This project focuses on the development of a highly versatile spectrum sharing antenna that enables precise directional control of the signal. The antenna can rapidly change direction (azimuth and elevation) and dynamically lock onto specific satellites or ground stations, effectively managing the shared frequency band. With a gain of 17 dBi and operation in the 400-470 MHz (Tx/Rx) range, it is suitable for a variety of applications. It will be designed as low-profile, with a tracking rate of 300°/s, RHCP/LHCP polarization and coverage up to 70 degrees from the zenith. A phase controller and an Ethernet driver will be integrated.

With the advent of AI technologies, new opportunities for optimizing spectrum sharing processes are emerging in the radio communications environment. The main objective of AI is to create more flexible management and access to frequency bands for different categories of users, for different levels of channel utilization directly at the satellite end. The AI deployed at the edge focuses on the recognition of radio signal modulations (e.g. QPSK, QAM or OFDM) and their packet protocols. Data simulated using GNU Radio Companion and other publicly available datasets are used to train the AI. The next step is to predict the available or congested frequency bands on a given satellite for specific time regions.

The project focuses on the development of a small phased array terrestrial antenna for satellite communications using electronic beam routing via digital beamforming for the UHF bands (395-405 MHz and 430-440 MHz). This design bypasses mechanical routing and allows advanced features such as multiple beam operation and interference cancellation. The antenna array will be scalable to higher frequencies and will consist of 9-16 dually circularly polarized elements. Digital beamforming algorithms will be implemented using commercially available SDR modules to ensure phase coherence. The system will be tested through simulations, experiments and field trials under realistic conditions.

The Spacemanic Ground Station System (SMGS) is a software solution for automating CubeSat operations via a VCOM interface. It predicts satellite positions, controls the antenna rotator, corrects Doppler shifts and updates TLE data. The proposed communication management software solution will introduce an advanced scheduling algorithm for efficient coordination between ground stations and frequency sharing satellites. This system integrates seamlessly with the existing SMGS, schedules distinct communication slots and reduces packet loss. The control software will leverage cloud-based solutions to ensure reliability and will have a user-friendly web interface to monitor satellite status and adjust schedules, improving the overall user experience.

The RF system will have a one metre diameter aluminium reflector with a frequency limit of 10.5 GHz, which will be analysed for power up to 12.5 GHz. A low-bandwidth feed antenna, optimized for offset reflectors, will be characterized in an anechoic chamber. Groundcom will design a simplified antenna rotator that will be significantly cheaper than the current version and will be able to carry a load of 20-25 kg. This new rotator will incorporate advanced control software for automatic monitoring and calibration. In addition, an open-source software solution for conical scanning (conscan) will be developed to optimise satellite tracking, using the geostationary satellite Es'hailsat 2 as a reference point for calibration.

The project focuses on the design of a cognitive radio based on GNU radio blocks (using existing and developing new blocks within the project) and on a commercially available software defined radio such as the USRP B210 or the RFSoC FPGA development board. The demonstration device will be tested together with a 5G base station based on openRAN, simulating the case of 5G networks in non-ground based systems. The secondary system will use spectrum information and known primary systems to detect free spectrum, fair access and transmission scheduling. The signal arrival angle information will improve the accuracy of spectrum decision making.