, A., & Han, G. (2018). A fairness-based MAC protocol for 5G Cognitive Radio Ad Hoc Networks. Journal of Network and Computer Applications , 111 , 28–34. ISSN 1084–8045, https://doi.org/10.1016/j.jnca.2018.03.019

Hu, F., Chen, B., & Zhu, K. (2018). Full spectrum sharing in cognitive radio networks toward 5G: A survey. IEEE Access, 6 , 15754–15776. https://doi.org/10.1109/ACCESS.2018.2802450

Ahmad, W. S. H. M. W., et al. (2020). 5G technology: Towards dynamic spectrum sharing using cognitive radio networks. IEEE Access, 8 , 14460–14488. https://doi.org/10.1109/ACCESS.2020.2966271

Silva, A. L. C., et al. (2023). Prescriptive and semantic analysis of an automatic sign language translation: Cases on VLibras Avatar translation using video interviews and textual interactions with a Chatbot. Interacting with Computers, 35 (2), 231–246. https://doi.org/10.1093/iwc/iwac020

Laurini, R. (2022). Semantic analysis of feedforward knowledge for regional policymaking. In Laurini, R., Nijkamp, P., Kourtit, K., Bouzouina, L. (Eds.) Knowledge management for regional policymaking . Springer. https://doi.org/10.1007/978-3-031-15648-9_5

Chen, C.-M., Li, M.-C., & Huang, Y.-L. (2023). Developing an instant semantic analysis and feedback system to facilitate learning performance of online discussion. Interactive Learning Environments, 31 (3), 1402–1420. https://doi.org/10.1080/10494820.2020.1839505

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Computer Engineering Department, Duzce University, Duzce, Turkey

Sumeyye Bayrakdar & Ibrahim Yucedag

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S.B., wrote the main manuscript text; prepared figures. I.Y., contributed data or analysis tools; conceived and designed the analysis. All authors reviewed the manuscript.

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Bayrakdar, S., Yucedag, I. Exploiting 5G Enabled Cognitive Radio Technology for Semantic Analysis in Social Networks. Wireless Pers Commun 133 , 1585–1598 (2023). https://doi.org/10.1007/s11277-023-10829-y

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Accepted : 21 December 2023

Published : 22 January 2024

Issue Date : December 2023

DOI : https://doi.org/10.1007/s11277-023-10829-y

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To understand cognition — and its dysfunction — neuroscientists must learn its rhythms

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It could be very informative to observe the pixels on your phone under a microscope, but not if your goal is to understand what a whole video on the screen shows. Cognition is much the same kind of emergent property in the brain .  It can only be understood by observing how millions of cells act in coordination, argues a trio of MIT neuroscientists. In a new article ,  they lay out a framework for understanding how thought arises from the coordination of neural activity driven by oscillating electric fields — also known as brain “waves” or “rhythms.”

Historically dismissed solely as byproducts of neural activity, brain rhythms are actually critical for organizing it, write Picower Professor Earl Miller and research scientists Scott Brincat and Jefferson Roy in  Current Opinion in Behavioral Science . And while neuroscientists have gained tremendous knowledge from studying how individual brain cells connect and how and when they emit “spikes” to send impulses through specific circuits, there is also a need to appreciate and apply new concepts at the brain rhythm scale, which can span individual, or even multiple, brain regions.

“Spiking and anatomy are important, but there is more going on in the brain above and beyond that,” says senior author Miller, a faculty member in The Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT. “There’s a whole lot of functionality taking place at a higher level, especially cognition.”

The stakes of studying the brain at that scale, the authors write, might not only include understanding healthy higher-level function but also how those functions become disrupted in disease.

“Many neurological and psychiatric disorders, such as schizophrenia, epilepsy, and Parkinson’s, involve disruption of emergent properties like neural synchrony,” they write. “We anticipate that understanding how to interpret and interface with these emergent properties will be critical for developing effective treatments as well as understanding cognition.”

The emergence of thoughts

The bridge between the scale of individual neurons and the broader-scale coordination of many cells is founded on electric fields, the researchers write. Via a phenomenon called “ephaptic coupling,” the electrical field generated by the activity of a neuron can influence the voltage of neighboring neurons, creating an alignment among them. In this way, electric fields both reflect neural activity and also influence it. In a  paper in 2022 , Miller and colleagues showed via experiments and computational modeling that the information encoded in the electric fields generated by ensembles of neurons can be read out more reliably than the information encoded by the spikes of individual cells. In 2023 Miller’s lab provided evidence that rhythmic electrical fields may  coordinate memories  between regions.

At this larger scale, in which rhythmic electric fields carry information between brain regions, Miller’s lab has published numerous studies showing that lower-frequency rhythms in the so-called “beta” band originate in deeper layers of the brain’s cortex and  appear to regulate  the power of faster-frequency “gamma” rhythms in more superficial layers. By recording neural activity in the brains of animals engaged in working memory games, the lab has shown that beta rhythms carry “top-down” signals to control when and where gamma rhythms can encode sensory information, such as the images that the animals need to remember in the game.

Some of the lab’s latest evidence suggests that beta rhythms apply this control of cognitive processes to physical patches of the cortex, essentially acting like stencils that pattern where and when gamma can encode sensory information into memory, or retrieve it. According to this theory, which Miller calls “ Spatial Computing ,” beta can thereby establish the general rules of a task (for instance, the back-and-forth turns required to open a combination lock), even as the specific information content may change (for instance, new numbers when the combination changes). More generally, this structure also enables neurons to flexibly encode more than one kind of information at a time, the authors write, a widely observed neural property called “mixed selectivity.” For instance, a neuron encoding a number of the lock combination can also be assigned, based on which beta-stenciled patch it is in, the particular step of the unlocking process that the number matters for.

In the new study, Miller, Brincat, and Roy suggest another advantage consistent with cognitive control being based on an interplay of large-scale coordinated rhythmic activity: “subspace coding.” This idea postulates that brain rhythms organize the otherwise massive number of possible outcomes that could result from, say, 1,000 neurons engaging in independent spiking activity. Instead of all the many combinatorial possibilities, many fewer “subspaces” of activity actually arise, because neurons are coordinated, rather than independent. It is as if the spiking of neurons is like a flock of birds coordinating their movements. Different phases and frequencies of brain rhythms provide this coordination, aligned to amplify each other, or offset to prevent interference. For instance, if a piece of sensory information needs to be remembered, neural activity representing it can be protected from interference when new sensory information is perceived.

“Thus the organization of neural responses into subspaces can both segregate and integrate information,” the authors write.

The power of brain rhythms to coordinate and organize information processing in the brain is what enables functional cognition to emerge at that scale, the authors write. Understanding cognition in the brain, therefore, requires studying rhythms.

“Studying individual neural components in isolation — individual neurons and synapses — has made enormous contributions to our understanding of the brain and remains important,” the authors conclude. “However, it’s becoming increasingly clear that, to fully capture the brain’s complexity, those components must be analyzed in concert to identify, study, and relate their emergent properties.”

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