Important highlights of these advances beyond the state-of-the-art with respect to the D-band wireless connectivity pillar (I) include
• ARIADNE use cases and system requirements specifications,
• D-band channel measurements and characterization,
• The design and manufacturing of A/D and D/A converters board for RF front-end,
• The development and implementation of an XPIC algorithm in the baseband processing unit,
• The design and implementation of a broadband transceiver chipset at D-band.
• The error-free communication achieved over the D-band link at a distance larger than 200m,
• The development of an LC reflect-array lab demonstrator at W-band,
• The design of metasurface topologies for multi-frequency anomalous reflectors and beam splitters at D-band,
• The design of low-complexity beam-tracking algorithm for D-band wireless systems.
Furthermore, important highlights of advances beyond the state-of-the-art with respect to the Communications beyond the Shannon pillar (II) include
• capacity-achieving scheme for RIS-assisted communications
• scaling laws of RIS-assisted communications in the near-field and far-field regimes,
• analytical studies in order to model RIS as a MIMO system and/or as a radiating sheet,
• analytical assessment of the beamforming efficiency in RIS aided links,
• optimal RIS placement and orientation scheme,
• analytical evaluation of the impact of beam misalignment, blockage, rain interference, and hardware imperfections,
• prediction-based tracking algorithm proposed,
• quantitative analysis of network interference generated by RIS,
• design, implementation and demonstration of a D-band link into a shadow region via reflection in anomalously reflecting metasurfaces.
Finally, important highlights of advances beyond the state of the art with respect to the Artificial Intelligence based wireless system concept pillar (III) include:
• a low-complexity algorithm design based on the stochastic block gradient descent method for optimum beamforming in the presence of random blockage,
• an Intelligent Spectrum Learning algorithm design exploiting Convolutional Neural networks,
• application of deep unfolding neural network for channel estimation in RIS-aided systems,
• optimum beam selection in multi-user multiple-input-single-output (MISO) downlink scenarios using distributed Deep Reinforcement Learning (DRL) methods,
• the design and evaluation of a protocol for minimizing Initial Access delay in D-band wireless systems and RIS-empowered D-band wireless systems,
• formulation and solution of the cell-user assignment problem using the proposed AI/ML hybrid framework combining Metaheuristics and Machine Learning algorithms, and
• cell-user assignment problem exploiting Complex Event Forecasting (CEF) leading to handover reduction.
In respect to techno-economic analysis performed in the project, business models and roles in the future communications systems, in particular the RIS aided, have been explored, where an assessment of a particular backhauling case has been done.
