Conditional Generative Adversarial Networks for Rx-to-Tx Translation in Wireless Communication Systems

Wireless communication systems rely on channel estimation and equalization to ensure reliable and efficient data transmission. However, with the increasing demand for high connectivity in massive IoT networks, these processes are facing significant challenges. The complexity and intensive computation required for channel estimation and equalization results in high communication latency and power consumption, which can ultimately prevent the transceiver from restoring the originally transmitted signal. In this paper, we propose a novel approach to simplify wireless communication systems by using a conditional generative adversarial network (cGAN) model to replace both channel estimation and equalization blocks. We formulate the data recovery task as a translation from received data to the corresponding transmitted signal and introduce the concept of Rx-to-Tx translation based on a cGAN, which was initially developed for image-to-image translation. Our preliminary results demonstrate the feasibility and effectiveness of this approach, particularly for digital modulations. By carefully tuning the model's hyperparameters, we achieve the theoretical symbol error rate (SER) of QAMs in a Rayleigh propagation channel. Our proposed approach has the potential to significantly reduce the computational complexity and overhead typically associated with traditional channel estimation and equalization blocks. This can lead to more efficient and cost-effective wireless communication systems.