Next-generation communication networks are expected to exploit recent advances in data science and cutting-edge communications technologies to improve the utilization of the available communications resources. In this article, we introduce an emerging deep learning (DL) architecture, the transformer-masked autoencoder (TMAE), and discuss its potential in next-generation wireless networks. We discuss the limitations of current DL techniques in meeting the requirements of 5G and beyond 5G networks, and how the TMAE differs from the classical DL techniques can potentially address several wireless communication problems. We highlight various areas in next-generation mobile networks which can be addressed using a TMAE, including source and channel coding, estimation, and security. Furthermore, we demonstrate a case study showing how a TMAE can improve data compression performance and complexity compared to existing schemes. Finally, we discuss key challenges and open future research directions for deploying the TMAE in intelligent next-generation mobile networks.

翻译：下一代通信网络有望利用数据科学和前沿通信技术的最新进展来提高可用通信资源的利用率。本文介绍了一种新兴的深度学习架构——Transformer掩码自编码器（TMAE），并探讨了其在下一代无线网络中的潜力。我们讨论了当前深度学习技术在满足5G及超5G网络需求方面的局限性，以及TMAE与经典深度学习技术的差异如何能够潜在地解决多个无线通信问题。我们重点阐述了TMAE可应用于下一代移动网络的多个领域，包括信源和信道编码、估计以及安全性。此外，我们通过一个案例研究展示了TMAE在数据压缩性能和复杂度方面相比现有方案的提升。最后，讨论了在智能下一代移动网络中部署TMAE的关键挑战和开放性的未来研究方向。