arXiv subjects
Publications and source records attributed to Carlo Centofanti.
We implement full-field digital back-propagation with a 1-THz receiver using 20 synchronous frequency-adjacent coherent receivers with digital stitching and a frequency-comb local oscillator. Relative to electronic dispersion compensation, per-channel DBP and full-field DBP achieve throughput gains of 2.2\% and 5.4\%, respectively.
Advanced remote applications such as Networked Music Performance (NMP) require solutions to guarantee immersive real-world-like interaction among users. Therefore, the adoption of spatial audio formats, such as Ambisonics, is fundamental to let the user experience an immersive acoustic scene. The accuracy of the sound scene reproduction increases with the order of the Ambisonics enconding, resulting in an improved immersivity at the cost of a greater number of audio channels, which in turn escalates both bandwidth requirements and susceptibility to network impairments (e.g., latency, jitter, and packet loss). These factors pose a significant challenge for interactive music sessions, which demand high spatial fidelity and low end-to-end delay. We propose a real-time adaptive higher-order Ambisonics strategy that continuously monitors network throughput and dynamically scales the Ambisonics order. When available bandwidth drops below a preset threshold, the order is lowered to prevent audio dropouts; it then reverts to higher orders once conditions recover, thus balancing immersion and reliability. A MUSHRA-based evaluation indicates that this adaptive approach is promising to guarantee user experience in bandwidth-limited NMP scenarios.
Recent research has demonstrated the effectiveness of Artificial Intelligence (AI), and more specifically, Large Language Models (LLMs), in supporting network configuration synthesis and automating network diagnosis tasks, among others. In this preliminary work, we restrict our focus to the application of AI agents to network troubleshooting and elaborate on the need for a standardized, reproducible, and open benchmarking platform, where to build and evaluate AI agents with low operational effort.