SUNRISE

The ever-increasing demand for ubiquitous wireless communication services with high data rate, low latency, and high reliability is considered unsustainable from an environmental perspective. In particular, the emerging high-rate services in the millimetre-wave (mmWave) range require a large number of base stations to cover larger areas or urban environments, which leads to a significant increase in the consumption of energy and resources. Several ideas exist to improve these networks without the need for additional base stations; in particular, reconfigurable intelligent surfaces (RIS) have recently been proposed as sustainable alternatives for many application scenarios. The main idea is to intelligently reflect otherwise unused and wasted signals back onto a path to the user, to improve overall coverage and throughput. However, such RISs typically require a large number of actively tunable components to dynamically redirect the reflections towards the users. The associated energy consumption prevents sustainability from being ensured in this way. This project builds on the idea of enhancing the wireless links via smart reconfigurable surfaces, but it adds an important key ingredient to actually achieve sustainability, while providing the needed improvement for wireless service coverage: sparsity. Sparse placement of tunable components on these reconfigurable surfaces, then referred to as sparse reconfigurable intelligent surfaces (SRISs), results in significant energy efficiency improvement. Beyond that, employing sparsity also in the deployment of these SRISs, further improves sustainability, by minimizing both manufacturing costs and used resources as well as overall energy consumption. Key components of the projects include the development of physics-based models of SRIS and a holistic design methodology based on combining ideas from metasurface design and sparsification, channel modelling via efficiency-enhancing electromagnetic simulation techniques, and optimisation of resource allocation and deployment (via modern convex optimisation and relaxation as well as artificial intelligence (AI)-based methods) to maximize SRIS improvement. Prototypes and measurements will prove the working concepts. This project will pave the way toward future high-performance wireless networks that are energy-efficient and sustainable by design.