## 5 Beyond 5G
In [79], a method using random beamforming and maximum likelihood estimation to calculate the AoA and AoD of the line-of-sight path between BS/AP (or IRSs) and the mobile terminal is shown, which then, with the estimated AoDs, proposes an iterative positioning algorithm that achieves centimeter-level positioning accuracy. In [80], a systematic overview of existing works is presented on IRS/RIS, mainly from the signal processing point of view, by focusing on channel estimation, transmission design, and radio localization issues. Similarly, edge intelligence--a novel technological framework focusing on the seamless integration of AI, communication networks, and mobile edge computing--is widely recognized to be one of the most sought after functions for wireless 6G cellular systems [81]. It conventionally consists of sensing, communication, training, and inference stages, where sensing and communication are executed sequentially, often leading to an excessive amount of dataset generation and uploading time [82]; however, novel solutions, such as integrated sensing and communication (ISAC) introduced in [73; 82], merge the sensing and communication stages in order to make the best use of the wireless signals for the dual purpose of dataset generation and uploading. In addition, to address the additional interference between sensing and communication functionalities that ISAC introduces, this paper also proposes a classification error minimization formulation to design the ISAC beamforming and time allocation [82]. Recently, increasing attention has been paid to device-free localization [83; 84; 85], i.e., the ability to detect and track objects that do not communicate with the localization infrastructure or do not want to be detected and localized at all. These technologies rely on signals designed for target detection and localization (active radar) or signals emitted by other sources of opportunity (passive radar) that are used for localization. Unlike UE localization, device-free localization can use any modulated signal at any operating frequency. As the wireless industry moves toward frequencies above 90 GHz (and eventually Terahertz frequencies) in the future, several Gigahertz wide frequency ranges will become available. Difficult indoor conditions (e.g., multipath effects and signal obstructions) can be mitigated by using waveforms characterized by this wide bandwidth, e.g., UWB waves, using prior knowledge of the environment, selecting reliable measurements, and employing various signal processing techniques [83; 85]. UWB technology provides exceptional resolution and localization accuracy in harsh environments because of its ability to resolve multipath effects and penetrate obstacles. These characteristics have helped make UWB an ideal candidate for non-collaborative object detection in short-range radar sensor networks. Device-free localization could be used for fall detection in assisted living facilities. Security applications such as intruder detection in offices are enabled by device-free localization without the knowledge or cooperation of the intruder [85]. Another key technology prominent for 6G network applications such as intelligent transportation systems is data-driven wireless sensing, such as Human Motion Recognition (HMR). HMR systems currently use Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) to classify radar signals, but new solutions such as the Deep Spectrogram Network (DSN) introduced in [86] are paving the way for the technology with a significant reductions of recognition errors. Clearly, numerous challenges await on the road to 6G, particularly tied to the evolution and integration of machine learning and artificial intelligence, which will be expected from networks beyond 5G. The development of intuitive AI solutions without excessive storage needs and their integration into 6G networks without compromising low latency will be one of the key challenges while building the network of the future.