42 Focus | Radio and telemetry However, a CR requires joint optimisation with medium access control (MAC) and physical layers. For example, a UAV with a CR user senses different channels simultaneously and uses some idle ones for data transmission. The more channels the CR device can use, the more efficient its bits-per-joule ratio throughput. The bits-per-second ratio also increases with the number of channels used. This has led to the development of the UAV-assisted cognitive radio (CR) network as a promising technique to address spectrum congestion issues in swarms. However, its performance can be severely affected by the blocked LoS channel in its air-to-ground (A2G) links. Reconfigurable intelligent surfaces Using reconfigurable intelligent surfaces (RIS) can help to reconstruct reliable links in UAV-assisted CR networks. As the name implies, the RIS is a surface that can be changed to reflect radio waves in different ways, and it is usually passive. It is often built from a metamaterial with tiny structures that redirect radio waves in a similar way to a beam-steering antenna by changing the phase relation of the signal. This allows signals to be directed around buildings or other blocks, reconstructing suitable wireless propagation channel links that a CR can identify and tap into by adjusting its phase shifts to boost performance. A RIS-based resource allocation method significantly improves energy efficiency by reducing losses. The RIS can reconfigure the wireless channel link between a transmitter and a receiver through controllable intelligent signal reflection, creating a virtual LoS link to bypass any obstacles between the transceivers, which helps to improve the wireless link performance. The RIS can be deployed in UAVassisted CR networks once the A2G LoS links have been blocked, and it allows mobile CR users to establish temporary or emergency communications via the cascaded channel links reconstructed by the RIS. Cooperative spectrum sensing Building on CR, cooperative spectrum sensing (CSS) involves multiple users reporting the channel data to a fusion centre to improve the network links. The high computing overheads of this approach use a lot of power, however, limiting its use in UAVs. Instead, a virtual CSS uses a single UAV to conduct CSS while following a circular flight trajectory in the air and acting as the fusion centre. This uses sensing and data-transmission periods that are further divided into mini-sensing slots. In the virtual CSS, UAV performs local sensing decisions in each mini-slot and accumulates them for a final decision. The proposed virtual CSS scheme exploits sequential decision fusion (SDF), which sequentially adds individual mini-slot decisions coupled with ML to inspect flight conditions and reconfigure mini-slot periods dynamically. The technique adapts to varying levels of UAV flight velocity, moving radius, detectionprobability demand and channel SNR. There are also opportunities to optimise network performance on the ground. For UAVs using 5G cellular networks, a new AI model could reduce the network resources required by 76%, compared with Open Radio Access Network (O-RAN) systems, and use less energy. This technique mathematically models the network, using AI to find the best way of allocating computing power across it with only a small additional February/March 2024 | Uncrewed Systems Technology Using Reconfigurable Intelligent Surfaces to redirect signals (Image courtesy of Zhejiang Sci-Tech University) A prototype of the PhantomBlu mmWave radio (Image courtesy of Blue Wireless Technologies)
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