A look at the synchronization of cognitive radio

Main Article Content

Danilo Alfonso López Sarmiento
Leydy Johana Hernández Viveros

Abstract

Objective. To propose the use of artificial intelligence tools as a model for the synchronization of the stages of decision and spectral sharing of cognitive radio. Methodology. The methodology used was exploratory-descriptive, collecting information chronologically organized over time and evaluating different elements that make up the cognitive radio in terms of synchronization between its stages. According to what was investigated and in order to carry out the research, each of the stages that make up the cognitive radio was analyzed for a total connection and synchronization, identifying in the decision and sharing stage what is advanced in the aspect of comprehensiveness between them. Originality. Of the investigated studies based on cognitive radio, the synchronization between its stages is not explored in any case to demonstrate an integral functionality of the system. Limitations. The different studies carried out on the stages of cognitive radio show that none of these separately co-index, identifying that for the development of the proposal, modeling must be carried out for both stages. Results. The results in the literary review found indicate that this synchronization has not been addressed giving space to a knowledge gap that is intended to attack. Conclusion. Cognitive radio is a methodology that proposes the dynamic management of the radio spectrum, integrating the stages of detection, decision making, sharing and spectral mobility. The spectral decision-making phase is responsible for deciding which is the best available channel to opportunistically transmit secondary user (SU) data, and its success depends on how efficient the primary user (PU) characterization model is. and the spectrum sharing phase will be in charge of coordinating access to the channel with other users in this way, seeking comprehensiveness between these stages.
 

References

-Arulkumaran, K., Deisenroth, M., Brundage, M., & Bharath, A. (2017). Deep reinforcement learning: A brief survey, Vol. 34 , no. 6,. IEEE Signal Processing Magazine, Vol. 34 (N° 6), 26-38.
-Cao, G., Lu, Z., Wen, X., Lei, T., & Hu, Z. (2018). AIF: An artificial intelligence framework for smart wireless network management. IEEE Communications Letters, Vol. 22(N° 2), 400-403.
-Caur, R., Buttar, A., & Anand, J. (2018). Spectrum sharing schemes in cognitive radio network: A survey. Proceeding of the 2nd International Conference on Electronics Communications and Aerospace Technology, 1279-1284.
-Caur, R., Buttar, A., & Anand, J. (2018). Spectrum sharing schemes in cognitive radio network: A survey. Proceeding of the 2nd International Conference on ElectronicsCommunications and Aerospace Technology, 1279-1284.
-Chin, W., Kao, C., Chen, H., & Liao, T. (2014). Iterative SynchronizationAssisted Detection of OFDM Signals in Cognitive Radio Systems. Vehicular Technology, IEEE Transactions, 63(4), 1633-1644.
-Federal Communications Commission. (2003). Notice of proposed rulemaking and order. Mexico D.F: Report ET Docket No: 03-332.
-Fortuna, C., & Mohorcic, M. (2009). Trends in the development of communication networks: Cognitive networks. Journal Computer Networks.,, 53(N° 9), 1354-1376. doi: https://doi.org/10.1016/j.comnet.2009.01.002.
-Gao, Z., Wen, B., Huang, L., Chen, C., & Su, Z. (2017). Q-Learning-based power control for LTE enterprise femtocell networks. IEEE Systems Journal, Vol. 11(No. 4), 2699-2707.
-Gopakumar, V., Tiwari, S., & Rahman, I. (2018). A deep learning based data driven soft sensor for bioprocesses. Journal Biochemical Engineering, 28-39.
-Hernández Sampieri, R., Collado, C. F., & Lucio, M. d. (2010). Metodología de la investigación. México D.F: McGraw Hill.
-Khalid, L., & Anpalagan, A. (2010). Emerging cognitive radio technology: Principles, challenges and opportunities. Elsevier, Computers and Electrical Engineering, 38(2), 358-366. doi: https://doi.org/10.1016/j. compeleceng.2009.03.004.
-Khomh, F., Adams, B., Cheng, J., & Fokaefs, M. (2018). Software Engineering for Machine-learning applications: The road ahead. IEEE Software, Vol. 35(No. 5), 81-84.
-López, D. (29 de Septiembre de 2017). Tesis doctoral: Implementación de un modelo predictor para la toma de decisiones en redes inalámbricas de radio cognitiva. Obtenido de Facultad de Ingenierías, Universidad Distrital Francisco José de Caldas: https://doctoradoingenieria.udistrital.edu.co/index.php/ es/inicio/documentos/repositorio-de-tesis-doctoral/item/488-implementacion-de-un-modelo-predictor-para-la-toma-de-decisiones-en-redes-inalambricas-de-radio-cognitiva
-López, D., Anzola, C., Zapata, D., & Rivas, E. (2018). Designing a MAC algorithm for equitable spectrum allocation in cognitive radio wireless networks. Journal Wireless Personal Communications, Vol. 98(N° 1), 135-145.
-López, D., Trujillo, E., & Gualdron, O. (2015). Elementos fundamentales que componen la radio cognitiva y asignación de bandas espectrales. Información tecnológica, 26(1), 23-40. doi: http://doi.org/10.4067/ S0718-07642015000100004.
-Maglogiannis, V., Naudts, D., Shahid, A., & Moerman, A. (2018). Q-Learning scheme for fair coexistence between LTE and Wi-Fi in unlicensed spectrum. IEEE Access, Vol. 6, 27278-27293.
-Mitola, J. (1999). Software radios - survey, critical evaluation and future directions. Proceedings of the National Telesystems Conference (NTC 1992, 13-23.
-Mohammadi, M., AlFuqaha, A., & Guizani, M. (2018). Semisupervised deep reinforcement learning in support of IoT and smart city servicesl, vol. 5 , no. 2,. IEEE Internet of Things Journal, Vol. 5(No. 2), 624635.
-Mohd, F., & Mohd, S. (2012). SVD Detection for Cognitive Radio Network based on Average of MaximumMinimum of the ICDF. International Journal of Advanced Computer Research (IJACR), 5. -Pedraza, L. (16 de 08 de 2016). Tesis doctoral: Modelo de propagación para un entorno urbano que identifica las oportunidades espectrales para redes móviles de radio cognitiva. Obtenido de Facultad de Ingeniería, Universidad Nacional de Colombia: http://bdigital.unal.edu.co/view/subjects/34.html.
-Peysakhovich, A., & Naecker, J. (2017). Using methods from machine learning to evaluate behavioral models of choice under risk and ambiguity. Journal of Economic Behavior and Organization, 373-384.
-Shared Spectrum Company. (2018). Spectrum reports: Spectrum occupancy measurement. General survey of radio frequency bands (30 MHz to 3 GHz). Vienna, Virginia: Shared Spectrum Company. Obtenido de http://www.sharedspectrum.com/wp-content/uploads/2010_0923-General-Band-Survey-30MHzto-3GHz.pdf.
-Shaw, S., Ghamri-Doudane, Y., Santos, A., & Nogueira, M. (2012). A reliable and distributed time synchronization for Cognitive Radio Networks. Global Information Infrastructure and Networking Symposium (GIIS), 1-19.
-Stergios, S., & Arumugam, N. (2011). Enhancing the Capacity of Spectrum Sharing Cognitive Radio Networks. IEEE Transactions on Vehicular Technology, 1-10.

Most read articles by the same author(s)