Abstract
This article presents a study focused on radio interfaces defined through HackRF One instrumentation used to develop block diagrams generating interference in the dynamic access of the spectrum. The present systems are designed by implementing algorithms with logical blocks and applying a Wi-Fi network analyzer through a Radio Defined by Software. The GNU operating system processes reusable signals for Frequency Hopping Spread Spectrum (FHSS) channel hopping to jam over an 802.11 b/g/n network. Therefore, the logic block diagram is implemented in a Radio system in a GNU to intervene in the radio transceiver. Thus, the transceiver generates the classification and stop of test signals by emitting an interference signal directed towards the network with a directional antenna. The 802.11 b/g/n connectivity speed tests evaluated at the installation of any device help to achieve the efficiency of the noise produced by the logical block that interferes with the signal to measure the degradation of connectivity. The results will allow us to determine whether Wi-Fi interference is possible through the logical development of blocks, providing information on the technique used and the initial optimization instructions possible during the test environment.
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References
Do, V.A., Rana, B., Hong, I.-P.: Identification of Wi-Fi and bluetooth signals at the same frequency using software defined radio. Journal of IKEEE 25(2), 252–260 (2021)
I. S. 8.-2.-I. S. f. I. Technology, “Telecommunications and Information Exchange between Systems—Local and Metropolitan Area Networks—Specific Requirements Part 11: Wireless LAN Medium Access Control (MAC) and Physical (PHY) Specification. In: IEEE IEEE, San Francisco, CA, USA (2012)
Machado, J.R.: Software Defined Radio: Basic Principles and Applications. 24(38), 7996 (2015)
Stewart, R.W., Barlee, K.W., Atkinson, D.S., Crockett, L.H.: Software defined radio using MATLAB & Simulink and the RTL-SDR, Strathclyde academic media (2015)
Jakubík, T., Jeníček, J.: SDR all-channels receiver for FHSS sensor network in Cortex-M. In: 2019 42nd International Conference on Telecommunications and Signal Processing (TSP), pp. 32–35 (2019). https://doi.org/10.1109/TSP.2019.8769064
Jaimes Rico, R., Salas, L.: Esquema de triangulación para detectar drones usando. 9(1) (2021)
Molla, D.M., Badis, H., George, L., Berbineau, M.: Software Defined Radio Platforms for Wireless Technologies 10 (2022)
Schwarz, F., Schwarz, K., Fuchs, D., Creutzburg, R., Akopian, D.L: Firmware Vulnerability Analysis of Widely Used Low-Budget TP-Link Routers. 33 (2021)
Patil, V., Singhal, C.: Throughput improvement in hybrid MIMO cognitive radio using simultaneous narrowband and wideband system. In: 2019 11th International Conference on Communication Systems & Networks (COMSNETS), pp. 285–290 (2019). https://doi.org/10.1109/COMSNETS.2019.8711135
Goldsmith, A., Jafar, S.A., Maric, I., Srinivasa, S.: Breaking spectrum gridlock with cognitive radios: an information theoretic perspective. Proceedings of the IEEE 97(5), 894–914 (2009)
Jeong, W., et al.: SDR receiver using commodity wifi via physical-layer signal reconstruction. In: Proceedings of the 26th Annual International Conference on Mobile Computing and Networking. Association for Computing Machinery, 32, pp. 1–14 (2020). https://doi.org/10.1145/3372224.3419189
Pfammatter, D., Guistiniano, D., Lenders, V.: A software-defined sensor architecture sensor architecture for large-scale wideband spectrum monitoring. In: Proceedings of the 14th International Conference on Information Processing in Sensor Networks, pp. 71–82 (2015). https://doi.org/10.1145/2737095.2737119
Nguyen, P., et al.: Drone presence detection by identifying physical signatures in the drone’s rf communication. In: Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services, pp. 211–224 (2017). https://doi.org/10.1145/3081333.3081354
Diraco, G., Leone, A., Sicilliano, P.: In-home hierarchical posture classification with a time-of-flight 3D sensor. Gait & Posture,Institute for Microelectronics and Microsystems, National Research Council, c/o Campus Ecotekne, Via Monteroni, Lecce, Italy 39(1), 182–187 (2014)
E. Research., E. Research.,. https://www.ettus.com/all-products/UN210-KIT/
Lara-Cueva, R., Morales, C., Fernandez, C.: Performance evaluation of WiFi technology in conformance with IEEE 802.11b/n/ac and WDS for indoor environments. 1(6) (2017)
Wang, M., Ma, X., Wang, Z., Guo, Y.: Analysis of Co-Channel Interference in Connected VehiclesWLAN with UAV. vol. 2022 (2022)
Chino, M., Miyashiro, H., Luis, A.J.: Implementation of SNR estimation algorithms, using LabVIEW Communications and GNU Radio Companion (2018)
Nafkha, A., Naoues, M., Cichon, K.: Experimental spectrum sensing measurements using USRP Software Radio platform and GNU-radio (2014)
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Torres Guin, W.D., Amaya Fariño, L.M., Arroyo Pizarro, J.F., García Santos, V.I., Del Rocío Villamar Garces, E. (2022). FHSS Classification System in the Spectrum Using SDR Generators for Signal Inhibitors. In: Guarda, T., Portela, F., Augusto, M.F. (eds) Advanced Research in Technologies, Information, Innovation and Sustainability. ARTIIS 2022. Communications in Computer and Information Science, vol 1675. Springer, Cham. https://doi.org/10.1007/978-3-031-20319-0_18
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