Drone Carry-on Weight and Wind Flow Assessment via Micro-Doppler Analysis

Remote monitoring of drones has become a global objective due to emerging applications in national security and managing aerial delivery traffic. Despite their relatively small size, drones can carry significant payloads, which require monitoring, especially in cases of unauthorized transportation of dangerous goods. A drone's flight dynamics heavily depend on outdoor wind conditions and the carry-on weight, which affect the tilt angle of a drone's body and the rotation velocity of the blades. A surveillance radar can capture both effects, provided a sufficient signal-to-noise ratio for the received echoes and an adjusted postprocessing detection algorithm. Here, we conduct a systematic study to demonstrate that micro-Doppler analysis enables the disentanglement of the impacts of wind and weight on a hovering drone. The physics behind the effect is related to the flight controller, as the way the drone counteracts weight and wind differs. When the payload is balanced, it imposes an additional load symmetrically on all four rotors, causing them to rotate faster, thereby generating a blade-related micro-Doppler shift at a higher frequency. However, the impact of the wind is different. The wind attempts to displace the drone, and to counteract this, the drone tilts to the side. As a result, the forward and rear rotors rotate at different velocities to maintain the tilt angle of the drone body relative to the airflow direction. This causes the splitting in the micro-Doppler spectra. By performing a set of experiments in a controlled environment, specifically, an anechoic chamber for electromagnetic isolation and a wind tunnel for imposing deterministic wind conditions, we demonstrate that both wind and payload details can be extracted using a simple deterministic algorithm based on branching in the micro-Doppler spectra.

Experimental Validation of Coherent Joint Transmission in a Distributed-MIMO System with Analog Fronthaul for 6G

The sixth-generation (6G) mobile networks must increase coverage and improve spectral efficiency, especially for cell-edge users. Distributed multiple-input multiple-output (D-MIMO) networks can fulfill these requirements provided that transmission/reception points (TRxPs) of the network can be synchronized with sub nanosecond precision, however, synchronization with current backhaul and fronthaul digital interfaces is challenging. For 6G new services and scenarios, analog radio-over-fiber (ARoF) is a prospective alternative for future mobile fronthaul where current solutions fall short to fulfill future demands on bandwidth, synchronization, and/or power consumption. This paper presents an experimental validation of coherent joint transmissions (CJTs) in a two TRxPs D-MIMO network where ARoF fronthaul links allow to meet the required level of synchronization. Results show that by means of CJT a combined diversity and power gain of +5 dB is realized in comparison with a single TRxP transmission.

NR Conformance Testing of Analog Radio-over-LWIR FSO Fronthaul link for 6G Distributed MIMO Networks

We experimentally test the compliance with 5G/NR 3GPP technical specifications of an analog radio-over-FSO link at 9 {\mu}m. The ACLR and EVM transmitter requirements are fulfilled validating the suitability of LWIR FSO for 6G fronthaul.

Self-Evolutionary Reservoir Computer Based on Kuramoto Model

The human brain's synapses have remarkable activity-dependent plasticity, where the connectivity patterns of neurons change dramatically, relying on neuronal activities. As a biologically inspired neural network, reservoir computing (RC) has unique advantages in processing spatiotemporal information. However, typical reservoir architectures only take static random networks into account or consider the dynamics of neurons and connectivity separately. In this paper, we propose a structural autonomous development reservoir computing model (sad-RC), which structure can adapt to the specific problem at hand without any human expert knowledge. Specifically, we implement the reservoir by adaptive networks of phase oscillators, a commonly used model for synaptic plasticity in biological neural networks. In this co-evolving dynamic system, the dynamics of nodes and coupling weights in the reservoir constantly interact and evolve together when disturbed by external inputs.