The field of wireless communication and signal processing is witnessing significant advancements, driven by the application of machine learning and deep learning techniques. Researchers are exploring new approaches to optimize signal design, mitigate mode mismatch, and improve channel estimation. The use of variational autoencoders, conditional generative models, and transformer-based architectures is becoming increasingly prevalent. These innovations are enabling the development of more efficient and reliable wireless communication systems, with potential applications in fields such as 5G and 6G networks, internet of things, and autonomous vehicles. Notable papers in this area include:

• A paper proposing a design for low peak-to-average power ratio and low symbol error rate signals for ACO-OFDM systems using a variational autoencoder, which achieves a considerably lower PAPR while maintaining superior SER and MI performance.
• A paper introducing a deep conditional generative approach for channel fingerprint construction, which exhibits significant improvement in reconstruction performance compared to baselines.