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The recent evolution of generative artificial intelligence (GAI) leads to the emergence of groundbreaking applications such as ChatGPT, which not only enhances the efficiency of digital content production, such as text, audio, video, or even network traffic data, but also enriches its diversity.
C. Doersch, “Tutorial on variational autoencoders,” arXiv preprint arXiv:1606.05908 , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
T. Wang, C.-K. Wen, H. Wang, F. Gao, T. Jiang, and S. Jin, “Deep learning for wireless physical layer: Opportunities and challenges,” China Communications , vol. 14, no. 11, pp. 92–111, 2017
2017
Earlier work this paper cites.
H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang, and D. N. Metaxas, “Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 5907–5915
2017
Earlier work this paper cites.
G. Papamakarios, T. Pavlakou, and I. Murray, “Masked autoregressive flow for density estimation,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
T. O’shea and J. Hoydis, “An introduction to deep learning for the physical layer,” IEEE Transactions on Cognitive Communications and Networking , vol. 3, no. 4, pp. 563–575, 2017
2017
Earlier work this paper cites.
H. Ye and G. Y. Li, “Initial results on deep learning for joint channel equalization and decoding,” in 2017 IEEE 86th vehicular technology conference (VTC-Fall) . IEEE, 2017, pp. 1–5
2017
Earlier work this paper cites.
M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein generative adversarial networks,” in International conference on machine learning . PMLR, 2017, pp. 214–223
2017
Earlier work this paper cites.
R. Sattiraju, A. Weinand, and H. D. Schotten, “Performance analysis of deep learning based on recurrent neural networks for channel coding,” in 2018 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) . IEEE, 2018, pp. 1–6
2018
Earlier work this paper cites.
C. Wang, C. Xu, C. Wang, and D. Tao, “Perceptual adversarial networks for image-to-image transformation,” IEEE Transactions on Image Processing , vol. 27, no. 8, pp. 4066–4079, 2018
2018
Earlier work this paper cites.
F. P. Casale, A. Dalca, L. Saglietti, J. Listgarten, and N. Fusi, “Gaussian process prior variational autoencoders,” Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
A. Oussidi and A. Elhassouny, “Deep generative models: Survey,” in 2018 International conference on intelligent systems and computer vision (ISCV) . IEEE, 2018, pp. 1–8
2018
Earlier work this paper cites.
B. Tang, Y. Tu, Z. Zhang, and Y. Lin, “Digital signal modulation classification with data augmentation using generative adversarial nets in cognitive radio networks,” IEEE Access , vol. 6, pp. 15 713–15 722, 2018
2018
Earlier work this paper cites.
M. Li, O. Li, G. Liu, and C. Zhang, “Generative adversarial networks-based semi-supervised automatic modulation recognition for cognitive radio networks,” Sensors , vol. 18, no. 11, p. 3913, 2018
2018
Earlier work this paper cites.
C. Zhao, C. Chen, Z. He, and Z. Wu, “Application of auxiliary classifier wasserstein generative adversarial networks in wireless signal classification of illegal unmanned aerial vehicles,” Applied Sciences , vol. 8, no. 12, p. 2664, 2018
2018
Earlier work this paper cites.
A. Caciularu and D. Burshtein, “Blind channel equalization using variational autoencoders,” in 2018 IEEE international conference on communications workshops (ICC Workshops) . IEEE, 2018, pp. 1–6
2018
Earlier work this paper cites.
X. Li, A. Alkhateeb, and C. Tepedelenlioğlu, “Generative adversarial estimation of channel covariance in vehicular millimeter wave systems,” in 2018 52nd Asilomar Conference on Signals, Systems, and Computers . IEEE, 2018, pp. 1572–1576
2018
Earlier work this paper cites.
T. Erpek, Y. E. Sagduyu, and Y. Shi, “Deep learning for launching and mitigating wireless jamming attacks,” IEEE Transactions on Cognitive Communications and Networking , vol. 5, no. 1, pp. 2–14, 2018
2018
Earlier work this paper cites.
C.-K. Wen, W.-T. Shih, and S. Jin, “Deep learning for massive mimo csi feedback,” IEEE Wireless Communications Letters , vol. 7, no. 5, pp. 748–751, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
S. M. Aldossari and K.-C. Chen, “Machine learning for wireless communication channel modeling: An overview,” Wireless Personal Communications , vol. 106, pp. 41–70, 2019
2019
Earlier work this paper cites.
Z. Qin, H. Ye, G. Y. Li, and B.-H. F. Juang, “Deep learning in physical layer communications,” IEEE Wireless Communications , vol. 26, no. 2, pp. 93–99, 2019
2019
Earlier work this paper cites.
N. Samuel, T. Diskin, and A. Wiesel, “Learning to detect,” IEEE Transactions on Signal Processing , vol. 67, no. 10, pp. 2554–2564, 2019
2019
Earlier work this paper cites.
T. J. O’Shea, T. Roy, and N. West, “Approximating the void: Learning stochastic channel models from observation with variational generative adversarial networks,” in 2019 International Conference on Computing, Networking and Communications (ICNC) . IEEE, 2019, pp. 681–686
2019
Earlier work this paper cites.
M. Soltani, V. Pourahmadi, A. Mirzaei, and H. Sheikhzadeh, “Deep learning-based channel estimation,” IEEE Communications Letters , vol. 23, no. 4, pp. 652–655, 2019
2019
Earlier work this paper cites.
K. Merchant and B. Nousain, “Securing iot rf fingerprinting systems with generative adversarial networks,” in MILCOM 2019-2019 IEEE Military Communications Conference (MILCOM) . IEEE, 2019, pp. 584–589
2019
Earlier work this paper cites.
D. Roy, T. Mukherjee, M. Chatterjee, and E. Pasiliao, “Detection of rogue rf transmitters using generative adversarial nets,” in 2019 IEEE wireless communications and networking conference (WCNC) . IEEE, 2019, pp. 1–7
2019
Earlier work this paper cites.
J. Gong, X. Xu, Y. Qin, and W. Dong, “A generative adversarial network based framework for specific emitter characterization and identification,” in 2019 11th International Conference on Wireless Communications and Signal Processing (WCSP) . IEEE, 2019, pp. 1–6
2019
Earlier work this paper cites.
Y. Shi, K. Davaslioglu, and Y. E. Sagduyu, “Generative adversarial network for wireless signal spoofing,” in Proceedings of the ACM Workshop on Wireless Security and Machine Learning , 2019, pp. 55–60
2019
Earlier work this paper cites.
T. Roy, T. O’Shea, and N. West, “Generative adversarial radio spectrum networks,” in Proceedings of the ACM Workshop on Wireless Security and Machine Learning , 2019, pp. 12–15
2019
Earlier work this paper cites.
T. Zhang, K. Zhu, and D. Niyato, “A generative adversarial learning-based approach for cell outage detection in self-organizing cellular networks,” IEEE Wireless Communications Letters , vol. 9, no. 2, pp. 171–174, 2019
2019
Earlier work this paper cites.
H. He, S. Jin, C.-K. Wen, F. Gao, G. Y. Li, and Z. Xu, “Model-driven deep learning for physical layer communications,” IEEE Wireless Communications , vol. 26, no. 5, pp. 77–83, 2019
2019
Earlier work this paper cites.
M. Vahdat, K. P. Roshandeh, M. Ardakani, and H. Jiang, “Papr reduction scheme for deep learning-based communication systems using autoencoders,” in 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring) . IEEE, 2020, pp. 1–5
2020
Earlier work this paper cites.
L. Sun, Y. Wang, A. L. Swindlehurst, and X. Tang, “Generative-adversarial-network enabled signal detection for communication systems with unknown channel models,” IEEE Journal on Selected Areas in Communications , vol. 39, no. 1, pp. 47–60, 2020
2020
Earlier work this paper cites.
H. Ye, L. Liang, G. Y. Li, and B.-H. Juang, “Deep learning-based end-to-end wireless communication systems with conditional gans as unknown channels,” IEEE Transactions on Wireless Communications , vol. 19, no. 5, pp. 3133–3143, 2020
2020
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM , vol. 63, no. 11, pp. 139–144, 2020
2020
Earlier work this paper cites.
B. Tolba, M. Elsabrouty, M. G. Abdu-Aguye, H. Gacanin, and H. M. Kasem, “Massive mimo csi feedback based on generative adversarial network,” IEEE Communications Letters , vol. 24, no. 12, pp. 2805–2808, 2020
2020
Earlier work this paper cites.
I. Kobyzev, S. J. Prince, and M. A. Brubaker, “Normalizing flows: An introduction and review of current methods,” IEEE transactions on pattern analysis and machine intelligence , vol. 43, no. 11, pp. 3964–3979, 2020
2020
Earlier work this paper cites.
Z. Chen, F. Gu, and R. Jiang, “Channel estimation method based on transformer in high dynamic environment,” in 2020 International Conference on Wireless Communications and Signal Processing (WCSP) . IEEE, 2020, pp. 817–822
2020
Earlier work this paper cites.
G. Harshvardhan, M. K. Gourisaria, M. Pandey, and S. S. Rautaray, “A comprehensive survey and analysis of generative models in machine learning,” Computer Science Review , vol. 38, p. 100285, 2020
2020
Earlier work this paper cites.
H. Kim, S. Oh, and P. Viswanath, “Physical layer communication via deep learning,” IEEE Journal on Selected Areas in Information Theory , vol. 1, no. 1, pp. 5–18, 2020
2020
Earlier work this paper cites.
F. Restuccia and T. Melodia, “Deep learning at the physical layer: System challenges and applications to 5g and beyond,” IEEE Communications Magazine , vol. 58, no. 10, pp. 58–64, 2020
2020
Earlier work this paper cites.
T. Xu and I. Darwazeh, “Wavelet classification for non-cooperative non-orthogonal signal communications,” in 2020 IEEE Globecom Workshops (GC Wkshps . IEEE, 2020, pp. 1–6
2020
Earlier work this paper cites.
C. Liu, Z. Wei, D. W. K. Ng, J. Yuan, and Y.-C. Liang, “Deep transfer learning for signal detection in ambient backscatter communications,” IEEE Transactions on Wireless Communications , vol. 20, no. 3, pp. 1624–1638, 2020
2020
Earlier work this paper cites.
N. Shlezinger, N. Farsad, Y. C. Eldar, and A. J. Goldsmith, “Viterbinet: A deep learning based viterbi algorithm for symbol detection,” IEEE Transactions on Wireless Communications , vol. 19, no. 5, pp. 3319–3331, 2020
2020
Earlier work this paper cites.
——, “Unsupervised linear and nonlinear channel equalization and decoding using variational autoencoders,” IEEE Transactions on Cognitive Communications and Networking , vol. 6, no. 3, pp. 1003–1018, 2020
2020
Earlier work this paper cites.
T. Hu, Y. Huang, Q. Zhu, and Q. Wu, “Channel estimation enhancement with generative adversarial networks,” IEEE Transactions on Cognitive Communications and Networking , vol. 7, no. 1, pp. 145–156, 2020
2020
Cited alongside, same era.
Y. Cai, F. Song, Y. Xu, X. Liu, X. Zhang, and H. Han, “Spectrum waterfall completion in jamming enviroment: A general adversarial networks method,” in 2020 IEEE 9th Joint International Information Technology and Artificial Intelligence Conference (ITAIC) , vol. 9. IEEE, 2020, pp. 1661–1665
2020
Cited alongside, same era.
Y. Tang, Z. Zhao, X. Ye, S. Zheng, and L. Wang, “Jamming recognition based on ac-vaegan,” in 2020 15th IEEE International Conference on Signal Processing (ICSP) , vol. 1. IEEE, 2020, pp. 312–315
2020
Cited alongside, same era.
A. Toma, A. Krayani, M. Farrukh, H. Qi, L. Marcenaro, Y. Gao, and C. S. Regazzoni, “Ai-based abnormality detection at the phy-layer of cognitive radio by learning generative models,” IEEE Transactions on Cognitive Communications and Networking , vol. 6, no. 1, pp. 21–34, 2020
N. Van Huynh and G. Y. Li, “Transfer learning for signal detection in wireless networks,” IEEE Wireless Communications Letters , vol. 11, no. 11, pp. 2325–2329, 2022
2022
Later among the works it cites.
E. Shtaiwi, A. El Ouadrhiri, M. Moradikia, S. Sultana, A. Abdelhadi, and Z. Han, “Mixture gan for modulation classification resiliency against adversarial attacks,” in GLOBECOM 2022-2022 IEEE Global Communications Conference . IEEE, 2022, pp. 1472–1477
2022
Later among the works it cites.
M. A. Alawad, M. Q. Hamdan, K. A. Hamdi, C. H. Foh, and A. U. Quddus, “A new approach for an end-to-end communication system using variational auto-encoder (vae),” in GLOBECOM 2022-2022 IEEE Global Communications Conference . IEEE, 2022, pp. 5159–5164
2022
Later among the works it cites.
M. A. Alawad, M. Q. Hamdan, and K. A. Hamdi, “Innovative variational autoencoder for an end-to-end communication system,” IEEE Access , 2022
2022
Later among the works it cites.
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2020
Cited alongside, same era.
H. Han, L. Cui, W. Li, L. Huang, Y. Cai, J. Cai, and Y. Zhang, “Radio frequency fingerprint based wireless transmitter identification against malicious attacker: An adversarial learning approach,” in 2020 International Conference on Wireless Communications and Signal Processing (WCSP) . IEEE, 2020, pp. 310–315
2020
Cited alongside, same era.
W. Fan, F. Zhou, and T. Tian, “A deceptive jamming template synthesis method for sar using generative adversarial nets,” in IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium . IEEE, 2020, pp. 6926–6929
2020
Cited alongside, same era.
K. S. Germain and F. Kragh, “Physical-layer authentication using channel state information and machine learning,” in 2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS) . IEEE, 2020, pp. 1–8
2020
Cited alongside, same era.
Y. Shi, K. Davaslioglu, and Y. E. Sagduyu, “Generative adversarial network in the air: Deep adversarial learning for wireless signal spoofing,” IEEE Transactions on Cognitive Communications and Networking , vol. 7, no. 1, pp. 294–303, 2020
2020
Cited alongside, same era.
A. M. Elbir, A. Papazafeiropoulos, P. Kourtessis, and S. Chatzinotas, “Deep channel learning for large intelligent surfaces aided mm-wave massive mimo systems,” IEEE Wireless Communications Letters , vol. 9, no. 9, pp. 1447–1451, 2020
2020
Cited alongside, same era.
H. Ngo, H. Fang, and H. Wang, “Deep learning-based adaptive beamforming for mmwave wireless body area network,” in GLOBECOM 2020-2020 IEEE Global Communications Conference . IEEE, 2020, pp. 1–6
2020
Cited alongside, same era.
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 8110–8119
2020
Cited alongside, same era.
X. Liang, Z. Liu, H. Chang, and L. Zhang, “Wireless channel data augmentation for artificial intelligence of things in industrial environment using generative adversarial networks,” in 2020 IEEE 18th International Conference on Industrial Informatics (INDIN) , vol. 1. IEEE, 2020, pp. 502–507
2020
Cited alongside, same era.
M. Ye, H. Zhang, and J.-B. Wang, “Channel estimation for intelligent reflecting surface aided wireless communications using conditional gan,” IEEE Communications Letters , vol. 26, no. 10, pp. 2340–2344, 2022
2022
Later among the works it cites.
Y. Hu, M. Yin, W. Xia, S. Rangan, and M. Mezzavilla, “Multi-frequency channel modeling for millimeter wave and thz wireless communication via generative adversarial networks,” in 2022 56th Asilomar Conference on Signals, Systems, and Computers . IEEE, 2022, pp. 670–676
2022
Later among the works it cites.
Y. Guo, Z. Qin, and O. A. Dobre, “Federated generative adversarial networks based channel estimation,” in 2022 IEEE International Conference on Communications Workshops (ICC Workshops) . IEEE, 2022, pp. 61–66
2022
Later among the works it cites.
I. Rasheed, M. Asif, A. Ihsan, W. U. Khan, M. Ahmed, and K. M. Rabie, “Lstm-based distributed conditional generative adversarial network for data-driven 5g-enabled maritime uav communications,” IEEE Transactions on Intelligent Transportation Systems , vol. 24, no. 2, pp. 2431–2446, 2022
2022
Later among the works it cites.
B. Banerjee, R. C. Elliott, W. A. Krzymień, and H. Farmanbar, “Downlink channel estimation for fdd massive mimo using conditional generative adversarial networks,” IEEE Transactions on Wireless Communications , vol. 22, no. 1, pp. 122–137, 2022
2022
Later among the works it cites.
P. F. de Araujo-Filho, G. Kaddoum, M. Naili, E. T. Fapi, and Z. Zhu, “Multi-objective gan-based adversarial attack technique for modulation classifiers,” IEEE Communications Letters , vol. 26, no. 7, pp. 1583–1587, 2022
2022
Later among the works it cites.
Y. Yang, L. Zhu, Q. He, and X. Deng, “A simple high-performance generation method for spoofing jamming signals,” in 2022 International Symposium on Networks, Computers and Communications (ISNCC) . IEEE, 2022, pp. 1–5
2022
Later among the works it cites.
R. Meng, X. Xu, B. Wang, H. Sun, S. Xia, S. Han, and P. Zhang, “Physical-layer authentication based on hierarchical variational autoencoder for industrial internet of things,” IEEE Internet of Things Journal , vol. 10, no. 3, pp. 2528–2544, 2022
2022
Later among the works it cites.
L. Yang, S. X. Yang, Y. Li, Y. Lu, and T. Guo, “Generative adversarial learning for trusted and secure clustering in industrial wireless sensor networks,” IEEE Transactions on Industrial Electronics , vol. 70, no. 8, pp. 8377–8387, 2022
2022
Later among the works it cites.
J. Han, Y. Zhou, G. Liu, T. Liu, and X. Zeng, “A novel physical layer key generation method based on wgan-gp adversarial autoencoder,” in 2022 4th International Conference on Communications, Information System and Computer Engineering (CISCE) . IEEE, 2022, pp. 1–6
2022
Later among the works it cites.
B. Barnes-Cook and T. O’Shea, “Scalable wireless anomaly detection with generative-lstms on rf post-detection metadata,” in 2022 IEEE Wireless Communications and Networking Conference (WCNC) . IEEE, 2022, pp. 483–488
2022
Later among the works it cites.
——, “Channel distribution learning: Model-driven gan-based channel modeling for irs-aided wireless communication,” IEEE Transactions on Communications , vol. 70, no. 7, pp. 4482–4497, 2022
2022
Later among the works it cites.
L. Pang, Y. Li, Y. Zhang, M. Shang, Y. Chen, and A. Wang, “Mggan-based hybrid beamforming design for massive mimo systems against rank-deficient channels,” IEEE Communications Letters , vol. 26, no. 11, pp. 2804–2808, 2022
2022
Later among the works it cites.
J. Dai, S. Wang, K. Tan, Z. Si, X. Qin, K. Niu, and P. Zhang, “Nonlinear transform source-channel coding for semantic communications,” IEEE Journal on Selected Areas in Communications , vol. 40, no. 8, pp. 2300–2316, 2022
2022
Later among the works it cites.
M. Hussien, K. K. Nguyen, and M. Cheriet, “Prvnet: A novel partially-regularized variational autoencoders for massive mimo csi feedback,” in 2022 IEEE Wireless Communications and Networking Conference (WCNC) . IEEE, 2022, pp. 2286–2291
2022
Later among the works it cites.
P. P. Ray, “Chatgpt: A comprehensive review on background, applications, key challenges, bias, ethics, limitations and future scope,” Internet of Things and Cyber-Physical Systems , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Y. Li, X. Chen, and X. Deng, “Joint source-channel coding for a multivariate gaussian over a gaussian mac using variational domain adaptation,” IEEE Transactions on Cognitive Communications and Networking , 2023
2023
Closest in time.
2023
Closest in time.
F.-A. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah, “Diffusion models in vision: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
D. Baidoo-Anu and L. O. Ansah, “Education in the era of generative artificial intelligence (ai): Understanding the potential benefits of chatgpt in promoting teaching and learning,” Journal of AI , vol. 7, no. 1, pp. 52–62, 2023
2023
Closest in time.
H. X. Qin and P. Hui, “Empowering the metaverse with generative ai: Survey and future directions,” in 2023 IEEE 43rd International Conference on Distributed Computing Systems Workshops (ICDCSW) . IEEE, 2023, pp. 85–90
2023
Closest in time.
A. Karapantelakis, P. Alizadeh, A. Alabassi, K. Dey, and A. Nikou, “Generative ai in mobile networks: a survey,” Annals of Telecommunications , pp. 1–19, 2023
2023
Closest in time.
H. Sharma and N. Kumar, “Deep learning based physical layer security for terrestrial communications in 5g and beyond networks: A survey,” Physical Communication , p. 102002, 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
S. Lee, Y.-I. Yoon, and Y. J. Jung, “Generative adversarial network-based signal inpainting for automatic modulation classification,” IEEE Access , 2023
2023
Closest in time.
C. Zou, F. Yang, J. Song, and Z. Han, “Underwater wireless optical communication with one-bit quantization: A hybrid autoencoder and generative adversarial network approach,” IEEE Transactions on Wireless Communications , 2023
2023
Closest in time.
2023
Closest in time.
Q. Zhang, H. Dong, and J. Zhao, “Channel estimation for high-speed railway wireless communications: A generative adversarial network approach,” Electronics , vol. 12, no. 7, p. 1752, 2023
2023
Closest in time.
2023
Closest in time.
Y. Li and J. Chen, “Uplink channel estimation for intelligent reflecting surface aided wireless communication systems with condition gan,” in 2023 5th International Conference on Electronic Engineering and Informatics (EEI) . IEEE, 2023, pp. 328–333
2023
Closest in time.
F. Naeem, M. Qaraqe, and H. Celebi, “Joint deployment design and phase-shift of irs-assisted 6g networks: An experience-driven approach,” IEEE Internet of Things Journal , 2023
2023
Closest in time.
E. Erdemir, T.-Y. Tung, P. L. Dragotti, and D. Gündüz, “Generative joint source-channel coding for semantic image transmission,” IEEE Journal on Selected Areas in Communications , 2023
2023
Closest in time.
2023
Closest in time.
D. Ye, X. Wang, and X. Chen, “Lightweight generative joint source-channel coding for semantic image transmission with compressed conditional gans,” in 2023 IEEE/CIC International Conference on Communications in China (ICCC Workshops) . IEEE, 2023, pp. 1–6
2023
Closest in time.
S. Zhang, A. Wijesinghe, and Z. Ding, “Rme-gan: A learning framework for radio map estimation based on conditional generative adversarial network,” IEEE Internet of Things Journal , 2023
2023
Closest in time.
L. Xu, L. Feng, and W. Li, “Ctgan-assisted cnn for high-resolution wireless channel delay estimation,” in 2023 IEEE 24th International Conference on High Performance Switching and Routing (HPSR) . IEEE, 2023, pp. 1–8
2023
Closest in time.