Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures

Abstract

Deep learning (DL) has proven its unprecedented success in diverse fields such as computer vision, natural language processing, and speech recognition by its strong representation ability and ease of computation. As we move forward to a thoroughly intelligent society with 6G wireless networks, new applications and use-cases have been emerging with stringent requirements for next-generation wireless communications. Therefore, recent studies have focused on the potential of DL approaches in satisfying these rigorous needs and overcoming the deficiencies of existing model-based techniques. The main objective of this article is to unveil the state-of-the-art advancements in the field of DL-based physical layer (PHY) methods to pave the way for fascinating applications of 6G. In particular, we have focused our attention on four promising PHY concepts foreseen to dominate next-generation communications, namely massive multiple-input multiple-output (MIMO) systems, sophisticated multi-carrier (MC) waveform designs, reconfigurable intelligent surface (RIS)-empowered communications, and PHY security. We examine up-to-date developments in DL-based techniques, provide comparisons with state-of-the-art methods, and introduce a comprehensive guide for future directions. We also present an overview of the underlying concepts of DL, along with the theoretical background of well-known DL techniques. Furthermore, this article provides programming examples for a number of DL techniques and the implementation of a DL-based MIMO by sharing user-friendly code snippets, which might be useful for interested readers.

Evolution of Wireless Communication to 6G: Potential Applications and Research Directions

“…[10] Energy, IoT, and ML in 6G. [11] Digital twins for wireless systems [12] Quantum search algorithms for wireless communications [13] Advancements in a DL-based physical layer (PHY) of 6G. [14] Wireless evolution toward 6G networks and related potential technologies.…”

“…Amidst such a scenario, many candidate technologies, including the terahertz (THz) regime and other technologies powered by AI, have been discussed in [3,16,[18][19][20][21]. There are a wide range of research studies and initiatives on the recent advances in wireless communication systems, future 6G vision with its candidate-enabling technologies, and use cases, including AI/ML, THz communication, edge intelligence, blockchain, molecular communication, V2X, IoE, UAVs, HT, XR [1,10,13,16,17,[22][23][24][25][26][27][28][29][30][31][32][33][34][35]. However, while the 6G experience is expected in a few years, the new impending challenges of meeting the key performance indicators of 6G necessitate extensive research initiatives.…”

GAN-Based Evasion Attack in Filtered Multicarrier Waveforms Systems

“…More precisely, the authors used the I/Q components of quadrature phase shift keying (QPSK) signals propagating through Rayleigh channels to train a GAN composed of dense layers in both the generator and discriminator. To evaluate the performance of their approach, the authors rely on a deep neural network (DNN) classifier designed to distinguish between signals from an intended transmitter, and unintended one [24]. The GAN based attack success probability is much higher than that of an attack using random or replayed signals [25].…”

Channel Estimation for UAV Communication Systems Using Deep Neural Networks

“…Most model-driven DNN solutions fall into one of two categories: deep unfolded networks, in which DNN layers reproduce rounds of an existing iterative process, or hybrid networks, in which DNNs assist conventional models and boost efficiency . The data scarcity problem has also been eliminated alongside the advancement of DL literature on communication technologies, making room for data-based DL systems [10]. Nevertheless, the promising enhancements of DL techniques for channel modeling have motivated researchers to investigate utilizing different learning methods and feature extraction approaches in the context of cellularconnected UAV communication systems.…”

“…In [11], a modeling framework for wave propagation in mobile communications is proposed by combining several learners in an ensemble learning method for RSS modeling. Furthermore, a deep reinforcement learning (DRL) -based model for channel and power assignment is developed in [10] for UAV-enabled IoT systems, where a single UAV-base station is deployed to collect data from multiple IoT nodes. In [13], a learning algorithm is used to predict channel characteristics between UAVs and ground users, which provides accurate environmental status information for UAV deployment decisions.…”