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The space communications industry is challenged to develop a technology that can deliver broadband services to user terminals equipped with miniature antennas, such as handheld devices. One potential solution to establish links with ground users is the deployment of massive antennas in one single spacecraft. However, this is not cost-effective. Aligning with recent \emph{NewSpace} activities directed toward miniaturization, mass production, and a significant reduction in spacecraft launch costs, an alternative could be distributed beamforming from multiple satellites. In this context, we propose a distributed beamforming modeling technique for wideband signals. We also consider the statistical behavior of the relative geometry of the swarm nodes. The paper assesses the proposed technique via computer simulations, providing interesting results on the beamforming gains in terms of power and the security of the communication against potential eavesdroppers at non-intended pointing angles. This approach paves the way for further exploration of wideband distributed beamforming from satellite swarms in several future communication applications., Comment: PIMRC'23 WS MCSN-6G (accepted)

The present report introduces the results of an extensive 3D laser measurement campaign with the objective of obtaining a precise raytracing model from point cloud data in an industrial environment. The measurements were conducted in a Bosch factory in Germany. The resulting model is tested with different raytracing tools and contrasted with multiband radio frequency measurements, simultaneously conducted in the same scenario. This raytracing model allows simulations with spatial consistency over the different bands of interest in 6G BRAINS, providing an accurate geometrical representation of the environment from the propagation properties for precise localization applications.

This deliverable reports all the activities and outcomes related to specifying the requirements for the multi-agent Deep Reinforcement Learning (MA-DRL) scheme of 6G BRAINS. Several applications of DRL are identified by the partners in relation to the general 6G BRAINS use cases defined in D2.1. For each of the DRL applications, it will identify the interfaces between the network and the MA-DRL, as well as the data that need to transit between the MA-DRL scheme and the network. This report lays the ground for the developments of the RL-applications in the MA-DRL scheme.