Machine Learning and Deep Learning powered satellite communications: : Enabling technologies, applications, open challenges, and future research directions
Authors: 
Arindam Bhattacharyya, Shvetha M. Nambiar, Ritwik Ojha, Amogh Gyaneshwar, Utkarsh Chadha, Kathiravan SrinivasanAuthors Info & Claims
Pages 539 - 588
Summary
The recent wave of creating an interconnected world through satellites has renewed interest in satellite communications. Private and government‐funded space agencies are making advancements in the creation of satellite constellations, and the introduction of 5G has brought a new focus to a fully connected world. Satellites are the proposed solutions for establishing high throughput and low latency links to remote, hard‐to‐reach areas. This has caused the injection of many satellites in Earth's orbit, which has caused many discrepancies. There is a need to establish highly adaptive and flexible satellite systems to overcome this. Machine Learning (ML) and Deep Learning (DL) have gained much popularity when it comes to communication systems. This review extensively provides insight into ML and DL's utilization in satellite communications. This review covers how satellite communication subsystems and other satellite system applications can be implemented through Artificial Intelligence (AI) and the ongoing open challenges and future directions.
Graphical Abstract
Satellites are the proposed solution to many of our communication and network‐related challenges. Recently, there has been a greater push for the use of machine learning and deep learning to assist these satellite systems making them more efficient and ubiquitous. This paper presents a comprehensive review of the usage of satellites for satellite communications, 5G, resource and spectrum allocation, hardware acceleration, and positioning and navigation. Further challenges and future directions are also explored.
References
[1]
Liu Y, Yuan X, Xiong Z, Kang J, Wang X, Niyato D. Federated learning for 6G communications: challenges, methods, and future directions. China Commun. 2020;17(9):105‐118.
[2]
Zhang C, Patras P, Haddadi H. Deep learning in mobile and wireless networking: a survey. IEEE Commun Surv Tutor. 2019;21(3):2224‐2287.
[3]
Ahmadi N. Review of terrestrial and satellite networks based on machine learning techniques. J Soft Comput Decision Support Syst. 2020;7(3):13‐22.
[4]
Tang F, Kawamoto Y, Kato N, Liu J. Future intelligent and secure vehicular network toward 6G: machine‐learning approaches. Proc IEEE. 2019;108(2):292‐307.
[5]
Nawaz SJ, Sharma SK, Wyne S, Patwary MN, Asaduzzaman M. Quantum machine learning for 6G communication networks: state‐of‐the‐art and vision for the future. IEEE Access. 2019;7:46317‐46350.
[6]
Rodrigues TK, Suto K, Nishiyama H, Liu J, Kato N. Machine learning meets computation and communication control in evolving edge and cloud: challenges and future perspective. IEEE Commun Surveys Tutorials. 2019;22(1):38‐67.
[7]
Hassanien AE, Darwish A, Abdelghafar S. Machine learning in telemetry data mining of space mission: basics, challenging and future directions. Artif Intell Rev. 2020;53(5):3201‐3230.
[8]
Fadlullah ZM, Tang F, Mao B, et al. State‐of‐the‐art deep learning: evolving machine intelligence toward tomorrow's intelligent network traffic control systems. IEEE Commun Surveys Tutorials. 2017;19(4):2432‐2455.
[9]
Bithas PS, Michailidis ET, Nomikos N, Vouyioukas D, Kanatas AG. A survey on machine‐learning techniques for UAV‐based communications. Sensors. 2019;19(23):5170.
[10]
Kato N, Mao B, Tang F, Kawamoto Y, Liu J. Ten challenges in advancing machine learning technologies toward 6G. IEEE Wirel Commun. 2020;27(3):96‐103.
[11]
Kato N, Fadlullah ZM, Mao B, et al. The deep learning vision for heterogeneous network traffic control: proposal, challenges, and future perspective. IEEE Wirel Commun. 2016;24(3):146‐153.
[12]
Centenaro M, Costa CE, Granelli F, Sacchi C, Vangelista L. A survey on technologies, standards and open challenges in satellite Iot. IEEE Commun Surveys Tutorials. 2021;23(3):1693‐1720.
[13]
Kodheli O, Lagunas E, Maturo N, et al. Satellite communications in the new space era: a survey and future challenges. IEEE Commun Surveys Tutorials. 2020;23(1):70‐109.
[14]
Khan MM, Hossain S, Majumder P, Akter S, Ashique RH. A review on machine learning and deep learning for various antenna design applications. Heliyon. 2022;8(4):e09317.
[15]
Wang P, Bayram B, Sertel E. A comprehensive review on deep learning based remote sensing image super‐resolution methods. Earth‐Sci Rev. 2022;232:104110.
[16]
Meena P, Pal MB, Jain PK, Pamula R. 6G communication networks: introduction, vision, challenges, and future directions. Wirel Pers Commun. 2022;125(2):1‐27.
[17]
Mortlock T, Kassas ZM. Assessing machine learning for LEO satellite orbit determination in simultaneous tracking and navigation. In: 2021 IEEE aerospace conference (50100). IEEE; 2021:1‐8.
[18]
Yeom JM, Park S, Chae T, Kim JY, Lee CS. Spatial assessment of solar radiation by machine learning and deep neural network models using data provided by the COMS MI geostationary satellite: a case study in South Korea. Sensors. 2019;19(9):2082.
[19]
Ghorbanzadeh O, Blaschke T, Gholamnia K, Meena SR, Tiede D, Aryal J. Evaluation of different machine learning methods and deep‐learning convolutional neural networks for landslide detection. Remote Sens (Basel). 2019;11(2):196.
[20]
Zhu J, Wang C. Satellite Networking Intrusion Detection System Design Based on Deep Learning Method. In: International conference in communications, signal processing, and systems. Springer; 2017:2295‐2304.
[21]
Pacheco F, Exposito E, Gineste M. A wearable Machine Learning solution for Internet traffic classification in Satellite Communications. In: International conference on service‐oriented computing. Springer; 2019:202‐215.
[22]
Yeh C, Perez A, Driscoll A, et al. Using publicly available satellite imagery and deep learning to understand economic well‐being in Africa. Nat Commun. 2020;11(1):2583.
[23]
Ibrahim SK, Ahmed A, Zeidan MAE, Ziedan IE. Machine learning methods for spacecraft telemetry mining. IEEE Trans Aerosp Electron Syst. 2018;55(4):1816‐1827.
[24]
Cigliano A, Zampognaro F. A Machine Learning approach for routing in satellite Mega‐Constellations. In: 2020 international symposium on advanced electrical and communication technologies (ISAECT). IEEE; 2020:1‐6.
[25]
Kourogiorgas C, Papafragkakis AZ, Panagopoulos AD, Ventouras S. Long‐term and short‐term atmospheric impairments forecasting for high throughput satellite communication systems. In: 12th European conference on antennas and propagation (EuCAP 2018). IET; 2018:1‐5.
[26]
Yang Y, Zhu L. An Efficient Way for Satellite Interference Signal Recognition Via Incremental Learning. In: 2019 international symposium on networks, computers and communications (ISNCC). IEEE; 2019:1‐5.
[27]
Oughton EJ, Mathur J. Predicting cell phone adoption metrics using machine learning and satellite imagery. Telematics Informatics. 2021;62:101622.
[28]
Kondmann, L., & Zhu, X. X. (2020). Measuring changes in poverty with deep learning and satellite imagery.
[29]
Chang Z, Lei L, Zhou Z, Mao S, Ristaniemi T. Learn to cache: machine learning for network edge caching in the big data era. IEEE Wirel Commun. 2018;25(3):28‐35.
[30]
Zhang Rolf E, Proctor J, Carleton T, et al. A generalizable and accessible approach to machine learning with global satellite imagery. Nat Commun. 2021;12(1):4392.
[31]
Han H, Lee S, Im J, et al. Detection of convective initiation using meteorological imager onboard communication, ocean, and meteorological satellite based on machine learning approaches. Remote Sens (Basel). 2015;7(7):9184‐9204.
[32]
Vázquez MÁ, Henarejos P, Pappalardo I, et al. Machine learning for satellite communications operations. IEEE Commun Mag. 2021;59(2):22‐27.
[33]
Henarejos P, Vázquez MÁ, Pérez‐Neira AI. Deep learning for experimental hybrid terrestrial and satellite interference management. In: 2019 IEEE 20th international workshop on signal processing advances in wireless communications (SPAWC). IEEE; 2019:1‐5.
[34]
Yuan Q, Shen H, Li T, et al. Deep learning in environmental remote sensing: achievements and challenges. Remote Sens Environ. 2020;241:111716.
[35]
Zhang Y, Wu Y, Liu A, Xia X, Pan T, Liu X. Deep learning‐based channel prediction for LEO satellite massive MIMO communication system. IEEE Wirel Commun Lett. 2021;10(8):1835‐1839.
[36]
Zhou Y, Fadlullah ZM, Mao B, Kato N. A deep‐learning‐based radio resource assignment technique for 5G ultra dense networks. IEEE Netw. 2018;32(6):28‐34.
[37]
Ozturk M, Gogate M, Onireti O, Adeel A, Hussain A, Imran MA. A novel deep learning driven, low‐cost mobility prediction approach for 5G cellular networks: the case of the control/data separation architecture (CDSA). Neurocomputing. 2019;358:479‐489.
[38]
Mishra KV, Gharanjik A, Shankar MB, Ottersten B. Deep learning framework for precipitation retrievals from communication satellites. In: European conference on radar in meteorology and hydrology (Vol 7). 2018.
[39]
Pinto F, Acciarini G, Metz S, et al. Towards automated satellite conjunction management with Bayesian deep learning. AI for earth sciences workshop at NeurIPS 2020; 2020.
[40]
Mao B, Tang F, Kawamoto Y, Kato N. Optimizing computation offloading in satellite‐UAV‐served 6G IoT: a deep learning approach. IEEE Netw. 2021;35(4):102‐108.
[41]
Peng, Y., Han, Q., Su, F., He, X., & Feng, X. (2021). Meteorological satellite operation prediction using a BiLSTM deep learning model. Security and communication networks, 2021. (not sure where to put, currently under 5.4 earth observation data collection).
[42]
Chen Q, Wang Y, Yadav A, et al. Efficient Detection of Rare Beacon Events in GEO Satellite Communication Systems using Deep Learning. In: 2021 IEEE MTT‐S international wireless symposium (IWS). IEEE; 2021:1‐3.
[43]
Xu T, Xu T, Darwazeh I. Deep learning for interference cancellation in non‐orthogonal signal based optical communication systems. In: 2018 Progress in electromagnetics research symposium (PIERS‐Toyama). IEEE; 2018:241‐248. (optical communications).
[44]
Lei L, Lagunas E, Yuan Y, Kibria MG, Chatzinotas S, Ottersten B. Deep learning for beam hopping in multibeam satellite systems. In: 2020 IEEE 91st vehicular technology conference (VTC2020‐spring). IEEE; 2020:1‐5.
[45]
Sun Y, Wang Y, Jiao J, Wu S, Zhang Q. Deep learning‐based long‐term power allocation scheme for NOMA downlink system in S‐IoT. IEEE Access. 2019;7:86288‐86296.
[46]
Wu S, Ya‐Ru H. Deep learning network for multiuser detection in satellite Mobile communication system. Computational Intelligence and Neuroscience: CIN; 2019:2019.
[47]
Cui Y, Jun Jing X, Sun S, Wang X, Cheng D, Huang H. Deep learning based primary user classification in cognitive radios. In: 2015 15th international symposium on communications and information technologies (ISCIT). IEEE; 2015:165‐168.
[48]
Saifi MY, Singla J. Deep learning‐based framework for semantic segmentation of satellite images. In: 2020 fourth international conference on computing methodologies and communication (ICCMC). IEEE; 2020:369‐374.
[49]
Tang F, Mao B, Fadlullah ZM, Kato N. On a novel deep‐learning‐based intelligent partially overlapping channel assignment in SDN‐IoT. IEEE Commun Mag. 2018;56(9):80‐86.
[50]
Oligeri G, Raponi S, Sciancalepore S, Di Pietro R. PAST‐AI: physical‐layer authentication of satellite transmitters via deep learning. IEEE Trans Inf Forensics Secur. 2023;18:274‐289.
[51]
Yao Y, Jiang Z, Zhang H, Zhou Y. On‐board ship detection in micro‐nano satellite based on deep learning and COTS component. Remote Sens (Basel). 2019;11(7):762.
[52]
Tang F, Mao B, Fadlullah ZM, et al. On removing routing protocol from future wireless networks: a real‐time deep learning approach for intelligent traffic control. IEEE Wirel Commun. 2017;25(1):154‐160.
[53]
Ates HF, Hashir SM, Baykas T, Gunturk BK. Path loss exponent and shadowing factor prediction from satellite images using deep learning. IEEE Access. 2019;7:101366‐101375.
[54]
Alam MZ, Ates HF, Baykas T, Gunturk BK. Analysis of deep learning based path loss prediction from satellite images. In: 2021 29th signal processing and communications applications conference (SIU). IEEE; 2021:1‐4.
[55]
Yu Y, Zhang L. Adaptive modulation scheme for satellite Communication Channel based on RLNN. J Phys: Conference Series. 2021;1856(1):012053. IOP Publishing.
[56]
Aung SWY, Khaing SS, Aung ST. Multi‐label land cover indices classification of satellite images using deep learning. In: International conference on big data analysis and deep learning applications. Springer; 2018:94‐103.
[57]
Kartal M, Duman O. Ship detection from optical satellite images with deep learning. In: 2019 9th international conference on recent advances in space technologies (RAST). IEEE; 2019:479‐484.
[58]
Varsni R. A. (2020). Feature extraction from satellite images using deep learning.
[59]
Kato N, Fadlullah ZM, Tang F, et al. Optimizing space‐air‐ground integrated networks by artificial intelligence. IEEE Wirel Commun. 2019;26(4):140‐147.
[60]
Gecgel S, Kurt GK. Intermittent Jamming against Telemetry and Telecommand of Satellite Systems and A Learning‐driven Detection Strategy. In: Proceedings of the 3rd ACM workshop on wireless security and machine learning; 2021, June:43‐48.
[61]
Mao B, Tang F, Fadlullah ZM, Kato N. An intelligent route computation approach based on real‐time deep learning strategy for software defined communication systems. IEEE Trans Emerg Topics Comput Secur. 2021;9(3):1554‐1565.
[62]
Kartchner DR, Palmer R, Jayaweera SK. Satellite Navigation Anti‐Spoofing Using Deep Learning on a Receiver Network. In: 2021 IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW). IEEE; 2021:1‐5.
[63]
Anantrasirichai N, Biggs J, Albino F, Bull D. A deep learning approach to detecting volcano deformation from satellite imagery using synthetic datasets. Remote Sens Environ. 2019;230:111179.
[64]
Irvin J, Sheng H, Ramachandran N, et al. Forestnet: classifying drivers of deforestation in Indonesia using deep learning on satellite imagery. 34th conference on neural information processing systems (NeurIPS 2020); 2020. https://s3.us-east-1.amazonaws.com/climate-change-ai/papers/neurips2020/22/paper.pdf
[65]
Wang H, Alwageed H, Yao YD. Modulation classification using convolutional neural network based deep learning model. In: 2017 26th wireless and optical communication conference (WOCC). IEEE; 2017:1‐5.
[66]
Thrane J, Zibar D, Christiansen HL. Model‐aided deep learning method for path loss prediction in mobile communication systems at 2.6 GHz. IEEE Access. 2020;8:7925‐7936.
[67]
Shetty A, Thorat A, Singru R, Shigawan M, Gaikwad V. Predict Socio‐Economic Status of an Area from Satellite Image Using Deep Learning. In: 2020 international conference on electronics and sustainable communication systems (ICESC). IEEE; 2020:177‐182.
[68]
Zheng S, Chen S, Qi P, Zhou H, Yang X. Spectrum sensing based on deep learning classification for cognitive radios. China Commun. 2020;17(2):138‐148.
[69]
Larsen A, Hanigan I, Reich BJ, et al. A deep learning approach to identify smoke plumes in satellite imagery in near‐real time for health risk communication. J Expo Sci Environ Epidemiol. 2021;31(1):170‐176.
[70]
Mai T, Yao H, Jing Y, Xu X, Wang X, Ji Z. Self‐learning congestion control of MPTCP in satellites communications. In: 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC). IEEE; 2019:775‐780.
[71]
Deng B, Jiang C, Yao H, Guo S, Zhao S. The next generation heterogeneous satellite communication networks: integration of resource management and deep reinforcement learning. IEEE Wirel Commun. 2019;27(2):105‐111.
[72]
Shen D, Sheaff C, Chen G. Game theoretic synthetic data generation for machine learning based satellite behavior detection. AMOS Tech; 2020.
[73]
Vaduva C, Gavat I, Datcu M. Deep learning in very high resolution remote sensing image information mining communication concept. In: 2012 proceedings of the 20th European signal processing conference (EUSIPCO). IEEE; 2012:2506‐2510.
[74]
Ferreira PVR, Paffenroth R, Wyglinski AM, et al. Multi‐objective reinforcement learning‐based deep neural networks for cognitive space communications. In: 2017 cognitive Communications for Aerospace Applications Workshop (CCAA). IEEE; 2017:1‐8.
[75]
Ferreira PVR, Paffenroth R, Wyglinski AM, et al. Multiobjective reinforcement learning for cognitive satellite communications using deep neural network ensembles. IEEE J Select Areas Commun. 2018;36(5):1030‐1041.
[76]
The need of 5G spectrum harmonization. ericsson.com; 2019, Ericsson Mobile Report 2019. https://www.ericsson.com/490523/assets/local/reports-papers/mobility-report/documents/2019/emr-wef-spectrum-harmonization.pdf
[77]
Fourati F, Alouini MS. Artificial intelligence for satellite communication: a review. Intelligent and Converged Networks. 2021;2(3):213‐243.
[78]
Vázquez MÁ, Henarejos P, Pérez‐Neira AI, et al. On the use of AI for satellite communications. IEEE Commun Mag. 2020;59(2):22‐27.
[79]
He Q, Xiang Z, Ren P. A CLSTM and transfer learning based CFDAMA strategy in satellite communication networks. PLoS ONE. 2021;16(3):e0248271.
[80]
Kim H, Jiang Y, Rana R, Kannan S, Oh S, Viswanath P. Communication algorithms via deep learning. In: International Conference on Learning Representations; 2018. https://openreview.net/forum?id%3DryazCMbR-
[81]
Dong K, Zhang H, Liu Y, Li Y, Peng Y. Research on Technologies of Vulnerability Mining and Penetration Testing for satellite communication network. IOP Conf Ser: Earth Environm Sci. 2021;693(1):012112. IOP Publishing.
[82]
Rath M, Mishra S. Security approaches in machine learning for satellite communication. In: Machine learning and data Mining in Aerospace Technology. Springer; 2020:189‐204.
[83]
Chang YH, Chen JL, He SL. Intelligent fault diagnosis of satellite communication antenna via a novel meta‐learning network combining with attention mechanism. J Phys: Conference Series. 2020;1510(1):012026. IOP Publishing.
[84]
Ortiz‐Gomez FG, Tarchi D, Rodriguez‐Osorio RM, Vanelli‐Coralli A, Salas‐Natera MA, Landeros‐Ayala S. Supervised Machine Learning for Power and Bandwidth Management in VHTS Systems. In: 2020 10th advanced satellite multimedia systems conference and the 16th signal processing for space communications workshop (ASMS/SPSC). IEEE; 2020:1‐7.
[85]
Huq R, Islam M, Siddique S. AI‐OBC: Conceptual Design of a Deep Neural Network based Next Generation Onboard Computing Architecture for Satellite Systems. In: 1st China microsatellite symposium; 2018.
[86]
Argyris A, Bueno J, Fischer I. Photonic machine learning implementation for signal recovery in optical communications. Sci Rep. 2018;8(1):8487.
[87]
Israel DJ, Heckler GW, Menrad RJ. Space mobile network: a near earth communications and navigation architecture. In: 2016 IEEE Aerospace Conference. IEEE; 2016:1‐7.
[88]
Taylor J. The deep space network: a functional description. Deep space Commun. 2016;15:15‐35.
[89]
Cornwell DM. NASA's optical communications program for 2017 and beyond. In: 2017 IEEE international conference on space optical systems and applications (ICSOS). IEEE; 2017:10‐14.
[90]
Mukai T, Inagawa S, Suzuki K. A study of free space laser communication experiment on the ISS Japanese experiment module for space explorations. In: 2015 IEEE international conference on space optical systems and applications (ICSOS). IEEE; 2015:1‐7.
[91]
Zhang S. Research on Photoelectric Effect for Artificial Satellite Communication. In: 2020 IEEE Intl Conf on parallel & distributed processing with applications, big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE; 2020:1465‐1469.
[92]
Mao B, Tang F, Fadlullah ZM, et al. A novel non‐supervised deep‐learning‐based network traffic control method for software defined wireless networks. IEEE Wirel Commun. 2018;25(4):74‐81.
[93]
Liu H, Zhang H, Yang K, Li J. Virtualized high throughput satellite gateway with a global bandwidth management method. Mob Inf Syst. 2022;2022:1‐11.
[94]
Vu TX, Maturo N, Vuppala S, Chatzinotas S, Grotz J, Alagha N. Efficient 5G edge caching over satellite. In: 36th international communications satellite systems conference (ICSSC 2018). IET; 2018:1‐5.
[95]
Berruet B, Baala O, Caminada A, Guillet V. DelFin: a deep learning based CSI fingerprinting indoor localization in IoT context. In: 2018 international conference on indoor positioning and indoor navigation (IPIN). IEEE; 2018:1‐8.
[96]
McMahan B. D. Ramage federated learning: collaborative machine learning without centralized training data. ai.googleblog.com; 2017. https://ai.googleblog.com/2017/04/federated-learning-collaborative.html
[97]
Jiang C, Shen J, Chen S, Chen Y, Liu D, Bo Y. UWB NLOS/LOS classification using deep learning method. IEEE Commun Lett. 2020;24(10):2226‐2230.
[98]
Hameed A, Ahmed HA. Survey on indoor positioning applications based on different technologies. In: 2018 12th international conference on mathematics, actuarial science, computer science and statistics (MACS). IEEE; 2018:1‐5.
[99]
Li CT, Cheng JC, Chen K. Top 10 technologies for indoor positioning on construction sites. Autom Construction. 2020;118:103309.
[100]
Samama N, Vervisch‐Picois A, Taillandier‐Loize T. A GNSS‐like indoor positioning system implementing an inverted radar approach simulation results with a 6/7‐antenna single transmitter. In: 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN). IEEE; 2016:1‐8.
[101]
Giuffrida G, Fanucci L, Meoni G, et al. The Φ‐Sat‐1 mission: the first on‐board deep neural network demonstrator for satellite earth observation. IEEE Trans Geosci Remote Sens. 2021;60:1‐14.
[102]
Rapuano E, Meoni G, Pacini T, et al. An fpga‐based hardware accelerator for cnns inference on board satellites: benchmarking with myriad 2‐based solution for the cloudscout case study. Remote Sens (Basel). 2021;13(8):1518.
[103]
Ortiz F, Monzon Baeza V, Garces‐Socarras LM, et al. Onboard processing in satellite communications using AI accelerators. Aerospace. 2023;10(2):101.
[104]
Leon V, Lentaris G, Soudris D, Vellas S, Bernou M. Towards Employing FPGA and ASIP Acceleration to Enable Onboard AI/ML in Space Applications. In: 2022 IFIP/IEEE 30th international conference on very large scale integration (VLSI‐SoC). IEEE; 2022:1‐4.
[105]
Ortiz‐Gomez FG, Lei L, Lagunas E, et al. Machine learning for radio resource management in multibeam GEO satellite systems. Electronics. 2022;11(7):992.
[106]
Leon V, Bezaitis C, Lentaris G, et al. FPGA & VPU Co‐Processing in Space Applications: Development and Testing with DSP/AI Benchmarks. In: 2021 28th IEEE international conference on electronics, circuits, and systems (ICECS). IEEE; 2021:1‐5.
[107]
Tingzon I, Orden A, Sy S, et al. Mapping poverty in the Philippines using machine learning, satellite imagery, and crowd‐sourced geospatial information. In: AI for social good ICML 2019 workshop; 2019.
[108]
Mohan A, Singh AK, Kumar B, Dwivedi R. Review on remote sensing methods for landslide detection using machine and deep learning. Trans Emerg Telecommun Technol. 2021;32(7):e3998.
[109]
Lee A, Jeong S, Joo J, Park CR, Kim J, Kim S. Potential role of urban forest in removing PM2. 5: a case study in Seoul by deep learning with satellite data. Urban Clim. 2021;36:100795.
[110]
Lee CS, Sohn E, Park JD, Jang JD. Estimation of soil moisture using deep learning based on satellite data: a case study of South Korea. GIScience Remote Sensing. 2019;56(1):43‐67.
[111]
Vignesh T, Thyagharajan KK, Ramya K. Change detection using deep learning and machine learning techniques for multispectral satellite images. Int J Innov Technol Explor Eng. 2019;9(1S):90‐93.
[112]
Helber P, Bischke B, Dengel A, Borth D. Eurosat: a novel dataset and deep learning benchmark for land use and land cover classification. IEEE J Select Topics Appl Earth Observ Remote Sensing. 2019;12(7):2217‐2226.
[113]
Vaishnnave MP, Devi KS, Srinivasan P. A study on deep learning models for satellite imagery. Int J Appl Eng Res. 2019;14(4):881‐887.
[114]
Kalinicheva E. Unsupervised satellite image time series analysis using deep learning techniques (Doctoral dissertation,. Sorbonne Université; 2020.
[115]
Hagos DH, Kakantousis T, Vlassov V, et al. ExtremeEarth meets satellite data from space. IEEE J Select Topics Appl Earth Observ Remote Sensing. 2021;14:9038‐9063.
[116]
Zhu XX, Tuia D, Mou L, et al. Deep learning in remote sensing: a comprehensive review and list of resources. IEEE Geosci Remote Sensing Mag. 2017;5(4):8‐36.
[117]
Chen J, Tang P, Rakstad T, Patrick M, Zhou X. Augmenting a deep‐learning algorithm with canal inspection knowledge for reliable water leak detection from multispectral satellite images. Adv Eng Informatics. 2020;46:101161.
[118]
Tang S, Pan Z, Hu G, Wu Y, Li Y. Deep reinforcement learning‐based resource allocation for satellite internet of things with diverse QoS guarantee. Sensors. 2022;22(8):2979.
[119]
Ortiz‐Gómez FG, Tarchi D, Martinez R, Vanelli‐Coralli A, Salas‐Natera MA, Landeros‐Ayala S. Supervised machine learning for power and bandwidth management in very high throughput satellite systems. Int J Satellite Commun Netw. 2022;40(6):392‐407.
[120]
Li X, Zhang H, Li W, Long K. Multi‐Agent DRL for User Association and Power Control in Terrestrial‐Satellite Network. In: 2021 IEEE global communications conference (GLOBECOM). IEEE; 2021:1‐5.
[121]
Jia M, Zhang X, Sun J, Gu X, Guo Q. Intelligent resource management for satellite and terrestrial spectrum shared networking toward B5G. IEEE Wirel Commun. 2020;27(1):54‐61.
[122]
Manning J, Langerman D, Ramesh B, et al. Machine‐learning space applications on smallsat platforms with tensorflow. 2018.
[123]
Ghiglione M, Serra V. Opportunities and challenges of AI on satellite processing units. In: Proceedings of the 19th ACM international conference on computing Frontiers; 2022:221‐224.
[124]
Software defined networking (SDN) definition. Open Networking Foundation. https://opennetworking.org/sdn-definition/
[125]
Kisseleff S, Lagunas E, Abdu TS, Chatzinotas S, Ottersten B. Radio resource management techniques for multibeam satellite systems. IEEE Commun Lett. 2020;25(8):2448‐2452.
[126]
Alsamhi SH, Ma O, Ansari M. Convergence of machine learning and robotics communication in collaborative assembly: mobility, connectivity and future perspectives. J Intell Robotic Syst. 2020;98(3):541‐566.
[127]
Gracla S, Beck E, Bockelmann C, Dekorsy A. Learning resource scheduling with high priority users using deep deterministic policy gradients. ICC 2022 ‐ IEEE international conference on communications. IEEE; 2022:4480‐4485.
[128]
Ortiz F, Lagunas E, Chatzinotas S. Unsupervised Learning for User Scheduling in Multibeam Precoded GEO Satellite Systems. In: 2022 joint European conference on networks and communications & 6G summit (EuCNC/6G summit). IEEE; 2022:190‐195.
[129]
Wei J, Cao S. Application of edge intelligent computing in satellite Internet of Things. In: 2019 IEEE international conference on smart internet of things (SmartIoT). IEEE; 2019:85‐91.
[130]
Li W, Su Z, Li R, Zhang K, Wang Y. Blockchain‐based data security for artificial intelligence applications in 6G networks. IEEE Netw. 2020;34(6):31‐37.
[131]
Hyland‐Wood, D., Robinson, P., Saltini, R., Johnson, S., & Hare, C. (2019). Methods for securing spacecraft tasking and control via an enterprise ethereum blockchain [international communications satellite systems conference].
[132]
Wei J, Han J, Cao S. Satellite IoT edge intelligent computing: a research on architecture. Electronics. 2019;8(11):1247.
[133]
Capizzi G, Lo Sciuto G, Woźniak M, Damaševicius R. A Clustering Based System for Automated Oil Spill Detection by Satellite Remote Sensing. In: Rutkowski L, Korytkowski M, Scherer R, Tadeusiewicz R, Zadeh L, Zurada J, eds. Artificial intelligence and soft computing. ICAISC 2016. Lecture Notes in Computer Science. Vol. 9693. Springer; 2016.
[134]
Toldinas J, Stuikys V, Damaševičius R, Ziberkas G. Application‐level energy consumption in communication models for handhelds. Elektronika Ir Elektrotechnika. 2009;94:73‐76.
[135]
Boateng ON, Nkrumah B, Boafo V, et al. A fraud prevention and secure cognitive SIM card registration model. Indian J Sci Technol. 2022;15(46):2562‐2569.
[136]
Ezra PJ, Misra S, Agrawal A, Oluranti J, Maskeliunas R, Damasevicius R. Secured Communication Using Virtual Private Network (VPN). In: Khanna K, Estrela VV, Rodrigues JJPC, eds. Cyber security and digital forensics. Lecture Notes on Data Engineering and Communications Technologies. Vol. 73. Springer; 2022.
Recommendations
Satellite selection with an end-to-end deep learning network
Benefiting from multi-constellation Global Navigation Satellite Systems (GNSS), more and more visible satellites can be used to improve user positioning performance. However, due to limited tracking receiver channels and power consumption, and other ...
Key technology trends - Satellite systems
This paper is based on material extracted from the WTEC Panel Report Global Satellite Communications Technology and Systems, December 1998. It presents an overview of key technology trends in communications satellites in the last few years. After the ...
Applicability of MIMO to satellite communications
This paper presents achievements of an on-going activity where the applicability of MIMO to satellite communications with Digital Video Broadcasting - Satellite services to Handhelds as the key application is studied. The potential satellite and hybrid ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
© 2023 John Wiley & Sons Ltd.
Publisher
John Wiley & Sons, Inc.
United States
Publication History
Published: 11 October 2023
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 01 Oct 2024
Other Metrics
Citations
Cited By
View allView Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in