Resource Allocation in a Quantum Key Distribution Network with LEO and GEO trusted-repeaters

Quantum Key Distribution~(QKD) is a technology that enables the exchange of private encryption keys between two legitimate parties, using protocols that involve quantum mechanics principles. The rate at which secret keys can be exchanged depends on the attenuation that is experienced. Therefore, it is more convenient to replace many terrestrial fiber segments (and repeaters) by just few optical satellite links that would enable flexible global coverage. Then, the satellite nodes can take the role of trusted-relays, forwarding the secret keys from source to destination. However, since the rate at which secret keys can be generated in each quantum link is limited, it is very important to select the intermediate satellite nodes to inter-connect ground stations efficiently. This paper studies the most convenient allocation of resources in a QKD network that combines complementary connectivity services of GEO and LEO satellites. The aim of the centralized routing algorithm is to select the most convenient trusted-relays to forward the secret keys between pairs of ground stations, verifying the constraints that satellite-to-ground and inter-satellite quantum channels have.

Visible light communication-based monitoring for indoor environments using unsupervised learning

Visible Light Communication~(VLC) systems provide not only illumination and data communication, but also indoor monitoring services if the effect that different events create on the received optical signal is properly tracked. For this purpose, the Channel State Information that a VLC receiver computes to equalize the subcarriers of the OFDM signal can be also reused to train an Unsupervised Learning classifier. This way, different clusters can be created on the collected CSI data, which could be then mapped into relevant events to-be-monitored in the indoor environments, such as the presence of a new object in a given position or the change of the position of a given object. When compared to supervised learning algorithms, the proposed approach does not need to add tags in the training data, simplifying notably the implementation of the machine learning classifier. The practical validation the monitoring approach was done with the aid of a software-defined VLC link based on OFDM, in which a copy of the intensity modulated signal coming from a Phosphor-converted LED was captured by a pair of Photodetectors~(PDs). The performance evaluation of the experimental VLC-based monitoring demo achieved a positioning accuracy in the few-centimeter-range, without the necessity of deploying a large number of sensors and/or adding a VLC-enabled sensor on the object to-be-tracked.