A Fast Machine Learning for 5G Beam Selection for Unmanned Aerial Vehicle Applications
: Wireless Network
Unmanned Ariel Vehicle,
Reinforcement Learning Algorithms,
Unmanned Aerial vehicles (UAVs) emerged into a promising research trend applied in several disciplines based on the benefits, including efficient communication, on-time search, and rescue operations, appreciate customer deliveries among more. The current technologies are using fixed base stations (BS) to operate onsite and off-site in the fixed position with its associated problems like poor connectivity. These open gates for the UAVs technology to be used as a mobile alternative to increase accessibility in beam selection with a fifth-generation (5G) connectivity that focuses on increased availability and connectivity. This paper presents a first fast semi-online 3-Dimensional machine learning algorithm suitable for proper beam selection as is emitted from UAVs. Secondly, it presents a detailed step by step approach that is involved in the multi-armed bandit approach in solving UAV solving selection exploration to exploitation dilemmas. The obtained results depicted that a multi-armed bandit problem approach can be applied in optimizing the performance of any mobile networked devices issue based on bandit samples like Thompson sampling, Bayesian algorithm, and ε-Greedy Algorithm. The results further illustrated that the 3-Dimensional algorithm optimizes utilization of technological resources compared to the existing single and the 2-Dimensional algorithms thus close optimal performance on the average period through machine learning of realistic UAV communication situations.
 R. Torkamani and R. A. Sadeghzadeh, “Wavelet-based Bayesian Algorithm for Distributed Compressed Sensing.”, Journal of Information Systems and Telecommunication, Vol. 7, No. 2, April-June 2019.
 M. Jaderyan and H. Khotanlou, “SGF (Semantic Graphs Fusion): A Knowledge-based Representation of Textual Resources for Text Mining Applications.” , Jour. of Information Systems and Tele.mmunication, Vol. 7, No. 1, January-March 2019.
 N. Sharma, A. S. Arora, A. P. Singh, and J. Singh, “The Role of Infrared Thermal Imaging in Road Patrolling Using Unmanned Aerial Vehicles,” in Unmanned Aerial Vehicle: Applications in Agriculture and Environment, Springer, 2020, pp. 143–157.
 C. Qu, W. Gai, M. Zhong, and J. Zhang, "A novel reinforcement learning-based grey wolf optimizer algorithm for unmanned aerial vehicles (UAVs) path planning," Appl. Soft Comput., p. 106099, 2020.
 F. Al-Turjman, H. Zahmatkesh, and R. Daboul, “Optimized Unmanned Aerial Vehicles Deployment for Static and Mobile Targets’ Monitoring,” Comput. Commun., vol. 149, pp. 27–35, 2020.
 Y.-F. Liu, X. Nie, J.-S. Fan, and X.-G. Liu, “Image-based crack assessment of bridge piers using unmanned aerial vehicles and three-dimensional scene reconstruction,” Comput.-Aided Civ. Infrastruct. Eng., 2020
 S. Lee et al., “Intelligent traffic control for autonomous vehicle systems based on machine learning,” Expert Syst. Appl., vol. 144, p. 113074, 2020.
 E. dos Santos Moreira, R. M. P. Vanni, D. L. Função, and C. A. C. Marcondes, “A Context-Aware Commu. Link for Unmanned Aerial Vehicles,” in 2010 Sixth Advanced International Conference on Telecommunications, 2010, pp. 497–502.
 D. He, S. Chan, and M. Guizani, “Communication security of unmanned aerial vehicles,” IEEE Wirel. Commun., vol. 24, no. 4, pp. 134–139, 2016.
A. Abdessameud and A. Tayebi, “Formation control of VTOL unmanned aerial vehicles with communication delays,” Automatica, vol. 4, no. 11, pp. 2383–2394, 2011.
W. Gaofeng, G. Xiaoguang, Z. Kun, and F. Xiaowei, “Multi-objective Placement of Unmanned Aerial Vehicles as Communication Relays Based on Clustering Method,” in 2019 Chinese Control And Decision Conference (CCDC), 2019, pp. 1462–1467.
H. Nawaz, H. M. Ali, and M. H. Mahar, “Swarm of Unmanned Aerial Vehicles Communication Using 802.11 g and 802.11 n,” IJCSNS, vol. 19, no. 4, p. 289, 2019.
G. S. Voronkov, E. A. Smirnova, and I. V. Kuznetsov, “The method for synthesis of the coordinated group DPCM codec for unmanned aerial vehicles communication systems,” in 2019 International Conference on Electrotechnical Complexes and Systems (ICOECS), 2019, pp. 1–4.
H. Harmon, “The Use of Unmanned Aerial Vehicles in Public Safety Communication and Optimization of Coverage (Case Study-Cook Islands),” PhD Thesis, Auckland University of Technology, 2017.
S. Fekri-Ershad and F. Tajeripour, “Multi-resolution and noise-resistant surface defect detection approach using new version of local binary patterns,” Appl. Artif. Intell., vol. 31, no. 5–6, pp. 395–410, 2017.
 S. Fekri-Ershad and F. Tajeripour, "Impulse-Noise resistant color-texture classification approach using hybrid color local binary patterns and pullback–leibler divergence," The Computer Journal, vol. 60, no. 11, pp. 1633–1648, 2017.
J. A. Adams et al., “Cognitive task analysis for developing unmanned aerial vehicle wilderness search support,” Journal of cognitive engineering and decision making, vol. 3, no. 1, pp. 1–26, 2009.
H. Ayaz et al., “Monitoring expertise development during simulated UAV piloting tasks using optical brain imaging,” in 2012 IEEE Aerospace Conference, 2012, pp. 1–11.
S. O. Murphy, C. Sreenan, and K. N. Brown, “Autonomous Unmanned Aerial Vehicle for Search and Rescue Using Software Defined Radio,” in 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), 2019, pp. 1–6.
H. El-Sayed, M. Chaqfa, S. Zeadally, and D. Putkal, “A Traffic-Aware Approach for Enabling Unmanned Aerial Vehicles (UAVs) in Smart City Scenarios*,” IEEE Access, pp. 1–1, 2019.
M. Meng et al., “BeamRaster: A Practical Fast Massive MU-MIMO System With Pre-Computed Precoders,” IEEE Transactions on Mobile Computing, vol. 18, no. 5, pp. 1014–1027, May 2019.
W. Shafik and S. A. Mostafavi, “Knowledge Engineering on Internet of Things through Reinforcement Learning,” Int. J. Comput. Appl., vol. 975, p. 8887.
S. M. Matinkhah, W. Shafik, and M. Ghasemzadeh, “Emerging Artificial Intelligence Application: Reinforcement Learning Issues on Current Internet of Things,” in 2019 16th international Conference in information knowledge and Technology (ikt2019), p. 2019.
W. Shafik, S. M. Matinkhah, and M. Ghasemazade, “Fog-Mobile Edge Performance Evaluation and Analysis on Internet of Things,” J. Adv. Res. Mob. Comput., vol. 1, no. 3.
Bonald, T., Borst, S., Hegde, N. and Proutiére, A., 2004. Wireless data performance in multi-cell scenarios. ACM SIGMETRICS Performance Evaluation Review, 32(1), pp.378-380.
Jose, J., Ashikhmin, A., Marzetta, T.L. and Vishwanath, S., 2009, June. Pilot contamination problem in multi-cell TDD systems. In 2009 IEEE International Symposium on Information Theory (pp. 2184-2188). IEEE.
Kazi, B.U., and Wainer, G., 2020. Coordinated multi-cell cooperation with a user-centric dynamic coordination station. Computer Networks, 166, p.106948.
 A. Asadi, S. Müller, G. H. Sim, A. Klein, and M. Hollick, “FML: Fast machine learning for 5G mmWave vehicular communications,” in IEEE INFOCOM 2018-IEEE Conference on Computer Communications, 2018, pp. 1961–1969.
M. M. Ferdaus, S. G. Anavatti, M. Pratama, and M. A. Garratt, “Towards the use of fuzzy logic systems in rotary wing unmanned aerial vehicle: a review,” Artif. Intell. Rev., vol. 53, no. 1, pp. 257–290, 2020.
W. Shafik, M. Matinkhah, M. Asadi, Z. Ahmadi, and Z. Hadiyan, “A Study on Internet of Things Performance Evaluation,” J. Commun. Technol. Electron. Comput. Sci., vol. 28, pp. 1–19, 2020.