This paper explores the integration of neuromorphic computing with wireless sensing, focusing on radar processing within the framework of Integrated Sensing and Communication (ISAC). In this context, we propose a neuromorphic signal processing module that employs an extension of the Spiking Locally Competitive Algorithm (S-LCA) to perform delay-Doppler estimation in an OFDM-based sensing setup. As sensing reference signals, we utilize sequences from a finite-dimensional Gabor frame constructed from time-frequency translates of a seed vector. We refer to a Gabor frame construction based on the Alltop seed vector due to its low mutual coherence and hardware-friendly implementation. The proposed system is deployed on the SpiNNaker neuromorphic platform, demonstrating notable power savings compared to traditional hardware.

This paper studies ultra-reliable low-latency communications (URLLC) scheduling in the finite blocklength regime. We develop a packet-level framework in which each packet is associated with a finite set of feasible transmission modes, including single-shot transmission, hybrid automatic repeat request (HARQ) with Chase combining (CC), and packet segmentation. We formulate a multi user equipment (UE) scheduling problem that maximizes the expected delivered payload per consumed resource element (RE) under latency, reliability, and multi-slot resource coupling constraints. To solve this problem, we design a dynamic queue-based weighting mechanism inspired by Lyapunov methods, where backlog and deadline urgency adapt the scheduling priorities. Low-complexity algorithms based on linear relaxation (LR) and sequential greedy selection are proposed. Simulation results show significant packet delivery ratio (PDR) improvement over round-robin (RR), proportional fair (PF), and weighted-sum baselines while maintaining fairness, with throughput within about 6 % of a no-latency benchmark.

In the context of integrated sensing and communication (ISAC) for 6G, sensing-related information needs to be exchanged between network nodes, analogous to channel state information (CSI) reporting in 5G NR Advanced. This paper proposes a general framework for the exchange of sensing-related data, considering scenarios in which the sensing procedure is split between the user device and the network side. Depending on the level of processing applied at the device, we consider three approaches: (i) shallow processing, where most of the sensing-related processing is offloaded to the base station (BS); (ii) autoencoder (AE)-based data-level processing, where an encoder-decoder pair is trained to compress (device side) and reconstruct (BS side) range-angle maps in an unsupervised manner; and (iii) AE-based feature-level processing, incorporating an encoder at the device to compress relevant features, which are then recovered at the BS and used for the sensing task in a way similar to (i).To ensure realistic and reproducible propagation conditions, we employ an end-to-end (E2E) simulation framework based on ray tracing, and examine how compression level, reconstruction method, and sparsity interact to affect the sensing performance. The results indicate that AEs are beneficial in communication-constrained regimes by learning compact yet informative representations, whereas shallow processing at the device is permissible when higher communication overhead is allowed.

Teleoperated driving is a highly demanding connected- mobility application, requiring ultra-reliable and low-latency communication under dynamic and heterogeneous network conditions. In campus networks, such as private industrial deployments, these guarantees are challenged by fluctuating traffic load, mobility-induced handovers, and time-varying radio conditions. This article presents a modular multi-twin framework for coordinated optimization of network and application behavior, along with a demonstrator that models a 5G campus network at the Bosch Research site in Hildesheim. The framework integrates digital twins of vehicles, user equipment, and the radio access network within a shared digital world, enabling prediction of mobility, radio conditions, and cell load. We demonstrate automated network- and application-level actions, including predictive handover management, loadaware steering of lower-priority users, and application- level adaptation for teleoperated vehicles. Results show that anticipatory coordination can prevent congestion and avoid service-level violations. This illustrates the potential of digital twins to enable predictive, self-optimizing campus networks.

The reconfigurable intelligent surface (RIS) is a promising technology for next-generation wireless communication systems, owing to its ability to intelligently manipulate wireless channels by adjusting the phase of impinging signals. Despite its potential, the practical deployment of RIS faces significant challenges, primarily due to the requirement of accurate RIS configuration, leading to substantial channel estimation overhead and increased hardware complexity. While the use of statistical channel state information (CSI) mitigates this issue to some extent, the dynamic nature of wireless channels, driven by user mobility, environmental changes, and other factors, continues to restrict system performance. In this work, we propose a joint optimization framework to address these challenges. Our approach combines a neural network-based RIS configuration method called statistical-RISnet (S-RISnet), which uses partial historical channel samples to predict optimal phase shifts, with a robust change-detection mechanism for the covariance matrix. The detector monitors variations in the angle of arrival (AoA) and user equipment (UE) distance, updating the channel covariance matrix only when significant performance degradation is predicted. Simulation results demonstrate that our proposed method significantly reduces estimation and computation overhead while maintaining performance near the theoretical optimum. This framework offers a scalable and efficient solution for real-world RIS-assisted networks, enabling feasible and high-performance deployment.

This paper addresses the coverage extension of User Equipment (UE) through the strategic placement of Reconfigurable Intelligent Surfaces (RISs). By considering Line-of-Sight (LOS) and surface orientation as key factors influencing RIS performance, we enhance existing models by incorporating 3D scenarios and more realistic wave propagation effects. Given that the RIS placement problem is NP-hard, we propose a low-complexity algorithm inspired by submodular optimization. Extensive numerical simulations in an urban environment demonstrate the scalability and effectiveness of the proposed approach, achieving coverage enhancements comparable to exhaustive search methods but with significantly reduced computational complexity. Furthermore, our results indicate that while RIS placement effectively extends LOS coverage, its primary benefit lies in basic coverage enhancement rather than delivering high data rates.

The Reconfigurable Intelligent Surface (RIS) is a popular technology in the evolution of 6 G wireless networks. However, the integration of RIS into realistic and standardized wireless channel simulators remains limited. The QuaDRiGa framework, recognized by 3 GPP as a reference model, is extended in this work to include RIS as a configurable network element. This paper details a modular, tile-based RIS implementation in QuaDRiGa, enabling scalable and flexible simulations across a wide range of deployment scenarios while balancing physical modeling accuracy and computational complexity. Although the current implementation does not include a nearfield model for RIS, it allows users to modify the configuration of the RIS through tiling. This approach reduces the effective near-field region of the RIS such that it is determined by the size of each tile rather than the entire RIS, making the implementation adaptable to near-field scenarios while managing the computational complexity. To demonstrate its utility, we present a rank-enhancement use case, showcasing the implementations adaptability to scalable scenarios.

Integrated sensing and communication (ISAC) is a cornerstone of sixth-generation (6G) wireless networks, enabling the seamless integration of high-speed communication with precise sensing and localization. The design of ISAC systems typically involves trade-offs between communication and sensing performance. This paper explores different aspects of orthogonal frequency-division multiplexing (OFDM) waveforms for monostatic radar, aiming to improve sensing performance while maintaining communication capabilities. Our derivation shows that range resolution is influenced by the shape of the baseband transmission pulse. However, as more bandwidth is allocated for sensing, the pulse's impact on resolution becomes negligible. Additionally, we investigate how resource allocation strategies affect resolution and ambiguity in both range and Doppler. Several adjacent and non-adjacent schemes are evaluated through simulations using a realistic ISAC framework developed at Fraunhofer HHI. The results highlight key trade-offs and provide recommendations for ISAC waveform design, laying a solid foundation for future research in this area.

It is envisioned that 6G mobile networks will enhance and majorly empower the Industry 4.0 paradigm, evolving towards smart factories with optimized and customized services. Especially the smart factory scenario with real-time and high-capacity data communication presents us with new challenges, both in communications (mmW/sub-THz) and networking. This article discusses these new challenges and proposes extensions to the current Open Radio Access Network (Open RAN) standards for 6G networks to enable further evolution of Industry 4.0 and beyond. We motivate the need for real-time functionalities in Open RAN and an extended interface to the user equipment (UE) to allow for its fine-grained control.

Next-generation sixth generation (6G) technologies require massive antenna deployments, increasing overhead for channel estimation and network reconfiguration. Covariance matrix (CM)-based methods help reduce this overhead but face two challenges: the need for many channel samples and the matrix’s slow but non-negligible variation. This paper proposes a two-step change point detection algorithm to address both issues. The first step detects angle of arrival (AoA) shifts to trigger channel updates only when necessary. The second evaluates spectral efficiency (SE) loss to decide when network reconfiguration is required. The proposed method strikes a balance between overhead, performance, and computational complexity, making it well suited for next-generation large-scale dynamic wireless systems.

Open radio access network (O-RAN) is a paradigm shift in telecommunications, facilitating interoperability and innovation through the disaggregation of traditional monolithic architecture, empowering operators to select equipment from diverse vendors. However, within the multi-vendor O-RAN ecosystem, individual xApps may pursue conflicting objectives. While fine-tuned coordination can alleviate conflicts, it often requires extensive information exchange, raising privacy concerns among competing vendors. This paper delves into these challenges, particularly focusing on the interplay between different xApps, such as energy efficiency (EE) and load balancing (LB), and highlights the tradeoff between performance and level of coordination. To address this, we propose novel algorithms to optimize performance across varying levels of coordination. Initial findings underscore the diminishing returns of coordination, with significant performance gains from zero to partial coordination, yet a more modest increase with full coordination.

We propose a reconfigurable intelligent surface (RIS)-assisted wiretap channel, where the RIS is strategically deployed to provide a spatial separation to the transmitter, and orthogonal combiners are employed at the legitimate receiver to extract the data streams from the direct and RIS-assisted links. Then we derive the achievable secrecy rate under semantic security for the RIS-assisted channel and design an algorithm for the secrecy rate optimization problem. The simulation results show the effects of total transmit power, the location and number of eavesdroppers on the security performance.

We propose a reconfigurable intelligent surface (RIS)-assisted wiretap channel, where the RIS is strategically deployed to provide a spatial separation to the transmitter, and orthogonal combiners are employed at the legitimate receiver to extract the data streams from the direct and RIS-assisted links. Then we derive the achievable secrecy rate under semantic security for the RIS-assisted channel and design an algorithm for the secrecy rate optimization problem. The simulation results show the effects of total transmit power, the location and the number of eavesdroppers on the security performance.

We study the problem of selecting a subset of vectors from a large set, to obtain the best signal representation over a family of functions. Although greedy methods have been widely used for tackling this problem and many of those have been analyzed under the lens of (weak) submodularity, none of these algorithms are explicitly devised using such a functional property. Here, we revisit the vector-selection problem and introduce a function which is shown to be submodular in expectation. This function does not only guarantee near-optimality through a greedy algorithm in expectation, but also alleviates the existing deficiencies in commonly used matching pursuit (MP) algorithms. We further show the relation between the single-point-estimate version of the proposed greedy algorithm and MP variants. Our theoretical results are supported by numerical experiments for the angle of arrival estimation problem, a typical signal representation task; the experiments demonstrate the benefits of the proposed method with respect to the traditional MP algorithms.

Open radio access network (O-RAN) offers interoperability and innovation by disaggregating traditional monolithic architecture. However, open networks' complexity and dynamic nature pose significant optimization challenges. In this context, digital twins are crucial as they enable real-time monitoring, simulation, and optimization, leading to substantial performance improvements in the O-RAN network. This paper presents a framework that integrates the ns-3 network simulator, the Open Source Community (OSC) RAN Intelligent Controller (RIC), and xApps within O-RAN digital twins. By bridging the gap between simulation and real-world deployment, this research underscores the role of digital twins in the O-RAN ecosystem for developing and testing xApps. These digital twins enhance network performance optimization through real-time data analysis and simulation, enabling operators to seamlessly transition from simulated environments to actual telecom infrastructure. This integration not only promotes the programmability and disaggregation of network functions but also drives innovation in network management and control, leading to more intelligent and responsive wireless networks.

Future smart factories are expected to deploy reliable applications over high-performance indoor wireless channels in the millimeter-wave (mmWave) bands. Since these bands are known to be susceptible to high path losses and Line-of-Sight (LoS) blockages, low-cost Reconfigurable Intelligent Surfaces (RISs) are used to enhance the reliability of wireless links. In this paper, we formulate a combinatorial optimization problem, solved with Integer Linear Programming (ILP) to optimally maintain reliable connectivity by solving the problem of allocating RIS to robots in an indoor wireless network. Our model exploits a new feature of the so-called nulling interference from RISs by tuning reflection coefficients. We further consider the system reliability by defining Quality-of-Service (QoS) at receivers in terms of Signal-to-Interference-plus-Noise Ratio (SINR) and connection outages due to insufficient transmission quality. Numerical results for the optimal solution and heuristics show the benefits of optimally deploying RISs by providing continuous connectivity through SINR. We also show that our method can significantly reduce outages due to link quality, while meeting the requirements on system and service reliability.

Thanks to its slow-varying characteristic and relatively low requirement for estimation overhead, the covariance matrix has been extensively researched in sixth-generation (6G) wireless systems. Nevertheless, user mobility in practice will cause a gradual change in the covariance matrix, thereby deteriorating the system's performance if no update of the covariance matrix is applied. In this paper, we study the problem of efficient detection of gradual changes in the covariance matrix. We first introduce four change-point detectors that directly map the observations to change in our target KPI. Then, we propose a low-overhead detection algorithm that omits unnecessary channel estimations by adapting an AoA-based estimation trigger. Simulation results show that our proposed scheme can provide near-optimal performance while drastically reducing the estimation and computation overhead.

The reconfigurable intelligent surface (RIS) technology is a potential solution to enhance network capacity and coverage without significant investment into additional infrastructure in 6G networks. This work highlights the interest of the mobile communication industry on RIS, and discusses the development of liquid crystal-based RIS for improved energy efficiency and coverage in the millimeter-wave band. Furthermore, the paper discusses perspectives and insights from an industry R&D point of view, addressing relevant use cases, technical requirements, implementation challenges, and practical considerations for RIS deployment optimization in the context of 6G networks. A hardware design of a RIS with liquid crystal at 28 GHz is presented. A propagation model for RIS as a new part in the system architecture is discussed with the approaches of semi-empirical models, geometric models and its combination by the application of artificial intelligence / machine learning. Finally, a channel model for deployment optimization and dimensioning is presented with the findings that rather large RIS is in favor for coverage improvement as well as a greater attenuation at higher frequencies combined with a smaller RIS size.

Future sixth-generation (6G) wireless networks will have to support ubiquitous communication, together with highly accurate sensing and localization services. This paper considers the problem of user-centric monostatic sensing aided by a reconfigurable intelligent surface (RIS). A major challenge in user-centric sensing is typically the limited hardware capability of user equipments (UEs), which makes it difficult in practice to fulfill the stringent requirements of some sensing applications. In this context, this paper proposes using RIS to provide a virtual bistatic perspective that complements UE-based sensing. The main motivation is that the high angular resolution of RIS, thanks to its typically large surface, can be combined with the (relatively high) ranging accuracy of the UE to achieve more accurate and reliable sensing. Simulation results demonstrate that the combination, i.e. the complementary use of RIS and UE, can significantly improve the sensing performance, especially in challenging radio propagation environments. Effectively, this would enable UEs with reduced capabilities to perform sensing with the required precision, potentially impacting various applications, such as target detection and tracking in robotic sensing, as well as simultaneous localization and (environmental) mapping.