Huawei Technologies (Germany)

Quantum Communications promise advances in cryptography, quantum computing and clock synchronisation, among other emerging applications. However, communication based on quantum phenomena requires an extreme level of isolation from external disturbances, complicating the co-propagation of quantum and classical signals. The challenge is greater when deploying networks that are both heterogeneous (e.g., multiple vendors) and installed in production facilities, given that this type of infrastructure already supports networks loaded with their own requirements. Moreover, to achieve a broad acceptance among network operators, the joint management and operation of quantum and classical resources, compliance with standards, and legal and quality assurance need to be addressed. This article presents solutions to the aforementioned challenges validated in the Madrid quantum network during the implementation of the projects CiViC and OpenQKD. This network was designed to integrate quantum communications in the telecommunications ecosystem by installing quantum-key-distribution modules from multiple providers in production nodes of two different operators. The modules were connected through an optically-switched network with more than 130 km of deployed optical fibre. The tests were done in compliance with strict service level agreements that protected the legacy traffic of the pre-existing classical network. The goal was to ensure full quantum-classical interoperability at all levels, while limiting the modifications to optical transport and encryption and complying with relevant standards. This effort is intended to lay the foundation for large-scale quantum network deployments.

The emergence of the 6 G era demands seamless orchestration across an increasingly heterogeneous and distributed compute continuum-spanning edge, fog, and cloud resources. Moreover, the next generation of the mobile network is poised to redefine the digital landscape by enabling pervasive intelligence, ultra-low latency communication, and extreme heterogeneity across the entire network infrastructure. This transformation introduces unprecedented orchestration challenges due to the dynamic, multi-domain, and resource-constrained nature of emerging workloads such as Generative Artificial Intelligence (GenAI) inference, immersive eXtended Reality (XR), and autonomous systems. To tackle this complexity, we advocate for a Hybrid Hierarchical Digital Twin (DT) architecture that serves as a foundation for intelligent, adaptive, and real-time orchestration in 6 G environments. We present a comprehensive vision for integrating DTs as enablers of intelligent, context-aware, and adaptive orchestration mechanisms that span across multiple domains. The proposed architecture introduces a multi-layered DT hierarchy combining local and global views, enabling scalable coordination and real-time decision-making. We highlight key architectural enhancements required to realize this vision, including inter-twin interoperability and behavioral modeling for QoE estimation. This work aims to guide researchers and practitioners in shaping the foundations of resilient and efficient orchestration frameworks for 6 G systems.

Networked Control System (NCS) is a paradigm where sensors, controllers, and actuators communicate over a shared network. One promising application of NCS is the control of Automated Guided Vehicles (AGVs) in the industrial environment, for example to transport goods efficiently and to autonomously follow predefined paths or routes. In this context, communication and control are tightly correlated, a paradigm referred to as Joint Communication and Control (JCC), since network issues such as delays or errors can lead to significant deviations of the AGVs from the planned trajectory. In this paper, we present a simulation framework based on Gazebo and Robot Operating System 2 (ROS 2) to simulate and visualize, respectively, the complex interaction between the control of AGVs and the underlying communication network. This framework explicitly incorporates communication metrics, such as delay and packet loss, and control metrics, especially the Mean Squared Error (MSE) between the optimal/desired and actual path of the AGV in response to driving commands. Our results shed light into the correlation between the network performance, particularly Packet Reception Ratio (PRR), and accuracy of control.

Liquid crystal on silicon (LCoS) based wavelength selective switch (WSS) has enabled several features including flexible grid, channel bandwidth shaping, signal switching and broadcasting. However, many challenges remain when using the LCoS in WSS. In this paper, we will discuss some of them including: static crosstalk, transient crosstalk during switching, reflectivity and switching speed improvement, PDL reduction, digital flicker, etc.

The technology of autonomous driving vehicles has made rapid progress over the last decade, but the challenge of proving these system's safety still exists. Compared to conventional mile-based testing, scenario-based testing (SBT) is a more promising solution since scenarios covering diverse and rare driving conditions in real traffic can be simulated to assess the system's performance in safety-critical scenarios. Furthermore, understanding run-time scenarios is vital to trigger safety mechanisms designed for the Safety Of The Intended Functionality (SOTIF). However, a challenging task is to extract or generate scenarios in the design phase and recognize driving scenarios in the run-time phase due to the complexity and diversity of driving scenarios, especially the interaction with other driving agents. In this study, we proposed a complete framework for offline extraction and online identification of all kinds of interaction scenarios. A covariance-clustering based method was adopted to identify the meta-driving actions, which used the Toeplitz matrix to achieve more interpretable clustering results than distance-based methods. Experiments demonstrated the effectiveness of our method by its robust identification results for cut-in and merge-in scenarios. With a lightweight design and a theoretically valid confidence estimation method, our approach is computationally efficient for reliable online applications.

Remote sensing image object detection (RSIOD) aims to identify and locate specific objects within satellite or aerial imagery. However, there is a scarcity of labeled data in current RSIOD datasets, which significantly limits the performance of current detection algorithms. Although existing techniques, e.g., data augmentation and semi-supervised learning, can mitigate this scarcity issue to some extent, they are heavily dependent on high-quality labeled data and perform worse in rare object classes. To address this issue, this paper proposes a layout-controllable diffusion generative model (i.e. AeroGen) tailored for RSIOD. To our knowledge, AeroGen is the first model to simultaneously support horizontal and rotated bounding box condition generation, thus enabling the generation of high-quality synthetic images that meet specific layout and object category requirements. Additionally, we propose an end-to-end data augmentation framework that integrates a diversity-conditioned generator and a filtering mechanism to enhance both the diversity and quality of generated data. Experimental results demonstrate that the synthetic data produced by our method are of high quality and diversity. Furthermore, the synthetic RSIOD data can significantly improve the detection performance of existing RSIOD models, i.e., the mAP metrics on DIOR, DIOR-R, and HRSC datasets are improved by 3.7%, 4.3%, and 2.43%, respectively. The code is available at here.

Due to the large available bands and specific propagation characteristics, terahertz (THz) communication systems hold the promise of providing high throughput, low latency communications, with concurrent possibility of achieving high sensing accuracy. For the accurate modelling of THz channels in indoor communications environments, such as offices, meeting rooms, hallways, and living rooms, the precise knowledge of the dielectric properties of common building materials is essential. To this aim, in this paper we present a measurement setup based on state-of-the-art frequency-domain spectroscopy system. For an accurate characterization of the material properties, custom beam-guide fixture and signal processing technique were developed. Preliminary tests carried out with samples of well-known dielectric materials proved a good measurement accuracy. Further, we performed measurements of glass and ceiling board samples in the frequency band of 100 to 400 GHz. Using our measurements, we extended the model in ITU-R P.2040-3 by deriving custom parameters to describe the dielectric behavior of these materials. The outcomes of this study can improve the deterministic modelling of wireless channels at sub-THz frequencies.

As 6G development progresses, joint sensing and communication (JSC) is emerging as a transformative technology, promising enhanced spectrum and energy efficiency alongside innovative services. This paper delves into underexplored facets of JSC, particularly its role in vehicular technology and transportation systems. It discusses data fusion techniques that enable cooperative sensing in networked environments and underscores the critical role of resource management in balancing sensing and communication. It suggests modeling extended targets, such as vehicles, within a computationally feasible framework. Moreover, it proposes a novel integration of AI-based target recognition, allowing target-specific tracking parameters and target-based sensing resource allocation. Importantly, a case study is presented to underscore the real-world applicability of these concepts in vehicular scenarios, demonstrating how networked devices can achieve high sensing and communication performance.