## 1 Introduction  
In today's world, mobile devices and applications are omnipresent, so much so that they have reshaped our society, especially the way people work, have fun, or even communicate. This change took the world by storm, increasing the requirements for more data traffic and more connected devices. By 2030, the latter are expected to reach 17.1 billion and the required data traffic may exceed 4300 Exabytes/month [1]. These numbers are significantly larger than the current capacities of our networks. Current technologies, such as massive Multiple-Input Multiple-Output (MIMO), Ultra-Dense Network (UDN), and millimeter Wave (mmWave) [2], are not enough to satisfy the requirements and novel approaches; thus, the employment of unconventional techniques may be needed. This leads to a next generation of wireless networks that will be able to address all new challenges and offer the necessary resources that will bring the future of telecommunications to the envisioned level of novelty. The advent of 6G wireless networks represents a paradigm shift in telecommunications, characterized by the unprecedented integration of AI and advanced radio technologies. This evolution is not merely an incremental improvement from 5G but also a revolutionary leap towards intelligent, adaptive, and highly efficient communication systems. The synergy between AI and wireless communications in 6G is expected to enable novel applications such as holographic communications, tactile internet, and brain-computer interfaces, pushing the boundaries of what is possible in digital connectivity. The discussion on future wireless networks (such as 6G) has already begun since 2018, even before the full implementation of 5G [3]. The next generation of wireless communications aims to unite the physical, digital, and biological worlds [4]. This will result in increasing Internet of Everything (IoE)-connected devices, from sensors and wearables to discreet devices such as phones and tablets. The application test-cases requirements for these devices alone set a new milestone in wireless communications. Moreover, a different network infrastructure is required, i.e., to unite all communications, from terrestrial to satellite, into a single entity. The nature of communications will also drastically change because a large number of these connected devices require Ultra Reliable Low Latency Connections (URLLCs) since the quality of the connection is critical for the applications. To accommodate these changes, new frequency bands are required ranging from above 100 GHz to 300 GHz [5]. These frequencies allow for even smaller devices and require less energy to operate. However, they come with a significant caveat, i.e., the signal degradation is significantly higher. At these wavelengths electromagnetic (EM) waves suffer from high penetration and propagation losses, as well as higher absorption rates and shifts. This, along with the fact that the number of connected devices is expected to be vast, prompted researchers to consider using the radio environment to their advantage. Until recently, the radio environment was considered a black box that could not change. In wireless communications, designers adopt two distinct approaches: (a) [PERSON]'s approach, according to which the model is static and can be formulated in terms of probabilities, requiring a proper initial planning [6], and (b) [PERSON]'s approach where the model is still the same, but the input is optimized according to feedback from the system's output [7]. Some notable attempts have been made to use the nature of the radio environment as an advantage. Index modulation technologies, such as spatial modulation, scattering media modulation and media-based modulation, have been employed, albeit in all the aforementioned technologies the radio environment is used as is, with these methods just exploiting its characteristics [8; 9; 10]. One of the suggested solutions is more groundbreaking than attempting to solely leverage the characteristics of the propagation environment.