 extended frequency ranges starting at the mm-wave bands are considerable, which in general will require line-of- sight operating conditions. Wireless channel characterization is thus required in order to provide adequate insight in relation to frequency/power characteristics, such as received power level distributions as well as with time-domain-related characteristics, such as Doppler spectrum and power delay profiles. Multiple approaches can be employed, depending on the characteristics of the environment (i.e., outdoor/indoor, vehicular, industrial, human body presence), system function- alities under analysis, or required accuracy, going from empirical/statistical model to full-wave electromagnetic simulation or geometric/stochastic techniques based on ray tracing/ray launching models [9-20]. Wireless channel characterization can also provide additional information related to interference sources, models, and distribution, which have a direct impact on the coverage/capacity thresholds. Models, such as BG5CM, have been recently proposed in order to provide high-level wireless channel characterization, generalizable from mm-wave to THz frequency bands [21], as well as more specific models for application in scenarios related to IoT applications [22]. If the focus is placed in IoT communications, integrated sensing, computation, and communication capabilities are provided in order to enable massive IoT connectivity. This can be achieved by following multiple joint approaches, such as the use of non-orthogonal wireless communications at the sensing level, novel computational schemes to reduce latency, and advanced interference reduction mechanisms [23]. Enhancements have also been proposed in relation to network slicing operation, by means of federated learning approaches in order to minimize I. Picallo et al. raw data exchange while preserving network slice service-level agreement quality of service conditions [24]. Energy consumption reduction and control is another relevant aspect in the design and implementa