{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:46:05Z","timestamp":1760150765856,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:00:00Z","timestamp":1702598400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Department of Defence, Commonwealth of Australia","award":["10254"],"award-info":[{"award-number":["10254"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Wireless communications systems are traditionally designed by independently optimising signal processing functions based on a mathematical model. Deep learning-enabled communications have demonstrated end-to-end design by jointly optimising all components with respect to the communications environment. In the end-to-end approach, an assumed channel model is necessary to support training of the transmitter and receiver. This limitation has motivated recent work on over-the-air training to explore disjoint training for the transmitter and receiver without an assumed channel. These methods approximate the channel through a generative adversarial model or perform gradient approximation through reinforcement learning or similar methods. However, the generative adversarial model adds complexity by requiring an additional discriminator during training, while reinforcement learning methods require multiple forward passes to approximate the gradient and are sensitive to high variance in the error signal. A third, collaborative agent-based approach relies on an echo protocol to conduct training without channel assumptions. However, the coordination between agents increases the complexity and channel usage during training. In this article, we propose a simpler approach for disjoint training in which a local receiver model approximates the remote receiver model and is used to train the local transmitter. This simplified approach performs well under several different channel conditions, has equivalent performance to end-to-end training, and is well suited to adaptation to changing channel environments.<\/jats:p>","DOI":"10.3390\/s23249848","type":"journal-article","created":{"date-parts":[[2023,12,18]],"date-time":"2023-12-18T11:28:07Z","timestamp":1702898887000},"page":"9848","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Channel-Agnostic Training of Transmitter and Receiver for Wireless Communications"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4742-9635","authenticated-orcid":false,"given":"Christopher P.","family":"Davey","sequence":"first","affiliation":[{"name":"School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5512-8843","authenticated-orcid":false,"given":"Ismail","family":"Shakeel","sequence":"additional","affiliation":[{"name":"Spectrum Warfare Branch, Information Sciences Division, Defence Science and Technology Group (DSTG), Edinburgh, SA 5111, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2290-6749","authenticated-orcid":false,"given":"Ravinesh C.","family":"Deo","sequence":"additional","affiliation":[{"name":"School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4048-1676","authenticated-orcid":false,"given":"Sancho","family":"Salcedo-Sanz","sequence":"additional","affiliation":[{"name":"School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, QLD 4300, Australia"},{"name":"Department of Signal Processing and Communications, Universidad de Alcal\u00e1, Alcal\u00e1 de Henares, 28805 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1109\/TCCN.2017.2758370","article-title":"An Introduction to Deep Learning for the Physical Layer","volume":"3","author":"Hoydis","year":"2017","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"110176","DOI":"10.1016\/j.asoc.2023.110176","article-title":"A Comprehensive Survey on Design and Application of Autoencoder in Deep Learning","volume":"138","author":"Li","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1109\/JSTSP.2017.2784180","article-title":"Deep Learning Based Communication Over the Air","volume":"12","author":"Cammerer","year":"2018","journal-title":"IEEE J. Sel. Top. Signal Process."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Stark, M., Aoudia, F.A., and Hoydis, J. (2019, January 9\u201313). Joint Learning of Geometric and Probabilistic Constellation Shaping. Proceedings of the 2019 IEEE Globecom Workshops (GC Wkshps), Waikoloa, HI, USA.","DOI":"10.1109\/GCWkshps45667.2019.9024567"},{"key":"ref_5","unstructured":"Proakis, J.G., and Salehi, M. (2008). Digital Communications, McGraw-Hill. [5th ed.]."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1109\/LWC.2017.2757490","article-title":"Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems","volume":"7","author":"Ye","year":"2018","journal-title":"IEEE Wirel. Commun. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1109\/OJCOMS.2020.2982513","article-title":"Deep Learning for Fading Channel Prediction","volume":"1","author":"Jiang","year":"2020","journal-title":"IEEE Open J. Commun. Soc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MCOM.2019.1800635","article-title":"Generative-Adversarial-Network-Based Wireless Channel Modeling: Challenges and Opportunities","volume":"57","author":"Yang","year":"2019","journal-title":"IEEE Commun. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"O\u2019Shea, T.J., Roy, T., West, N., and Hilburn, B.C. (2018, January 3\u20137). Physical Layer Communications System Design Over-the-Air Using Adversarial Networks. Proceedings of the 2018 26th European Signal Processing Conference (EUSIPCO), Rome, Italy.","DOI":"10.23919\/EUSIPCO.2018.8553233"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"O\u2019Shea, T.J., Roy, T., and West, N. (2019, January 18\u201321). Approximating the Void: Learning Stochastic Channel Models from Observation with Variational Generative Adversarial Networks. Proceedings of the 2019 International Conference on Computing, Networking and Communications (ICNC), Honolulu, HI, USA.","DOI":"10.1109\/ICCNC.2019.8685573"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ye, H., Li, G.Y., Juang, B.F., and Sivanesan, K. (2018, January 9\u201313). Channel Agnostic End-to-End Learning Based Communication Systems with Conditional GAN. Proceedings of the 2018 IEEE Globecom Workshops (GC Wkshps), Abu Dhabi, United Arab Emirates.","DOI":"10.1109\/GLOCOMW.2018.8644250"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3133","DOI":"10.1109\/TWC.2020.2970707","article-title":"Deep Learning-Based End-to-End Wireless Communication Systems With Conditional GANs as Unknown Channels","volume":"19","author":"Ye","year":"2020","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/LCOMM.2018.2868103","article-title":"Backpropagating Through the Air: Deep Learning at Physical Layer Without Channel Models","volume":"22","author":"Raj","year":"2018","journal-title":"IEEE Commun. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Aoudia, F.A., and Hoydis, J. (2018, January 28\u201331). End-to-End Learning of Communications Systems Without a Channel Model. Proceedings of the 2018 52nd Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/ACSSC.2018.8645416"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2503","DOI":"10.1109\/JSAC.2019.2933891","article-title":"Model-free training of end-to-end communication systems","volume":"37","author":"Aoudia","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5489","DOI":"10.1109\/TCOMM.2020.3002915","article-title":"Trainable Communication Systems: Concepts and Prototype","volume":"68","author":"Cammerer","year":"2020","journal-title":"IEEE Trans. Commun."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Sahai, A., Sanz, J., Subramanian, V., Tran, C., and Vodrahalli, K. (2019, January 24\u201327). Learning to Communicate with Limited Co-design. Proceedings of the 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA.","DOI":"10.1109\/ALLERTON.2019.8919749"},{"key":"ref_18","unstructured":"Goodfellow, I. (2016). Nips 2016 tutorial: Generative adversarial networks. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_20","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_21","unstructured":"Mirza, M., and Osindero, S. (2014). Conditional generative adversarial nets. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"D\u00f6rner, S., Henninger, M., Cammerer, S., and Brink, S.t. (2020, January 26\u201329). WGAN-based Autoencoder Training Over-the-air. Proceedings of the 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Online.","DOI":"10.1109\/SPAWC48557.2020.9154335"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/BF00992696","article-title":"Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning","volume":"8","author":"Williams","year":"1992","journal-title":"Mach. Learn."},{"key":"ref_24","unstructured":"Sutton, R.S., McAllester, D., Singh, S., and Mansour, Y. (1999). Policy gradient methods for reinforcement learning with function approximation. Adv. Neural Inf. Process. Syst., 12."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, B., and Van Huynh, N. (2023). Deep Deterministic Policy Gradient for End-to-End Communication Systems without Prior Channel Knowledge. arXiv.","DOI":"10.1109\/GLOBECOM54140.2023.10436824"},{"key":"ref_26","unstructured":"de Vrieze, C., Barratt, S., Tsai, D., and Sahai, A. (2018). Cooperative multi-agent reinforcement learning for low-level wireless communication. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1145\/3446776","article-title":"Understanding deep learning (still) requires rethinking generalization","volume":"64","author":"Zhang","year":"2021","journal-title":"Commun. ACM"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xie, L., Wang, J., Wei, Z., Wang, M., and Tian, Q. (2016, January 27\u201330). Disturblabel: Regularizing cnn on the loss layer. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.514"},{"key":"ref_29","unstructured":"Glorot, X., Bordes, A., and Bengio, Y. (2011, January 11\u201313). Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics. JMLR Workshop and Conference Proceedings, Ft. Lauderdale, FL, USA."},{"key":"ref_30","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_32","first-page":"550","article-title":"Residual networks behave like ensembles of relatively shallow networks","volume":"29","author":"Veit","year":"2016","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_33","first-page":"448","article-title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","volume":"Volume 37","author":"Bach","year":"2015","journal-title":"Proceedings of the 32nd International Conference on Machine Learning, Lille, France, 6\u201311 July 2015"},{"key":"ref_34","unstructured":"Ramachandran, P., Zoph, B., and Le, Q.V. (2017). Searching for activation functions. arXiv."},{"key":"ref_35","unstructured":"Izmailov, P., Podoprikhin, D., Garipov, T., Vetrov, D., and Wilson, A.G. (2018). Averaging weights leads to wider optima and better generalization. arXiv."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Smith, L.N. (2017, January 24\u201331). Cyclical Learning Rates for Training Neural Networks. Proceedings of the 2017 IEEE Winter Conference on Applications of Computer Vision (WACV), Santa Rosa, CA, USA.","DOI":"10.1109\/WACV.2017.58"},{"key":"ref_37","first-page":"179","article-title":"Effects of HPA-nonlinearity on a 4-DPSK\/OFDM-signal for a digital sound broadcasting signal","volume":"332","author":"Rapp","year":"1991","journal-title":"ESA Spec. Publ."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1109\/26.668727","article-title":"Adaptive coded modulation for fading channels","volume":"46","author":"Goldsmith","year":"1998","journal-title":"IEEE Trans. Commun."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9848\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:39:37Z","timestamp":1760132377000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/24\/9848"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,15]]},"references-count":38,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["s23249848"],"URL":"https:\/\/doi.org\/10.3390\/s23249848","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,12,15]]}}}