Ericsson (Germany)

In 3GPP networks, Access Traffic Steering, Switching, and Splitting (ATSSS) is specified to enable network-assisted use of multiple paths based on a multipath-enabled QUIC tunnel. Using multiple paths can introduce packet reordering because of path asymmetry, loss, and differing delays. Packet reordering may be mistaken for congestion and thus lead to unnecessary reductions in throughput. While various packet-based schedulers have been proposed to address these issues, stream-aware concepts have not been widely explored when multiple end-to-end flows use the same tunnel. In this paper, we present our scheduling framework that supports stream-aware scheduling and compare state-of-the-art packet-based schedulers to stream-aware approaches in the multipath tunnel scenario, with particular focus on the Bandwidth-Limited Application Demand Estimating (BLADE) scheduler. Our results show that stream-aware scheduling concepts mitigate reordering and improve throughput in this scenario. This paper provides an overview of the current state of stream-aware scheduling for proxy scenarios. Future work is necessary and should investigate stream-aware techniques, adapt packet-based schedulers to be stream-aware, and propose novel stream-aware schedulers.

Deploying Machine Learning (ML) models in wireless networks comes with challenges beyond standard performance evaluation, particularly in their ability to remain reliable under changing network conditions. This study highlights some of these challenges by analyzing how models perform when tested on new radio links after training was completed, revealing significant performance degradation. A testbed was developed to study these effects, capturing real-world variations in wireless environments. Using this setup, the study based on link quality estimation (LQE), that is a critical aspect of network performance, demonstrates how data distribution shifts affect model performance. The findings from this first step emphasize the need for continuous monitoring and adaptation strategies, as well as further research on effectively implementing these methods in wireless networks.

Industry 4.0 promises the increase of productivity and efficiency within manufacturing and industrial processes. A key milestone in this evolution is the 3GPP specification on time-sensitive communication and integration of 5G with time-sensitive networking (TSN), thus enabling the required flexibility for future time-sensitive industrial applications. Although 3GPP introduces ultra-reliable low latency communication (URLLC) features to meet stringent timing requirements, the notable challenge persists in resource overprovisioning, limiting the capacity of the next-generation radio access network (NG-RAN). Our work addresses this by focusing on the sources of packet delay and packet delay variation within the 6G system. Through analysis and proposed enhancements, supported by simulation and measurement results, we aim to minimize queuing delay and optimize 6GS capacity. Our study not only explores standardized 5GS features but also proposes extensions to support periodic deterministic traffic, offering insights to enhance 6GS performance and capacity in real-world scenarios.

Mobile wireless broadband systems using 4G and 5G are being widely deployed. At the same time, traffic demand is still growing with operators challenged for capacity from their spectrum. The need is addressed through more compact site installations and further integrating multi-band radios and MIMO antenna systems together on the same site. However, high transmitted power levels of multiple 3GPP broadband carriers tend to generate Passive Intermodulation (PIM) sources in such high electric-field situations. With the surge of these compact integrated radio antenna systems came an increase in PIM requirements for site deployments as well as the need for suitable testing methods. This paper addresses the complexity of real-world scenarios by conducting a comprehensive investigation into Radio Base Station (RBS) equipment with PIM issues observed over the past few years. Using realistic measurements and empirical data, the findings provide valuable insights into PIM test methodologies using both Continuous-Wave (CW) and modulated signals. Furthermore, this work contributes to the development of large-scale deployment testing methodologies, fostering a deeper understanding of PIM-related challenges in 5G wireless systems. This work can help mitigate and manage PIM issues in the evolving landscape of 5G wireless communication systems.

Abstract We explore some of the risks related to Artificial Intelligence (AI) from an actuarial perspective based on research from a transregional industry focus group. We aim to define the key gaps and challenges faced when implementing and utilising modern modelling techniques within traditional actuarial tasks from a risk perspective and in the context of professional standards and regulations. We explore best practice guidelines to attempt to define an ideal approach and propose potential next steps to help reach the ideal approach. We aim to focus on the considerations, initially from a traditional actuarial perspective and then, if relevant, consider some implications for non-traditional actuarial work, by way of examples. The examples are not intended to be exhaustive. The group considered potential issues and challenges of using AI, related to the following key themes: Ethical ○ Bias, fairness, and discrimination ○ Individualisation of risk assessment ○ Public interest Professional ○ Interpretability and explainability ○ Transparency, reproducibility, and replicability ○ Validation and governance Lack of relevant skills available Wider themes This paper aims to provide observations that could help inform industry and professional guidelines or discussion or to support industry practitioners. It is not intended to replace current regulation, actuarial standards, or guidelines. The paper is aimed at an actuarial and insurance technical audience, specifically those who are utilising or developing AI, and actuarial industry bodies.

In this paper, a novel radio unit (RU) uplink (UL)/downlink (DL) configuration algorithm for dynamic time division duplex (DTDD) distributed multiple-input multiple-output (D-MIMO) systems with half-duplex RUs is proposed, where the configuration is performed only by the long-term channel statistics without the knowledge of instantaneous channel state information (CSI). Although there are several RU configuration approaches based on instantaneous CSI in the literature, a remaining challenge is to reduce the frequency of RUs UL/DL reconfiguration so as to lower the computational cost in the network and reduce the control signaling overhead in the fronthaul links. The proposed method is shown to be effective compared to the state-of-the-art methods in the trade-off between performance, complexity, and fronthaul overhead.

Since its first release in the late 1990s, Wi-Fi has been updated to keep up with evolving user needs. Recently, Wi-Fi and other radio access technologies have been pushed to their edge when serving Augmented Reality (AR) applications. AR applications require high throughput, low latency, and high reliability to ensure a high-quality user experience. The 802.11be amendment, which will be marketed as Wi-Fi 7, introduces several features that aim to enhance its capabilities to support challenging applications like AR. One of the main features introduced in this amendment is Multi-Link Operation (MLO) which allows nodes to transmit and receive over multiple links concurrently. When using MLO, traffic is distributed among links using an implementation-specific traffic-to-link allocation policy. This paper aims to evaluate the performance of MLO, using different policies, in serving AR applications compared to Single-Link (SL). Experimental simulations using an event-based Wi-Fi simulator have been conducted. Our results show the general superiority of MLO when serving AR applications. MLO achieves lower latency and serves a higher number of AR users compared to SL with the same frequency resources. In addition, increasing the number of links can improve the performance of MLO. Regarding traffic-to-link allocation policies, we found that policies can be more susceptible to channel blocking, resulting in possible performance degradation.

Abstract5G promises to shift Industry 4.0 to the next level by allowing flexible production. However, many communication standards are used throughout a production site, which will stay so in the foreseeable future. Furthermore, localization of assets will be equally valuable in order to get to a higher level of automation. This paper proposes a reference architecture for a convergent localization and communication network for smart manufacturing that combines 5G with other existing technologies and focuses on high-mix, low-volume applications, particularly at small and medium-sized enterprises. The architecture is derived from a set of functional requirements, and we describe different views on this architecture to show how the requirements can be fulfilled. It connects private and public mobile networks with local networking technologies to achieve a flexible setup addressing many industrial use cases.KeywordsAdaptive manufacturing5GLocalizationM2M

Multiplexed Application Substrate over QUIC Encryption (MASQUE) is a new protocol mechanism that defines an extension to the HTTP CONNECT to support QUIC-based tunneling to a proxy and forwarding of UDP and IP traffic. MASQUE can be used in cases where tunneling is beneficial such as VPN-like scenarios or other proxy-based services. In this paper we investigate impacts on end-to-end QUIC performance when using a MASQUE-based tunnel setup. In particular, we investigate the impacts of reliable versus unreliable transmission of packets in the tunnel connection, as well as the impacts of nested congestion control between the inner connection and the out tunnel connection. We show that tunneling implies per-packet overhead but can also provide additional performance benefits, e.g. for lossy local links.

Cellular- Vehicle-to-Everything (C- V2X) communication as standardized in the 3rd generation partnership project (3GPP) plays an essential role in enabling fully autonomous driving. C- V2X envisions supporting various use-cases, e.g., platooning and remote driving, with varying quality of service (QoS) requirements regarding latency, reliability, data rate, and positioning. In order to ensure meeting these stringent QoS requirements in realistic mobility scenarios, an intelligent and efficient resource allocation scheme is required. This paper addresses channel congestion in location-based resource allocation based on Deep Reinforcement Learning (DRL) for vehicle user equipment (V-UE) in dynamic groupcast communication, i.e., without a V-UE acting as a group head. Using DRL base station acts as a centralized agent. It adapts the channel congestion due to vehicle density in resource pools segregated based on location in a TAPASCologne scenario in the Simulation of Urban Mobility (SUMO) platform. A system-level simulation shows that a DRL-based congestion approach can achieve a better packet reception ratio (PRR) than a legacy congestion control scheme when resource pools are segregated based on location.