40 Focus | Radio and telemetry polarised antennas. High-volume spiral antennas have been developed with two-arm spiral versions, while custommade, four-arm spiral versions provide higher sensitivity. The spirals have a compact, lightweight design, formed by placing the spiral on top of a cylindrical absorber-filled cavity, and they can be provided with a protective, lensed radome covering if required. They can be used as single elements, in arrays or as feeds in reflectors. AI for radio Another new field of ML is federated learning (FL), which was proposed by Google in 2016 and is designed to support network systems with decentralised data. It aims to train a highly centralised model on devices sharing decentralised data without the need for this data to be sent to a local shared unit, essentially running ML algorithms with decentralised data architecture. This is of particular interest for UAV swarms and their radio connections through a combination of mobile edge computing (MEC) and non-orthogonal multiple access (NOMA). In these UAV-NOMA-MEC systems, UAVs are equipped with computing capabilities, and they can be swiftly deployed for situations where terrestrial MEC servers are overloaded or unavailable. Operational control of UAVs often relies on wireless communication, which introduces difficult challenges for spectrum allocation and interference cancellation. Developments such as fog computing use these techniques to achieve higher capacity than conventional communication networks by jointly optimising user association, power control, the allocation of computing resources and location planning. In AI algorithms, the network parameters need to be shared with other intelligent agents or network models. For AI-enabled UAV-NOMA-MEC, the transmission of network parameters in AI algorithms and wireless signals need to be jointly optimised, so this is leading to the development of a unified design for AI and wireless transmission systems. AI is also used to improve the control of a swarm of UAVs to optimise the radio links. The proposed solution supports the swarm of UAVs during their flight by maintaining their topology in an energy efficient manner. This uses the principles of a mathematical model called the Reynolds’ Boids model, which looks at the alignment, separation and cohesion of the swarm. The effectiveness of this approach has been successfully tested in simulation with AI frameworks to improve reliability, low latency, handover and path planning. Cognitive radio The European Telecommunications Standards Institute (ETSI) defines CR as a radio that can sense and understand the radio environment and policies, and monitor usage patterns and users’ needs; autonomously and dynamically adapting according to the radio environment, so that it can achieve predefined objectives, such as the efficient utilisation of spectrum; learning from the environment and results of its own actions, so it can improve its performance. Essentially, it is an intelligent SDR, using spectrum sensing to identify the available spectrum and detect sources when operating in a licensed band. Spectrum management elects the best available channel, coordinates accessibility to the available channel with other users, and vacates the channel when a primary user arrives. Although CR devices might represent a computational overload for UAVs, they could be capable of reducing energy consumption. Because UAVs operate in overcrowded spectrum bands, they are susceptible to a high number of lost data packets, increasing the need for packets to be retransmitted using more energy. As a result, the energy consumed for packet retransmission may be greatly reduced when a UAV is equipped with a CR. February/March 2024 | Uncrewed Systems Technology A radio for digital beam steering (Image courtesy of Radionor)
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