德国电信

The O-RAN Alliance promotes the integration of intelligent autonomous agents to control the Radio Access Network (RAN). This improves flexibility, performance, and observability in the RAN, but introduces new challenges, such as the detection and management of conflicts among the intelligent autonomous agents. A solution consists of profiling the agents before deployment to gather statistical information about their decision-making behavior, then using the information to estimate the level of conflict among agents with different goals. This approach enables determining the occurrence of conflicts among agents, but does not provide information about the impact on RAN performance, including potential service degradation. The problem becomes more complex when agents generate control actions at different timescales, which makes conflict severity hard to predict. In this paper, we present a novel approach that fills this gap. Our solution leverages the same data used to determine conflict severity but extends its use to predict the impact of such conflicts on RAN performance based on the frequency at which each agent generates actions, giving more weight to faster applications, which exert control more frequently. Via a prototype, we demonstrate that our solution is viable and accurately predicts conflict impact on RAN performance.

Covering codes in finite Hamming spaces ask for small sets of words whose Hamming balls cover the whole space. This paper presents a Lean 4 formalization of the elementary theory of q-ary covering codes, centered on certificate predicates for upper bounds, lower bounds, and exact covering numbers K_q(n,r). The formalization proves the q-ary Hamming-ball volume formula, the sphere-covering lower bound, elementary exact cases, product and relation rules, and selected small exact certificates. It also demonstrates an end-to-end workflow for checking explicit upper bounds transcribed from van Laarhoven et al. (1989). The accompanying database is proof-carrying: stored bounds have traces that replay to Lean proofs of the corresponding upper- or lower-bound predicates. The contribution is not new record bounds or a reproduction of known tables, but a reusable, auditable foundation for machine-checked covering-code certificates.

The coordination of large populations of highly constrained devices, such as micro- and nanoscale agents in biomedical applications, poses fundamental challenges to classical communication paradigms. In scenarios such as targeted drug delivery, devices operate under severe limitations in energy, size, and communication capabilities, while requiring precise and selective activation within spatially localized regions. In this work, we propose the framework of Joint Detection and Identification (JDAI) as a system-level approach for scalable control under such constraints. The key idea is to shift from reliable message transmission to a control-oriented paradigm, in which devices locally decide whether a broadcast signal is relevant. This enables implicit addressing and subset activation without the need for explicit per-device communication. We demonstrate how message identification can be combined with sensing. This enables the realization of a closed-loop system that integrates detection, communication, and actuation. Using the example of targeted nanorobot therapy, we analyze the interplay between sensing resolution, communication constraints, and system dynamics. In particular, we show that while identification exhibits favorable asymptotic scaling, practical implementations are governed by finite blocklength effects, noise, and latency. The proposed framework complements existing physical-layer communication approaches, including molecular, electromagnetic, and acoustic techniques, by providing a control-layer abstraction for scalable subset selection. Overall, JDAI connects identification-theoretic principles with system-level design to control large, resource-limited environments.

As quantum technologies mature, telecommunication operators have a clear opportunity to unlock and scale new services by providing the connectivity layer that links quantum computers, sensors, clocks, and other quantum devices. Realizing this opportunity requires demonstrating quantum networking protocols, including quantum teleportation, under real-world conditions on existing telecom infrastructure. In this work, we demonstrate quantum teleportation over Deutsche Telekom's metropolitan fiber testbed in Berlin using commercial components deployed at the telecom datacenter. A local Bell-state measurement between 795 nm photons from a weak coherent source and from a bichromatic warm-atom entangled photon source enables conditional state transfer onto an O-band photon, which is transmitted through a 30-km field-deployed fiber loop under real-world environmental conditions. The teleported state is reconstructed after propagation via state tomography, achieving an average teleportation fidelity of 90% on the deployed link. System performance is evaluated in both the absence and the presence of co-propagating C-band classical traffic within the same fiber, demonstrating compatibility with wavelength-division multiplexed telecom infrastructure carrying live data channels.

This paper presents a measurement-based study of radio propagation along traffic roads under varying environmental conditions, including terrain variations, noise barriers, and vegetation. Field measurements at mobile communication frequencies relevant to legacy and 5G cellular systems are used to characterize propagation behavior along traffic roads. Based on these measurements, high-resolution elevation and land-use data are employed to develop a baseline propagation modeling framework that captures attenuation caused by roadside environments. The proposed approach enables propagation analysis beyond conventional distance-based reference models and shows improved agreement with measured data. The presented framework provides a general basis for propagation analysis in traffic road environments and supports future extensions toward more advanced modeling techniques.