Skip to main content
SpringerLink
Log in

Generative AI in mobile networks: a survey

  • Published:
Annals of Telecommunications Aims and scope Submit manuscript

Abstract

This paper provides a comprehensive review of recent challenges and results in the field of generative AI with application to mobile telecommunications networks. The objective is to classify the literature using an approach that encompasses the type of generative AI technology employed, the functional purpose, and the specific component of the mobile network that each solution targets. Moreover, performance requirements for generative AI applications are considered. Thereafter, state-of-the-art generative AI algorithms and an examination of their use cases across various industry verticals are presented. The discussion extends to the current level of AI integration in telecom standardization bodies, such as the 3rd Generation Partnership Project (3GPP). Finally, the open research challenges that the generative AI technology aims to address are thoroughly investigated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Note that the terms RBS and cell are used interchangeably in the context of this publication.

  2. In context of this publication, the terms “execution” and “inferencing” are used interchangeably.

  3. Fidelity refers to the degree to which the generated content matches the real content. Diversity measures whether the generated content covers the full variability of the real content.

References

  1. Morocho Cayamcela ME, Lim W (2018) Artificial intelligence in 5G technology: a survey. In: 2018 International Conference on Information and Communication Technology Convergence (ICTC), pp 860–865. https://doi.org/10.1109/ICTC.2018.8539642

  2. Tataria H, Shafi M, Molisch AF, Dohler M, Sjöland H, Tufvesson F (2021) 6G wireless systems: vision, requirements, challenges, insights, and opportunities. Proceedings of the IEEE 109(7):1166–1199. https://doi.org/10.1109/JPROC.2021.3061701

    Article  Google Scholar 

  3. Hinton GE, Sejnowski T (1986) Learning and relearning in Boltzmann machines. Parallel distributed processing: explorations in the microstructure of cognition. MIT Press, Cambridge, MA, pp 282–317

    Google Scholar 

  4. Salakhutdinov R, Mnih A, Hinton G (2007) Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning. ICML ’07, pp 791–798. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/1273496.1273596

  5. Hinton GE (2009) Deep belief networks. Scholarpedia 4(5):5947

  6. Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence N, Weinberger KQ (eds.) Advances in Neural Information Processing Systems, vol 27. https://proceedings.neurips.cc/paper files/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf

  7. Gui J, Sun Z, Wen Y, Tao D, Ye J (2023) A review on generative adversarial networks: algorithms, theory, and applications. IEEE Transactions on Knowledge and Data Engineering 35(4):3313–3332. https://doi.org/10.1109/TKDE.2021.3130191

    Article  Google Scholar 

  8. Kingma DP, Welling M (2019) An introduction to variational autoencoders. Found. Trends Mach. Learn. 12(4):307–392. https://doi.org/10.1561/2200000056

    Article  Google Scholar 

  9. Vaithilingam P, Zhang T, Glassman EL (2022) Expectation vs. experience: evaluating the usability of code generation tools powered by large language models. In: Chi Conference on Human Factors in Computing Systems Extended Abstracts, pp 1–7

  10. Iqbal T, Qureshi S (2022) The survey: text generation models in deep learning. Journal of King Saud University - Computer and Information Sciences 34(6,Part A), 2515–2528. https://doi.org/10.1016/j.jksuci.2020.04.001

  11. Hochreiter S (1998) The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int. J. Uncertain. Fuzziness Knowl.-Based Syst. 6(2): 107–116. https://doi.org/10.1142/S0218488598000094

  12. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput. 9(8):1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735

    Article  Google Scholar 

  13. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. NIPS’17, pp 6000–6010. Curran Associates Inc., Red Hook, NY, USA

  14. Radford A, Narasimhan K, Salimans T, Sutskever I, et al (2018) Improving language understanding by generative pre-training. Preprint, published by OpenAI

  15. Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Burstein J, Doran C, Solorio T (Eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), pp 4171–4186. https://doi.org/10.18653/v1/n19-1423

  16. Ouyang L, Wu J, Jiang X, Almeida D, Wainwright C, Mishkin P, Zhang C, Agarwal S, Slama K, Ray A et al (2022) Training language models to follow instructions with human feedback. Advances in Neural Information Processing Systems 35:27730–27744

    Google Scholar 

  17. de Rosa GH, Papa JP (2021) A survey on text generation using generative adversarial networks. Pattern Recognition 119:108098. https://doi.org/10.1016/j.patcog.2021.108098

    Article  Google Scholar 

  18. Junyi L, Tang T, Zhao W, Wen J-R (2021) Pretrained language model for text generation: a survey, pp 4492–4499. https://doi.org/10.24963/ijcai.2021/612

  19. Chui M, Roberts R, Yee L (2022) Generative AI is here: how tools like ChatGPT could change your business. Quantum Black AI by McKinsey

  20. Vaithilingam P, Zhang T, Glassman EL (2022) Expectation vs. experience: evaluating the usability of code generation tools powered by large language models. In: Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems. CHI EA’22. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3491101.3519665

  21. Lee H, Ullah U, Lee J-S, Jeong B, Choi H-C (2021) A brief survey of text driven image generation and manipulation. In: 2021 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), pp 1–4. https://doi.org/10.1109/ICCE-Asia53811.2021.9641929

  22. Li S, Tao Z, Li K, Fu Y (2019) Visual to text: survey of image and video captioning. IEEE Transactions on Emerging Topics in Computational Intelligence 3(4):297–312. https://doi.org/10.1109/TETCI.2019.2892755

    Article  Google Scholar 

  23. Zhang C, Zhang C, Zheng S, Zhang M, Qamar M, Bae S-H, Kweon IS (2023) A survey on audio diffusion models: text to speech synthesis and enhancement in generative AI. arXiv. arXiv:2303.13336

  24. Hernandez-Olivan C, Beltrán JR (2023) In: Biswas A, Wennekes E, Wieczorkowska A, Laskar RH (Eds.) Music composition with deep learning: a review, pp 25–50. Springer, Cham. https://doi.org/10.1007/978-3-031-18444-4_2

  25. Aldausari N, Sowmya A, Marcus N, Mohammadi G (2022) Video generative adversarial networks: a review. ACM Comput. Surv. 55(2). https://doi.org/10.1145/3487891

  26. Zhang H, Shao S, Tao M, Bi X, Letaief KB (2023) Deep learning-enabled semantic communication systems with task-unaware transmitter and dynamic data. IEEE Journal on Selected Areas in Communications 41(1):170–185. https://doi.org/10.1109/JSAC.2022.3221991

    Article  Google Scholar 

  27. Guo J, Wen C-K, Jin S, Li X (2022) AI for CSI feedback enhancement in 5G advanced. IEEE Wireless Communications 1–8. https://doi.org/10.1109/MWC.010.2200304

  28. 3GPP: Study on Artificial Intelligence (AI)/Machine Learning (ML) for NR air interface. Technical Report (TR) 38.843, 3rd Generation Partnership Project (3GPP) (2022). Version 0.1.0

  29. 3GPP: study on enhancement for data collection for NR and ENDC. Technical Report (TR) 38.817, 3rd Generation Partnership Project (3GPP) (2022). Version 17.0.0

  30. O-RAN Working Group 2: AI/ML workflow description and requirements. Technical report (tr), O-RAN Alliance (2021). Version 1.03

  31. O-RAN working group 1: use cases detailed specification. Technical specification, O-RAN Alliance (2023). Version 10.00

  32. 3GPP: study of enablers for network automation for the 5G system (5GS); Phase 3. Technical Report (TR) 23.700-81, 3rd Generation Partnership Project (3GPP) (December 2022). Version 18.0.0

  33. TMForum: autonomous networks technical architecture. Technical Report (TR) IG1230 (December 2022). Version 1.1.1

  34. Niemöller J, Szabö R, Zahemzky A, Roeland D (2022) Creating autonomous networks with intent-based closed loops. Whitepaper, Ericsson

    Book  Google Scholar 

  35. 3GPP: study on AI/ML model transfer-phase 2. Technical Report (TR) 38.843, 3rd Generation Partnership Project (3GPP) (March 2023). Version 1.0.0

  36. Zhou L, Cai C, Gao Y, Su S, Wu J (2018) Variational autoencoder for low bitrate image compression. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops

  37. Grassucci E, Barbarossa S, Comminiello D (2023) Generative semantic communication: diffusion models beyond bit recovery. arXiv preprint arXiv:2306.04321

  38. Gulrajani I, Raffel C, Metz L (2020) Towards GAN benchmarks which require generalization. arXiv preprint arXiv:2001.03653

  39. Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A, Chen X (2016) Improved techniques for training gans. Advances in neural information processing systems 29

  40. Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2017) GANs trained by a two time-scale update rule converge to a local Nash equilibrium. Advances in neural information processing systems 30

  41. Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: A largescale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp 248–255. IEEE

  42. Borji A (2019) Pros and cons of GAN evaluation measures. Computer Vision and Image Understanding 179:41–65

    Article  Google Scholar 

  43. Borji A (2022) Pros and cons of GAN evaluation measures: new developments. Computer Vision and Image Understanding 215

  44. Mathiasen A, Hvilshoj F (2020) Fast frëchet inception distance. CoRR abs/2009.14075

  45. Chong MJ, Forsyth DA (2019) Effectively unbiased FID and inception score and where to find them. CoRR abs/1911.07023

  46. Naeem MF, Oh SJ, Uh Y, Choi Y, Yoo J (2020) Reliable fidelity and diversity metrics for generative models. CoRR abs/2002.09797

  47. Zhou S, Gordon M, Krishna R, Narcomey A, Fei-Fei LF, Bernstein M (2019) Hype: a benchmark for human eye perceptual evaluation of generative models. In: Wallach H, Larochelle H, Beygelzimer A, Alchë-Buc F, Fox E, Garnett R (Eds.) Advances in Neural Information Processing Systems, vol 32. https://proceedings.neurips.cc/paper files/paper/2019/file/65699726a3c601b9f31bf04019c8593c-Paper.pdf

  48. Xia P, Wu S, Van Durme B (2020) Which* bert? a survey organizing contextualized encoders. arXiv preprint arXiv:2010.00854

  49. Rajpurkar P, Zhang J, Lopyrev K, Liang P (2016) SQuAD: 100,000+ questions for machine comprehension of text. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp 2383–2392. Association for Computational Linguistics, Austin, Texas. https://doi.org/10.18653/v1/D16-1264 . https://aclanthology.org/D16-1264

  50. La G, Xie Q, Liu H, Yang Y, Hovy E (2017) RACE: large-scale reading comprehension dataset from examinations. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp 785–794. Association for Computational Linguistics, Copenhagen, Denmark. https://doi.org/10.18653/v1/D17-1082 . https://aclanthology.org/D17-1082

  51. Wang A, Singh A, Michael J, Hill F, Levy O, Bowman SR (2018) Glue: a multi-task benchmark and analysis platform for natural language understanding. arXiv preprint arXiv:1804.07461

  52. Kaplan J, McCandlish S, Henighan T, Brown TB, Chess B, Child R, Gray S, Radford A, Wu J, Amodei D (2020) Scaling laws for neural language models. arXiv preprint arXiv:2001.08361

  53. Gunnarsson M (2021) Multi-hop neural question answering in the telecom domain. Master’s thesis, LTH, Lund University

  54. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T. Dehghani M, Minderer M, Heigold G, Gelly S, et al (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929

  55. Touvron H, Cord M, Douze M, Massa F, Sablayrolles A, Jëgou H (2021) Training data-efficient image transformers & distillation through attention. In: International Conference on Machine Learning, pp 10347–10357. PMLR

  56. Zhu X, Su W, Lu L, Li B, Wang X, Dai J (2020) Deformable detr: deformable transformers for end-to-end object detection. arXiv preprint arXiv:2010.04159

  57. Soltani M, Pourahmadi V, Mirzaei A, Sheikhzadeh H (2019) Deep learning-based channel estimation. IEEE Communications Letters 23(4):652–655

    Article  Google Scholar 

  58. Yang Y, Li Y, Zhang W, Qin F, Zhu P, Wang C-X (2019) Generative-adversarial-network-based wireless channel modeling: challenges and opportunities. IEEE Communications Magazine 57(3):22–27. https://doi.org/10.1109/MCOM.2019.1800635

    Article  Google Scholar 

  59. Orekondy T, Behboodi A, Soriaga JB (2022) MIMO-GAN: generative MIMO channel modeling

  60. O’Shea TJ, Roy T, West N (2019) Approximating the void: learning stochastic channel models from observation with variational generative adversarial networks. In: 2019 International Conference on Computing, Networking and Communications (ICNC), pp 681–686. https://doi.org/10.1109/ICCNC.2019.8685573

  61. Davaslioglu K, Sagduyu YE (2018) Generative adversarial learning for spectrum sensing. In: 2018 IEEE International Conference on Communications (ICC), pp 1–6. https://doi.org/10.1109/ICC.2018.8422223

  62. DCM, Reddy, BVR, (2023) Enhanced capsule generative adversarial network for spectrum and energy efficiency of cooperative spectrum prediction framework in cognitive radio network. Transactions on Emerging Telecommunications Technologies 34(4):4736. https://doi.org/10.1002/ett.4736

  63. Wang Y, Gao Z, Zheng D, Chen S, Gunduz D, Poor HV (2022) Transformer-empowered 6G intelligent networks: from massive MIMO processing to semantic communication. IEEE Wireless Communications, pp 1–9. https://doi.org/10.1109/MWC.008.2200157

  64. Hussien M, Nguyen KK, Cheriet M (2022) PRVNet: a novel partially-regularized variational autoencoders for massive MIMO CSI feedback. In: 2022 IEEE Wireless Communications and Networking Conference (WCNC), pp 2286–2291. https://doi.org/10.1109/WCNC51071.2022.9771642

  65. Nguyen NT, Ma M, Shlezinger N, Eldar YC, Swindlehurst AL, Juntti MJ (2023) Deep unfolding hybrid beamforming designs for THz massive MIMO systems. arXiv:2302.12041

  66. Balevi E, Andrews JG (2021) Unfolded hybrid beamforming with GAN compressed ultra-low feedback overhead. IEEE Transactions on Wireless Communications 20(12):8381–8392. https://doi.org/10.1109/TWC.2021.3092350

    Article  Google Scholar 

  67. Anande TJ, Al-Saadi S, Leeson MS (2023) Generative adversarial networks for network traffic feature generation. International Journal of Computers and Applications 45(4):297–305. https://doi.org/10.1080/1206212X.2023.2191072

    Article  Google Scholar 

  68. Zhang C, Ouyang X, Patras P (2017) ZipNet-GAN: inferring fine-grained mobile traffic patterns via a generative adversarial neural network. In: Proceedings of the 13th International Conference on Emerging Networking EXperiments and Technologies. CoNEXT’17, pp 363–375. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3143361.3143393

  69. Nguyen QP, Wai L, Divakaran DM, Low K, Chan M (2019) GEE: a gradient based explainable variational autoencoder for network anomaly detection. https://doi.org/10.1109/CNS.2019.8802833

  70. Leng C, Yang C, Chen S, Wu Q, Peng Y (2022) GAN for load estimation and traffic-aware network selection for 5G terminals. IEEE Internet of Things Journal 9(17):16353–16362. https://doi.org/10.1109/JIOT.2022.3152729

    Article  Google Scholar 

  71. Ott H, Bogatinovski J, Acker A, Nedelkoski S, Kao O (2021) Robust and transferable anomaly detection in log data using pre-trained language models. In: 2021 IEEE/ACM International Workshop on Cloud Intelligence (CloudIntelligence), pp 19–24. https://doi.org/10.1109/CloudIntelligence52565.2021.00013

  72. Almodovar C, Sabrina F, Karimi S, Azad S (2022) Can language models help in system security? Investigating log anomaly detection using BERT. In: Proceedings of the The 20th Annual Workshop of the Australasian Language Technology Association, pp 139–147. Australasian Language Technology Association, Adelaide, Australia. https://aclanthology.org/2022.alta-1.19

  73. Le V-H, Zhang H (2023) Log parsing with prompt-based few-shot learning. arXiv preprint arXiv:2302.07435

  74. Ahmed S, Singh M, Doherty B, Ramlan E, Harkin K, Bucholc M, Coyle D (2023) An empirical analysis of state-of-art classification models in an it incident severity prediction framework. Applied Sciences 13(6). https://doi.org/10.3390/app13063843

  75. Alexander B (2022) Automated trouble report labeling in the Telecom industry. Thesis at Uppsala University, Department of Information Technology, ISSN 1401–5749

  76. Zhang S, Wijesinghe A, Ding Z (2023) RME-GAN: a learning framework for radio map estimation based on conditional generative adversarial network. IEEE Internet of Things Journal 1–1. https://doi.org/10.1109/JIOT.2023.3278235

  77. Tian Y, Yuan S, Chen W, Liu N (2021) Transformer based radio map prediction model for dense urban environments. In: 2021 13th International Symposium on Antennas, Propagation and EM Theory (ISAPE), vol 1, pp 1–3. https://doi.org/10.1109/ISAPE54070.2021.9753644

  78. Maksymyuk T, Gazda J, Luntovskyy A, Klymash M (2018) Artificial intelligence based 5G coverage design and optimization using deep generative adversarial neural networks. In: 2018 International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo), pp 1–4. https://doi.org/10.1109/UkrMiCo43733.2018.9047611

  79. Bao L, Liu X, Wang F, Fang B (2019) ACTGAN: automatic configuration tuning for software systems with generative adversarial networks. In: 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE), pp 465–476. https://doi.org/10.1109/ASE.2019.00051

  80. Ickin S (2021) Recommending changes on QoE factors with conditional variational autoencoder. In: Proceedings of the 4th FlexNets Workshop on Flexible Networks Artificial Intelligence Supported Network Flexibility and Agility. FlexNets’21, pp 20–25. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3472735.3473387

  81. Rizwan A, Abu-Dayya A, Filali F, Imran A (2022) Addressing data sparsity with GANs for multi-fault diagnosing in emerging cellular networks. In: 2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp 318–323. https://doi.org/10.1109/ICAIIC54071.2022.9722696

  82. Hua Y, Li R, Zhao Z, Zhang H, Chen X (2019) GAN-based deep distributional reinforcement learning for resource management in network slicing. In: 2019 IEEE Global Communications Conference (GLOBECOM), pp 1–6. https://doi.org/10.1109/GLOBECOM38437.2019.9014217

  83. Abbas K, Afaq M, Ahmed Khan T, Rafiq A, Song W-C (2020) Slicing the core network and radio access network domains through intent-based networking for 5G networks. Electronics 9(10). https://doi.org/10.3390/electronics9101710

  84. Hu W, Tan Y (2022) Generating adversarial malware examples for black-box attacks based on GAN. In: Tan Y, Shi Y (eds) Data Mining and Big Data. Springer, Singapore, pp 409–423

    Chapter  Google Scholar 

  85. Peng X, Xian H, Lu Q, Lu X (2021) Semantics aware adversarial malware examples generation for black-box attacks. Applied Soft Computing 109:107506. https://doi.org/10.1016/j.asoc.2021.107506

    Article  Google Scholar 

  86. Roy D, Mukherjee T, Chatterjee M, Pasiliao E (2019) Detection of rogue RF transmitters using generative adversarial nets. In: 2019 IEEE Wireless Communications and Networking Conference (WCNC), pp 1–7. https://doi.org/10.1109/WCNC.2019.8885548

  87. Chen Z, Peng L, Hu A (2021) Fu H (2021) Generative adversarial network-based rogue device identification using differential constellation trace figure. EURASIP Journal on Wireless Communications and Networking 1:72. https://doi.org/10.1186/s13638-021-01950-2

    Article  Google Scholar 

  88. Behringer MH, Pritikin M, Bjarnason S, Clemm A, Carpenter BE, Jiang S, Ciavaglia L (2015) Autonomic networking: definitions and design goals. RFC Editor. https://doi.org/10.17487/RFC7575

  89. Soman S, G RH (2023) Observations on LLMs for Telecom domain: capabilities and limitations. arXiv:2305.13102

  90. Gräsler I, Preus D, Brandt L, Mohr M (2022) Efficient extraction of technical requirements applying data augmentation. In: 2022 IEEE International Symposium on Systems Engineering (ISSE), pp 1–8 . https://doi.org/10.1109/ISSE54508.2022.10005452

  91. Allam S (2023) AI-based use-pattern generative hybrid spaces for indoor and outdoor activities. In: 2023 20th Learning and Technology Conference (L &T), pp 54–58. https://doi.org/10.1109/LT58159.2023.10092345

  92. Mentzer F, Agustsson E, Ballë J, Minnen D, Johnston N, Toderici G (2022) Neural video compression using GANs for detail synthesis and propagation. [eess.IV] arXiv:2107.12038

  93. Moustris G, Tzafestas C, Konstantinidis K (2023) A long distance telesurgical demonstration on robotic surgery phantoms over 5G. International Journal of Computer Assisted Radiology and Surgery. https://doi.org/10.1007/s11548-023-02913-2

  94. Xu S, Perez M, Yang K, Perrenot C, Felblinger J, Hubert J (2014) Determination of the latency effects on surgical performance and the acceptable latency levels in telesurgery using the dV-Trainer (r) simulator. Surgical endoscopy 28. https://doi.org/10.1007/s00464-014-3504-z

  95. Inam R, Schrammar N, Wang K, Karapantelakis A, Mokrushin L, Feljan AV, Fersman E (2016) Feasibility assessment to realise vehicle teleoperation using cellular networks. In: 2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC), pp 2254–2260. https://doi.org/10.1109/ITSC.2016.7795920

  96. Zhang JA, Rahman ML, Wu K, Huang X, Guo YJ, Chen S, Yuan J (2022) Enabling joint communication and radar sensing in mobile networks-a survey. IEEE Communications Surveys & Tutorials 24(1):306–345. https://doi.org/10.1109/COMST.2021.3122519

    Article  Google Scholar 

  97. Li X, Chen M, Liu Y, Zhang Z, Liu, D, Mao S (2023) Graph neural networks for joint communication and sensing optimization in vehicular networks. arXiv:2302.02878

  98. Rahnemoonfar M, Johnson J, Paden J (2019) AI radar sensor: creating radar depth sounder images based on generative adversarial network. Sensors 19(24) https://doi.org/10.3390/s19245479

  99. Serreli L, Nonnis R, Bingöl G, Anedda M, Fadda M, Giusto DD (2021) Fingerprint-based positioning method over LTE advanced pro signals with GAN training contribute. In: 2021 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), pp 1–5. https://doi.org/10.1109/BMSB53066.2021.9547015

  100. Wei W, Yan J, Wan L, Wang C, Zhang G, Wu X (2021) Enriching indoor localization fingerprint using a single AC-GAN. In: 2021 IEEE Wireless Communications and Networking Conference (WCNC), pp 1–6. https://doi.org/10.1109/WCNC49053.2021.9417513

  101. Xie H, Qin Z, Li GY, Juang B-H (2021) Deep learning enabled semantic communication systems. IEEE Transactions on Signal Processing 69:2663–2675. https://doi.org/10.1109/tsp.2021.3071210

    Article  MathSciNet  Google Scholar 

  102. Weng Z, Qin Z (2021) Semantic communication systems for speech transmission. IEEE Journal on Selected Areas in Communications 39(8):2434–2444. https://doi.org/10.1109/JSAC.2021.3087240

    Article  Google Scholar 

  103. Yoo H, Jung T, Dai L, Kim S, Chae C-B (2022) Demo: Real-time semantic communications with a vision transformer. In: 2022 IEEE International Conference on Communications Workshops (ICC Workshops), pp 1–2. https://doi.org/10.1109/ICCWorkshops53468.2022.9914635

  104. Tsialiamanis G, Wagg DJ, Dervilis N, Worden K (2021) On generative models as the basis for digital twins. Data-Centric Engineering 2:11. https://doi.org/10.1017/dce.2021.13

    Article  Google Scholar 

  105. Mozo A, Karamchandani A, Gómez-Canaval S, Sanz M, Moreno JI, Pastor A (2022) B5GEMINI: AI-driven network digital twin. Sensors 22(11) https://doi.org/10.3390/s22114106

  106. Baldvinsson JR, Ganjalizadeh M, AlAbbasi A, Björkman M, Payberah AH (2022) IL-GAN: rare sample generation via incremental learning in GANs. In: GLOBECOM 2022 - 2022 IEEE Global Communications Conference, pp 621–626. https://doi.org/10.1109/GLOBECOM48099.2022.10001069

  107. Svyatkovskiy A, Deng SK, Fu S, Sundaresan N (2020) Intellicode compose: code generation using transformer. In: Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering. ESEC/FSE 2020, pp 1433–1443. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3368089.3417058

  108. Sun Z, Du X, Song F, Wang S, Ni M, Li L (2023) Don’t complete it! Preventing unhelpful code completion for productive and sustainable neural code completion systems. arXiv:2209.05948 [cs.SE]

  109. Ganesh P, Chen Y, Lou X, Khan MA, Yang Y, Sajjad H, Nakov P, Chen D, Winslett M (2021) Compressing large-scale transformer-based models: a case study on BERT. Transactions of the Association for Computational Linguistics 9:1061–1080. https://doi.org/10.1162/tacl_a_00413

    Article  Google Scholar 

  110. Geiger A, Liu D, Alnegheimish S, Cuesta-Infante A, Veeramachaneni K (2020) TadGAN: time series anomaly detection using generative adversarial networks

  111. Li Y, Peng X, Zhang J, Li Z, Wen M (2023) DCT-GAN: dilated convolutional transformer-based GAN for time series anomaly detection. IEEE Transactions on Knowledge and Data Engineering 35(4):3632–3644. https://doi.org/10.1109/TKDE.2021.3130234

    Article  Google Scholar 

  112. Zhang Q, Xiao T, Efros AA, Pinto L, Wang X (2021) Learning cross-domain correspondence for control with dynamics cycle-consistency. In: International Conference on Learning Representations. https://openreview.net/forum?id=QIRlze3I6hX

  113. Dey K, Perepu SK, Dasgupta P, Das A (2023) Domain adaptation of reinforcement learning agents based on network service proximity. In: 2023 IEEE 9th International Conference on Network Softwarization (NetSoft), pp 152–160 (2023). https://doi.org/10.1109/NetSoft57336.2023.10175507

  114. d’Ascoli S, Touvron H, Leavitt ML, Morcos AS, Biroli G (2022) Sagun L (2022) Convit: improving vision transformers with soft convolutional inductive biases. Journal of Statistical Mechanics: Theory and Experiment 11. https://doi.org/10.1088/1742-5468/ac9830

  115. TSG RAN - radio access network. https://www.3gpp.org/3gpp-groups/radio-access-networks-ran

  116. Wu W, Yang B, Wang D, Zhang W (2020) A novel trajectory generator based on a constrained GAN and a latent variables predictor. IEEE Access 8:212529–212540. https://doi.org/10.1109/ACCESS.2020.3039801

    Article  Google Scholar 

  117. Wei H, Ye D, Liu Z, Wu H, Yuan B, Fu Q, Yang W, Li Z (2021) Boosting offline reinforcement learning with residual generative modeling. In: Zhou Z-H (ed.) Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pp 3574–3580. Main Track. https://doi.org/10.24963/ijcai.2021/492

Download references

Author information

Authors and Affiliations

  1. Ericsson Research, Ericsson AB, Torshamnsgatan 21, Kista, Stockholm, 164 83, Sweden

    Athanasios Karapantelakis, Abdulrahman Alabassi & Alexandros Nikou

  2. Ericsson Research, Ericsson France, 25 Carnot, Massy, 91300, France

    Pegah Alizadeh

  3. Ericsson Research, Ericsson India, MGR Salai, Perengudi, Chennai, Tamil Nadu, 600096, India

    Kaushik Dey

Authors
  1. Athanasios Karapantelakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pegah Alizadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdulrahman Alabassi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kaushik Dey
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alexandros Nikou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Athanasios Karapantelakis.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Karapantelakis, A., Alizadeh, P., Alabassi, A. et al. Generative AI in mobile networks: a survey. Ann. Telecommun. 79, 15–33 (2024). https://doi.org/10.1007/s12243-023-00980-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12243-023-00980-9

Keywords

Search

Navigation