[en] 5G enabled maritime unmanned aerial vehicle (UAV) communication is one of the important applications of 5G wireless network which requires minimum latency and higher reliability to support mission-critical applications. Therefore, lossless reliable communication with a high data rate is the key requirement in modern wireless communication systems. These all factors highly depend upon channel conditions. In this work, a channel model is proposed for air-to-surface link exploiting millimeter wave (mmWave) for 5G enabled maritime unmanned aerial vehicle (UAV) communication. Firstly, we will present the formulated channel estimation method which directly aims to adopt channel state information (CSI) of mmWave from the channel model inculcated by UAV operating within the Long Short Term Memory (LSTM)-Distributed Conditional generative adversarial network (DCGAN) i.e. (LSTM-DCGAN) for each beamforming direction. Secondly, to enhance the applications for the proposed trained channel model for the spatial domain, we have designed an LSTM-DCGAN based UAV network, where each one will learn mmWave CSI for all the distributions. Lastly, we have categorized the most favorable LSTM-DCGAN training method and emanated certain conditions for our UAV network to increase the channel model learning rate. Simulation results have shown that the proposed LSTM-DCGAN based network is vigorous to the error generated through local training. A detailed comparison has been done with the other available state-of-the-art CGAN network architectures i.e. stand-alone CGAN (without CSI sharing), Simple CGAN (with CSI sharing), multi-discriminator CGAN, federated learning CGAN and DCGAN. Simulation results have shown that the proposed LSTM-DCGAN structure demonstrates higher accuracy during the learning process and attained more data rate for downlink transmission as compared to the previous state of artworks.
Disciplines :
Electrical & electronics engineering
Author, co-author :
Rasheed, Iftikhar
Asif, Muhammad
Ihsan, Asim
KHAN, Wali Ullah ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SigCom
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
A. Ihsan, W. Chen, M. Asif, W. U. Khan, and J. Li, "Energy-efficient IRS-Aided NOMA beamforming for 6G wireless communications, " 2022, arXiv:2203.16099.
W. U. Khan, A. Ihsan, T. N. Nguyen, M. A. Javed, and Z. Ali, "NOMA-enabled backscatter communications for green transportation in automotive-industry 5.0, " IEEE Trans. Ind. Informat., early access, Mar. 22, 2022, doi: 10.1109/TII.2022.3161029.
T. Hasan et al., "Securing industrial Internet of Things against botnet attacks using hybrid deep learning approach, " IEEE Trans. Netw. Sci. Eng., early access, Mar. 22, 2022, doi: 10.1109/TNSE.2022.3168533.
B. T. Jijo et al., "A comprehensive survey of 5G mm-wave technology design challenges, " Asian J. Res. Comput. Sci., vol. 8, no. 1, pp. 1-20, Apr. 2021.
I. Rasheed and F. Hu, "Intelligent super-fast vehicle-To-everything 5G communications with predictive switching between mmWave and THz links, " Veh. Commun., vol. 27, Jan. 2021, Art. no. 100303.
Z. Zheng and A. K. Bashir, "Graph-enabled intelligent vehicular network data processing, " IEEE Trans. Intell. Transp. Syst., vol. 23, no. 5, pp. 4726-4735, May 2022.
Q. Pan, J. Wu, J. Nebhen, A. K. Bashir, Y. Su, and J. Li, "Artificial intelligence-based energy efficient communication system for intelligent reflecting surface-driven VANETs, " IEEE Trans. Intell. Transp. Syst., early access, Mar. 4, 2022, doi: 10.1109/TITS.2022.3152677.
S. Noh, J. Song, and Y. Sung, "Fast beam search and refinement for millimeter-wave massive MIMO based on two-level phased arrays, " IEEE Trans. Wireless Commun., vol. 19, no. 10, pp. 6737-6751, Oct. 2020.
R. Abbasi, A. K. Bashir, H. J. Alyamani, F. Amin, J. Doh, and J. Chen, "Lidar point cloud compression, processing and learning for autonomous driving, " IEEE Trans. Intell. Transp. Syst., early access, May 2, 2022, doi: 10.1109/TITS.2022.3167957.
H. Babbar, S. Rani, A. K. Bashir, and R. Nawaz, "LBSMT: Load balancing switch migration algorithm for cooperative communication intelligent transportation systems, " IEEE Trans. Green Commun. Netw., early access, Mar. 25, 2022, doi: 10.1109/TGCN.2022.3162237.
M. T. Dabiri, H. Safi, S. Parsaeefard, and W. Saad, "Analytical channel models for millimeter wave UAV networks under hovering fluctuations, " IEEE Trans. Wireless Commun., vol. 19, no. 4, pp. 2868-2883, Apr. 2020.
Q. Zhang, W. Saad, and M. Bennis, "Reflections in the sky: Millimeter wave communication with UAV-carried intelligent reflectors, " in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2019, pp. 1-6.
Y. Yang, Y. Li, W. Zhang, F. Qin, P. Zhu, and C.-X. Wang, "Generativeadversarial-network-based wireless channel modeling: Challenges and opportunities, " IEEE Commun. Mag., vol. 57, no. 3, pp. 22-27, Mar. 2019.
I. Rasheed, F. Hu, Y.-K. Hong, and B. Balasubramanian, "Intelligent vehicle network routing with adaptive 3D beam alignment for mmWave 5G-based V2X communications, " IEEE Trans. Intell. Transp. Syst., vol. 22, no. 5, pp. 2706-2718, May 2020.
W. U. Khan et al., "Opportunities for physical layer security in UAV communication enhanced with intelligent reflective surfaces, " 2022, arXiv:2203.16907.
M. R. Akdeniz et al., "Millimeter wave channel modeling and cellular capacity evaluation, " IEEE J. Sel. Areas Commun., vol. 32, no. 6, pp. 1164-1179, Jun. 2014.
P. Dong, H. Zhang, G. Y. Li, I. Gaspar, and N. NaderiAlizadeh, "Deep CNN-based channel estimation for mmWave massive MIMO systems, " IEEE J. Sel. Topics Signal Process., vol. 13, no. 5, pp. 989-1000, Sep. 2019.
M. Polese, L. Bertizzolo, L. Bonati, A. Gosain, and T. Melodia, "An experimental mmWave channel model for UAV-To-UAV communications, " in Proc. 4th ACM Workshop Millim.-Wave Netw. Sens. Syst., Sep. 2020, pp. 1-6.
L. Cheng, Q. Zhu, C.-X. Wang, W. Zhong, B. Hua, and S. Jiang, "Modeling and simulation for UAV air-To-ground mmWave channels, " in Proc. 14th Eur. Conf. Antennas Propag. (EuCAP), Mar. 2020, pp. 1-5.
M. Asif, W. Zhou, M. Ajmal, Z. U. A. Akhtar, and N. A. Khan, "A construction of high performance quasicyclic LDPC codes: A combinatoric design approach, " Wireless Commun. Mobile Comput., vol. 2019, pp. 1-10, Feb. 2019.
Q. Zhang, A. Ferdowsi, W. Saad, and M. Bennis, "Distributed conditional generative adversarial networks (GANs) for data-driven millimeter wave communications in UAV networks, " 2021, arXiv:2102.01751.
H. Ye, L. Liang, G. Y. Li, and B.-H. F. Juang, "Deep learning-based end-To-end wireless communication systems with conditional GANs as unknown channels, " IEEE Trans. Wireless Commun., vol. 19, no. 5, pp. 3133-3143, May 2020.
W. Xia et al., "Generative neural network channel modeling for millimeter-wave UAV communication, " 2020, arXiv:2012.09133.
A. M. Elbir and S. Coleri, "Federated learning for channel estimation in conventional and RIS-Assisted massive MIMO, " IEEE Trans. Wireless Commun., vol. 21, no. 6, pp. 4255-4268, Jun. 2021.
A. Alkhateeb, "DeepMIMO: A generic deep learning dataset for millimeter wave and massive MIMO applications, " 2019, arXiv:1902.06435.
Y. Han and J. Lee, "Two-stage compressed sensing for millimeter wave channel estimation, " in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Jul. 2016, pp. 860-864.
W. Khawaja, I. Guvenc, D. W. Matolak, U. Fiebig, and N. Schneckenburger, "A survey of air-To-ground propagation channel modeling for unmanned aerial vehicles, " IEEE Commun. Surveys Tuts., vol. 21, no. 3, pp. 2361-2391, 3rd Quart., 2019.
W. Khawaja, O. Ozdemir, and I. Guvenc, "UAV air-To-ground channel characterization for mmWave systems, " in Proc. IEEE 86th Veh. Technol. Conf. (VTC-Fall), Sep. 2017, pp. 1-5.
A. M. Elbir and S. Coleri, "Federated learning for channel estimation in conventional and RIS-Assisted massive MIMO, " 2020, arXiv:2008.10846.
J. Park et al., "Communication-efficient and distributed learning over wireless networks: Principles and applications, " 2020, arXiv:2008.02608.
A. L. Swindlehurst, E. Ayanoglu, P. Heydari, and F. Capolino, "Millimeter-wave massive MIMO: The next wireless revolution?" IEEE Commun. Mag., vol. 52, no. 9, pp. 56-62, Sep. 2014.
W. Khawaja, O. Ozdemir, and I. Guvenc, "Temporal and spatial characteristics of mm wave propagation channels for UAVs, " in Proc. 11th Global Symp. Millim. Waves (GSMM), May 2018, pp. 1-6.
A. Khalili, S. Shahsavari, M. A. A. Khojastepour, and E. Erkip, "On optimal multi-user beam alignment in millimeter wave wireless systems, " in Proc. IEEE Int. Symp. Inf. Theory (ISIT), Jun. 2020, pp. 2953-2958.
M. A. A. Khojastepour, S. Shahsavari, A. Khalili, and E. Erkip, "Multi-user beam alignment for millimeter wave systems in multi-path environments, " in Proc. 54th Asilomar Conf. Signals, Syst., Comput., Nov. 2020, pp. 549-553.
B. Sadhu et al., "A 28 GHz 32-element phased-Array transceiver IC with concurrent dual polarized beams and 1.4 degree beam-steering resolution for 5G communication, " in IEEE Int. Solid-State Circuits Conf. (ISSCC) Dig. Tech. Papers, Feb. 2017, pp. 128-129.
A. L. Ha, T. Van Chien, T. H. Nguyen, W. Choi, and V. D. Nguyen, "Deep learning-Aided 5G channel estimation, " in Proc. 15th Int. Conf. Ubiquitous Inf. Manage. Commun. (IMCOM), Jan. 2021, pp. 1-7.
C. Umit Bas et al., "A real-Time millimeter-wave phased array MIMO channel sounder, " 2017, arXiv:1703.05271.
M. Asif et al., "Reduced-complexity LDPC decoding for next-generation IoT networks, " Wireless Commun. Mobile Comput., vol. 2021, pp. 1-10, Sep. 2021.
M. Asif, W. Zhou, Q. Yu, S. Adnan, M. S. Ali, and M. S. Iqbal, "Jointly designed quasi-cyclic LDPC-coded cooperation with diversity combining at receiver, " Int. J. Distrib. Sensor Netw., vol. 16, no. 7, 2020, Art. no. 1550147720938974.
I. Rasheed, "Machine learning enhanced 5G vehicle-To-everything (V2X) communication networks with millimeter-waves and terahertz links, " Ph.D. dissertation, Dept. Elect. Comput. Eng. (ECE), Univ. Alabama, Tuscaloosa, AL, USA, 2020.
Z. Feng, L. Ji, Q. Zhang, and W. Li, "Spectrum management for mmWave enabled UAV swarm networks: Challenges and opportunities, " IEEE Commun. Mag., vol. 57, no. 1, pp. 146-153, Jan. 2018.
Z. Liu and X. Yin, "LSTM-CGAN: Towards generating low-rate DDoS adversarial samples for blockchain-based wireless network detection models, " IEEE Access, vol. 9, pp. 22616-22625, 2021.
A. Ferdowsi and W. Saad, "Generative adversarial networks for distributed intrusion detection in the Internet of Things, " in Proc. IEEE Global Commun. Conf. (GLOBECOM), Dec. 2019, pp. 1-6.
I. Goodfellow et al., "Generative adversarial nets, " in Proc. Adv. Neural Inf. Process. Syst. (NIPS), vol. 27, 2014, pp. 1-9.
C. U. Bas et al., "A real-Time millimeter-wave phased array MIMO channel sounder, " in Proc. IEEE 86th Veh. Technol. Conf. (VTC-Fall), Sep. 2017, pp. 1-6.
M. Mirza and S. Osindero, "Conditional generative adversarial nets, " 2014, arXiv:1411.1784.
Similar publications
Sorry the service is unavailable at the moment. Please try again later.