## 3 Emerging and State-of-the-Art Technologies
### On Digital Twins and Communications  
As discussed in Section 1, next-generation communication systems will form an interconnected ecosystem, linking everything to everything, everyone to everything, and everyone to everyone. A key component of this ecosystem will rely on emerging technologies, particularly AI and ML. These technologies will be crucial in developing a cyber space that mirrors the physical space (e.g., user equipment, core networks, base stations, point-to-point communications, etc. ), with the goal of predicting behavior, simplifying the management and orchestration of complex network infrastructures (e.g., [216]), and optimizing performance in real-time. This cyber space will operate as a closed-loop system capable of (1) collecting real-time data from various applications (e.g., vehicular, terrestrial, and satellite networks), involving any technological components connected to the Internet or future networks, (2) diagnosing system performance, (3) identifying deficiencies, (4) predicting failures, and (5) preventing negative outcomes. This cyber space is what has been referred to as a digital twin (DT), and it is a system that will enable the optimal performance of next-generation communication systems. Although DTs are not traditionally part of the physical layer, they depend on continuous, real-time interaction with physical systems, making them a natural extension of it. The authors consider that their inclusion in this paper is essential, as they represent an application domain that directly influences the development and optimization of physical layer technologies. Essentially, a bi-directional communication link will be established between the digital twin and the physical space, where the events of the physical space are captured in near-real-time, and the tasks performed within the cyber space, such as modeling, learning, data analytics, prediction, and simulation, provide feedback to the physical space [217; 218]. To the authors' understanding, DT technology is expected to impact multiple disciplines within the broader framework of next-generation communication systems. By creating real-time, data-driven virtual replicas of physical systems, DTs will enable optimization, predictive analysis, and autonomous decision making across various domains. One prominent application of DTs is in satellite communications, particularly with the rapid expansion of low Earth orbit (LEO) satellite constellations. Companies such as Starlink, Kuiper, and OneWeb have already launched, or plan to launch, thousands of satellites to provide global connectivity. As of today, Starlink operates over 3000 satellites, serving more than 400,000 customers worldwide [219]. To optimize satellite-to-satellite and satellite-to-Earth/user communication links, highly efficient computational models are required. A DT framework, leveraging learning-by-example algorithms [220], can dynamically predict and optimize system performance. For instance, in Starlink's Ku-band operation, satellite beam steering is limited to 40-degree scan angles with an EIRP of 5 dBW/MHz [221]. Moreover, with an average line-of-sight connection of only 10 min per satellite, users must continuously switch between satellites for uninterrupted connectivity. A DT model that maps the entire communication ecosystem, incorporating satellite availability, frequency bands, modulation schemes, and antenna polarization, is expected to be able to provide real-time recommendations to user terminals, ensuring optimal connectivity. Furthermore, DTs will be critical in mitigating latency challenges in space communications. The one-way propagation delay between a user and a satellite varies from 1 ms to 140 ms, depending on the satellite's altitude [222]. It is these authors' intuition that a DT engine will help minimize this delay by optimizing packet error rates and maximizing data burst efficiency. DTs will also play a transformative role in vehicular networks, particularly with the rapid deployment of autonomous vehicles (AVs).