In the past decades, researchers/scientists have paid attention to the physical layer of underwater communications (UWCs) due to a variety of scientific, military, and civil tasks completed beneath water. This includes numerous activities critical for communication, such as survey and monitoring of oceans, rescue, and response to disasters under the sea. Till the end of the last decade, many review articles addressing the history and survey of UWC have been published which were mostly focused on underwater sensor networks (UWSN), routing protocols, and underwater optical communication (UWOC). This paper provides an overview of underwater acoustic (UWA) physical layer techniques including cyclic prefix orthogonal frequency division multiplexing (CP-OFDM), zero padding orthogonal frequency division multiplexing (ZP-OFDM), time-domain synchronization orthogonal frequency division multiplexing (TDS-OFDM), multiple input multiple output orthogonal frequency division multiplexing (MIMO-OFDM), generalized frequency division multiplexing (GFDM), unfiltered orthogonal frequency division multiplexing (UF-OFDM), continuous phase modulation orthogonal frequency division multiplexing (CPM-OFDM), filter bank multicarrier (FBMC) modulation, MIMO, spatial modulation technologies (SMTs), and orthogonal frequency division multiplexing index modulation (OFDM-IM). Additionally, this paper provides a comprehensive review of UWA channel modeling problems and challenges, such as transmission loss, propagation delay, signal-to-noise ratio (SNR) and distance, multipath effect, ambient noise effect, delay spread, Doppler effect modeling, Doppler shift estimation. Further, modern technologies of the physical layer of UWC have been discussed. This study also discusses the different modulation technology in terms of spectral efficiency, computational complexity, date rate, bit error rate (BER), and energy efficiency along with their merits and demerits.

With the rapid development of technology, the number of smart mobile users is increasing, accompanied by growing demands from applications such as virtual/augmented reality (VR/XR), remote surgery, autonomous vehicles, and real-time holographic communications, all of which require high transmission rates and ultra-low latency in 6G and beyond networks (6G). This poses enormous challenges for the efficient deployment of large-scale networks, including network design, planning, troubleshooting, optimization, and maintenance, without affecting the user experience. Network Digital Twin (NDT) has emerged as a potential solution, enabling the creation of a virtual model that reflects the actual network and supports the simulation of various network designs, the application of diverse operating policies, and the reproduction complex fault scenarios under real-world conditions. This motivates us to conduct this study, where we provide a comprehensive survey of NDT in the context of 6G, covering areas such as radio access networks (RAN), transport networks, 5G core networks and beyond (5GCORE), cloud/edge computing, applications (blockchain, health system, manufacturing, security, and vehicular networks), non-terrestrial networks (NTNs), and quantum networks, from both academic and industrial perspectives. In particular, we are the first to provide an in-depth guide and usage of RAN and 5GCORE+ for NDT. Then, we provide an extensive review of foundation technologies such as transport networks, cloud/edge computing, applications, NTNs, and quantum networks in NDT. Finally, we discuss the key challenges, open issues, and future research directions for NDT in the context of 6G.

In this paper, we investigate unmanned aerial vehicle (UAV)-aided multiuser reconfigurable intelligent surface (RIS) communication for next generation communication networks. We aim to jointly optimize the active beamforming, passive beamforming, and UAV trajectory jointly to minimize power consumption in presence of UAV jitters and imperfect hardware constraints. We decouple the formulated nonconvex problem into three subproblems. For active beamforming subproblem, we linearize and approximate the constraints using S-procedure and general sign-definitiveness technique. Then, we again apply S-procedure and convex-concave technique to handle the passive beamforming. For UAV trajectory subproblem, we apply first-Taylor expansion to transform the problem into a tractable form. On the highlights of the proposed solution, we design a hybrid semi-unfolding deep neural network (HSUDNN) to mitigate the constraints during the channel state information gain for RIS and UAV links in real-time. Using our proposed active beamforming solution and the optimality conditions, we design the unfolding-based sub neural network. Moreover, we design inception-like multi-kernel convolutional long short-term memory (IL-MK-CLSTM) sub networks to handle the UAV trajectory and passive beamforming. IL-MK-CLSTM provides spatiotemporal connection which helps in overcoming vanishing gradient problem and provides multi-step prediction. The proposed HSUDNN achieves 99.24% accuracy which demonstrates its superior performance in comparison to the existing state-of-the-art techniques in literature.

In this article, we study the secure communication in unmanned aerial vehicle (UAV) aided and reconfigurable intelligent surface (RIS) empowered multiuser networks. In the proposed configuration, we consider UAV-mounted RIS to reflect the signal between the base station and the legitimate users in the presence of eavesdroppers. The problem is formulated as secrecy rate maximization with channel uncertainty constraints, which is nonconvex. Therefore, we decouple it into beamforming, phase shift, and UAV trajectory subproblems which are jointly optimized. For the beamforming and phase shift subproblems, we apply S-procedure and general sign-definiteness to transform them into tractable forms. And for the UAV trajectory, we unroll the constraints and apply first-order Taylor transform and the difference of two convex functions (DC) to obtain a relaxed convex problem that can be numerically solved. The proposed solution show effectiveness and improved the secrecy rate of the network.

Next generation networks will involve huge number of industrial internet of things (IIoT) sensors which require reliable connectivity with low latency to manage the data transmission and processing. The design of these networks entails a lot of challenges. This article describes the 3D placement of multiple unmanned aerial vehicles (UAVs) in an IIoT network that supports non-orthogonal multiple access (NOMA). UAVs act as decode and forward (DF) relays. The 3D UAV placement problem is formulated which is highly non-convex in the coordinates. Therefore, we employ an improved adaptive whale optimization algorithm (IAWOA) to handle the problem. Even with its improved performance, IAWOA is not suitable for real-time application. Hence, we propose path aggregation network (PANet) to handle the 3D UAV placement. The simulation results show that PANet is more suitable for the online-learning.

Energy Efficiency (EE) has become an essential metric in Device-to-Device (D2D) communication underlaying Unmanned Aerial Vehicles (UAVs) Among the several technologies that provide significant energy, simultaneous wireless information and power transfer (SWIPT) has been proposed as a promising solution to improve EE. However, it is a challenging task to study the EE under nonlinear energy harvesting (EH) due to the limited sensitivity and the composition of the non-linear circuit. Moreover, when D2D users transmit information using the EH from UAVs, interferences to cellular users occur and deteriorate the throughput. To tackle these problems, we leverage concepts from artificial intelligence (AI) to optimize EE of UAV-assisted D2D communication. Specifically, multi-agent deep reinforcement learning was proposed to jointly maximize throughput and EE, where the reward function is defined in terms of the introduced goal. Simulation results verify the supremacy of proposed approach over traditional algorithms.