Abstract
To enhance the anti-interference performance of airborne communication data link system, a transmission control method based on improved fish swarm algorithm is constructed. Firstly, the airborne data link channel model is constructed. Secondly, equalization algorithm of airborne data link is constructed, and the improved artificial fish algorithm is designed, and the algorithm procedure of transmission control of airborne communication data link system is established. Finally, simulation analysis is carried out, results show that the proposed method has higher transmission success rate, lower transmission time, and higher transmission rate, the proposed model has better performance in transmission control of airborne communication data link system.
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REFERENCES
Yao, G. and Xu, Z., Trajectory tracking analysis of airborne data link antenna, Comput. Commun., 2021, vol. 176, pp. 182–189. https://doi.org/10.1016/j.comcom.2021.06.001
Almalawi, A., Khan, A., Alsolami, F., Alkhathlan, A., Fahad, A., Irshad, K., Alfakeeh, A., and Qaiyum, S., Arithmetic optimization algorithm with deep learning enabled airborne particle-bound metals size prediction model, Chemosphere, 2022, vol. 303, p. 134960. https://doi.org/10.1016/j.chemosphere.2022.134960
Gulec, F. and Atakan, B., Fluid dynamics-based distance estimation algorithm for macroscale molecular communication, Nano Commun. Networks, 2021, vol. 28, p. 100351. https://doi.org/10.1016/j.nancom.2021.100351
Dastranj, P., Solouk, V., and Kalbkhani, H., Energy-efficient deep-predictive airborne base station selection and power allocation for UAV-assisted wireless networks, Comput. Commun., 2022, vol. 191, pp. 274–284. https://doi.org/10.1016/j.comcom.2022.05.001
Zhang, Yi., Li, S., and Xu, B., Convergence analysis of beetle antennae search algorithm and its applications, Soft Comput., 2021, vol. 25, no. 16, pp. 10595–10608. https://doi.org/10.1007/s00500-021-05991-z
Costabile, P., Costanzo, C., De Lorenzo, G., De Santis, R., Penna, N., and Macchione, F., Terrestrial and airborne laser scanning and 2-D modelling for 3-D flood hazard maps in urban areas: new opportunities and perspectives, Environ. Modell. Software, 2021, vol. 135, p. 104889. https://doi.org/10.1016/j.envsoft.2020.104889
Joshi, A., Wala, A., Ludhiyani, M., Chakraborty, D., Chung, H., and Manjunath, D., Outdoor cooperative flight using decentralized consensus algorithm and a guaranteed real-time communication protocol, Control Eng. Pract., 2019, vol. 88, pp. 128–140. https://doi.org/10.1016/j.conengprac.2019.05.002
Kernchen, S., Löder, M., Fischer, F., Fischer, D., Moses, S., Georgi, C., Nölscher, A., Held, A., and Laforsch, C., Airborne microplastic concentrations and deposition across the Weser River catchment, Sci. Total Environ., 2022, vol. 818, p. 151812. https://doi.org/10.1016/j.scitotenv.2021.151812
Fan, Yi., Tao, M., Su, J., and Wang, L., Analysis of goodness-of-fit method based on local property of statistical model for airborne sea clutter data, Digital Signal Process., 2020, vol. 99, p. 102653. https://doi.org/10.1016/j.dsp.2019.102653
Vermillion, C., Cobb, M., Fagiano, L., Leuthold, R., Diehl, M., Smith, R., Wood, T., Rapp, S., Schmehl, R., Olinger, D., and Demetriou, M., Electricity in the air: Insights from two decades of advanced control research and experimental flight testing of airborne wind energy systems, Annu. Rev. Control, 2021, vol. 52, pp. 330–357. https://doi.org/10.1016/j.arcontrol.2021.03.002
Zhao, B., Ren, Yi., Gao, D., Xu, L., and Zhang, Yu., Energy utilization efficiency evaluation model of refining unit Based on Contourlet neural network optimized by improved grey optimization algorithm, Energy, 2019, vol. 185, pp. 1032–1044. https://doi.org/10.1016/j.energy.2019.07.111
Ebrahimi, M., Joseph, S., Cathal, C., Donnell, O., and Toal, D., Experimental rig investigation of a direct interconnection technique for airborne wind energy systems, Int. J. Electr. Power Energy Syst., 2020, vol. 123, p. 106300.
Zhao, B., Chen, H., Gao, D., and Xu, L., Risk assessment of refinery unit maintenance based on fuzzy second generation curvelet neural network, Alexandria Eng. J., 2020, vol. 59, no. 3, pp. 1823–1831. https://doi.org/10.1016/j.aej.2020.04.052
Merkert, R. and Bushell, J., Managing the drone revolution: A systematic literature review into the current use of airborne drones and future strategic directions for their effective control, J. Air Transp. Manage., 2020, vol. 89, p. 101929. https://doi.org/10.1016/j.jairtraman.2020.101929
Liu, Q., Ren, H., Tang, R., and Yao, J., Optimizing co-existing multicast routing trees in IP network via discrete artificial fish school algorithm, Knowl.-Based Syst., 2020, vol. 191, p. 105276. https://doi.org/10.1016/j.knosys.2019.105276
Zhao, B., Ren, Yi., Gao, D., and Xu, L., Performance ratio prediction of photovoltaic pumping system based on grey clustering and second curvelet neural network, Energy, 2019, vol. 171, pp. 360–371. https://doi.org/10.1016/j.energy.2019.01.028
Venu, D., Mayuri, A.V.R., Neelakandan, S., Murthy, G.L.N., Arulkumar, N., and Shelke, N., An efficient low complexity compression based optimal homomorphic encryption for secure fiber optic communication, Optik, 2022, vol. 252, p. 168545. https://doi.org/10.1016/j.ijleo.2021.168545
Gu, K., Mao, Z., Duan, X., Wu, G., and Yan, L., Identifying the module structure of swarms using a new framework of network-based time series clustering, Eng. Appl. Artif. Intell., 2021, vol. 101, p. 104214. https://doi.org/10.1016/j.engappai.2021.104214
Hassan Nasir, M., Khan, S., Mubashir Khan, M., and Fatima, M., Swarm intelligence inspired intrusion detection systems-A systematic literature review, Comput. Networks, 2022, vol. 205, p. 108708.
Nedjah, N., Macedo Mourelle, L., Jorge, P., and De Oliveira, A., Simultaneous localization and mapping using swarm intelligence based methods, Expert Syst. with Appl., 2020, vol. 159, p. 113547. https://doi.org/10.1016/j.eswa.2020.113547
Faradonbe, S.M. and Safi-Esfahani, F., A classifier task based on neural Turing machine and particle swarm algorithm, Neurocomputing, 2020, vol. 396, pp. 133–152. https://doi.org/10.1016/j.neucom.2018.07.097
Parrott, C., Dodd, T., Boxall, J., and Horoshenkov, K., Simulation of the behavior of biologically-inspired swarm robots for the autonomous inspection of buried pipes, Tunnelling Underground Space Technol., 2020, vol. 101, p. 103356. https://doi.org/10.1016/j.tust.2020.103356
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Xiuzhen Nie, Yingxue Jiao Research on Transmission Control of Airborne Communication Data Link System Based on Artificial Fish Swarm Algorithm. Aut. Control Comp. Sci. 57, 327–336 (2023). https://doi.org/10.3103/S0146411623040077
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DOI: https://doi.org/10.3103/S0146411623040077