arXiv subjects

Hans D. Schotten

Publications and source records attributed to Hans D. Schotten.

At least 109 records · Page 6Linked to original sources

arXiv2024

Heterogeneous System Design for Cell-Free Massive MIMO in Wideband Communications

Cell-free massive multi-input multi-output (CFmMIMO) offers uniform service quality through distributed access points (APs), yet unresolved issues remain. This paper proposes a heterogeneous system design that goes beyond the original CFmMIMO architecture by exploiting the synergy of a base station (BS) and distributed APs. Users are categorized as near users (NUs) and far users (FUs) depending on their proximity to the BS. The BS serves the NUs, while the APs cater to the FUs. Through activating only the closest AP of each FU, the use of downlink pilots is enabled, thereby enhancing performance. This heterogeneous design outperforms other homogeneous massive MIMO configurations, demonstrating superior sum capacity while maintaining comparable user-experienced rates. Moreover, it lowers the costs associated with AP installations and reduces signaling overhead for the fronthaul network.

cs.IT↗

arXiv2024

Adaptive Prediction Approach for 3D Geometry-based communication

This paper addresses the challenges of mobile user requirements in shadowing and multi-fading environments, focusing on the Downlink (DL) radio node selection based on Uplink (UL) channel estimation. One of the key issues tackled in this research is the prediction performance in scenarios where estimated channels are integrated. An adaptive deep learning approach is proposed to improve performance, offering a compelling alternative to traditional interpolation techniques for air-to-ground link selection on demand. Moreover, our study considers a 3D channel model, which provides a more realistic and accurate representation than 2D models, particularly in the context of 3D network node distributions. This consideration becomes crucial in addressing the complex multipath fading effects within geometric stochastic 3D 3GPP channel models in urban environments. Furthermore, our research emphasises the need for adaptive prediction mechanisms that carefully balance the trade-off between DL link forecasted frequency response accuracy and the complexity requirements associated with estimation and prediction. This paper contributes to advancing 3D radio resource management by addressing these challenges, enabling more efficient and reliable communication for energy-constrained flying network nodes in dynamic environments.

cs.NI↗

arXiv2024

Nomadic Non-Public Networks for 6G: Use Cases and Key Performance Indicators

The landscape of wireless communication systems is evolving rapidly, with a pivotal role envisioned for dynamic network structures and self-organizing networks in upcoming technologies like the 6G mobile communications standard. This evolution is fueled by the growing demand from diverse sectors, including industry, manufacturing, agriculture, and the public sector, each with increasingly specific requirements. The establishment of non-public networks in the current 5G standard has laid a foundation, enabling independent operation within certain frequencies and local limitations, notably for Internet of Things applications. This paper explores the progression from non-public networks to nomadic non-public networks and their significance in the context of the forthcoming 6G era. Building on existing work in dynamic network structures, non-public networks regulations, and alternative technological solutions, this paper introduces specific use cases enhanced by nomadic networks. In addition, relevant Key Performance Indicators are discussed on the basis of the presented use cases. These serve as a starting point for the definition of requirement clusters and thus for a evaluation metric of nomadic non-public networks. This work lays the groundwork for understanding the potential of nomadic non-public networks in the dynamic landscape of 6G wireless communication systems.

cs.NI↗

arXiv2024

Robust Federated Learning for Wireless Networks: A Demonstration with Channel Estimation

Federated learning (FL) offers a privacy-preserving collaborative approach for training models in wireless networks, with channel estimation emerging as a promising application. Despite extensive studies on FL-empowered channel estimation, the security concerns associated with FL require meticulous attention. In a scenario where small base stations (SBSs) serve as local models trained on cached data, and a macro base station (MBS) functions as the global model setting, an attacker can exploit the vulnerability of FL, launching attacks with various adversarial attacks or deployment tactics. In this paper, we analyze such vulnerabilities, corresponding solutions were brought forth, and validated through simulation.

cs.LG↗

arXiv2024

Evaluation of Reinforcement Learning for Autonomous Penetration Testing using A3C, Q-learning and DQN

Penetration testing is the process of searching for security weaknesses by simulating an attack. It is usually performed by experienced professionals, where scanning and attack tools are applied. By automating the execution of such tools, the need for human interaction and decision-making could be reduced. In this work, a Network Attack Simulator (NASim) was used as an environment to train reinforcement learning agents to solve three predefined security scenarios. These scenarios cover techniques of exploitation, post-exploitation and wiretapping. A large hyperparameter grid search was performed to find the best hyperparameter combinations. The algorithms Q-learning, DQN and A3C were used, whereby A3C was able to solve all scenarios and achieve generalization. In addition, A3C could solve these scenarios with fewer actions than the baseline automated penetration testing. Although the training was performed on rather small scenarios and with small state and action spaces for the agents, the results show that a penetration test can successfully be performed by the RL agent.

cs.CR↗

arXiv2024

Bridging Simulation and Measurements through Ray-Launching Analysis: A Study in a Complex Urban Scenario Environment

With the rapid increase in mobile subscribers, there is a drive towards achieving higher data rates, prompting the use of higher frequencies in future wireless communication technologies. Wave propagation channel modeling for these frequencies must be considered in conjunction with measurement results. This paper presents a ray-launching (RL)-based simulation in a complex urban scenario characterized by an undulating terrain with a high density of trees. The simulation results tend to closely match the reported measurements when more details are considered. This underscores the benefits of using the RL method, which provides detailed space-time and angle-delay results.

eess.SP↗

arXiv2024

Cost-Effectiveness Analysis and Design of Cost-Efficient Cell-Free Massive MIMO Systems

Cell-free massive multi-input multi-output (MIMO) has recently attracted much attention, attributed to its potential to deliver uniform service quality. However, the adoption of a cell-free architecture raises concerns about the high implementation costs associated with deploying numerous distributed access points (APs) and the need for fronthaul network installation. To ensure the sustainability of next-generation wireless networks, it is crucial to improve cost-effectiveness, alongside achieving high performance. To address this, we conduct a cost analysis of cell-free massive MIMO and build a unified model with varying numbers of antennas per AP. Our objective is to explore whether employing multi-antenna APs could reduce system costs while maintaining performance. The analysis and evaluation result in the identification of a cost-effective design for cell-free massive MIMO, providing valuable insights for practical implementation.

cs.IT↗

arXiv2024

GNN-based Anomaly Detection for Encoded Network Traffic

The early research report explores the possibility of using Graph Neural Networks (GNNs) for anomaly detection in internet traffic data enriched with information. While recent studies have made significant progress in using GNNs for anomaly detection in finance, multivariate time-series, and biochemistry domains, there is limited research in the context of network flow data. In this report, we explore the idea that leverages information-enriched features extracted from network flow packet data to improve the performance of GNN in anomaly detection. The idea is to utilize feature encoding (binary, numerical, and string) to capture the relationships between the network components, allowing the GNN to learn latent relationships and better identify anomalies.

cs.SI↗

arXiv2024

A Robust UAV-Based Approach for Power-Modulated Jammer Localization Using DoA

Unmanned aerial vehicles (UAVs) are well-suited to localize jammers, particularly when jammers are at non-terrestrial locations, where conventional detection methods face challenges. In this work we propose a novel localization method, sample pruning gradient descend (SPGD), which offers robust performance against multiple power-modulated jammers with low computational complexity.

eess.SP↗

arXiv2024

Resource Allocation in Mobile Networks: A Decision Model Of Jockeying in Queues

Use-case-specific network slicing in decentralized multi-tenancy cloud environments is a promising approach to bridge the gap between the demand and supply of resources in next-generation communication networks. Our findings associate different slice profiles to queues in a multi-server setting, such that tenants continuously assess their preferences and make rational decisions to minimize the queuing delay. Deviated from classical approaches that statistically model the jockeying phenomena in queuing systems, our work pioneers to setup a behavioral model of jockeying impatient tenants. This will serve as a basis for decentralized management of multi-queue systems, where the decision to jockey is individually made by each tenant upon its up-to-date assessment of expected waiting time. Additionally, we carry out numerical simulations to empirically unravel the parametric dependencies of the tenants' jockeying behavior.

cs.NI↗

arXiv2024

Cell-Free Terahertz Massive MIMO: A Novel Paradigm Beyond Ultra-Massive MIMO

Terahertz (THz) frequencies have recently garnered considerable attention due to their potential to offer abundant spectral resources for communication, as well as distinct advantages in sensing, positioning, and imaging. Nevertheless, practical implementation encounters challenges stemming from the limited distances of signal transmission, primarily due to notable propagation, absorption, and blockage losses. To address this issue, the current strategy involves employing ultra-massive multi-input multi-output (UMMIMO) to generate high beamforming gains, thereby extending the transmission range. This paper introduces an alternative solution through the utilization of cell-free massive MIMO (CFmMIMO) architecture, wherein the closest access point is actively chosen to reduce the distance, rather than relying solely on a substantial number of antennas. We compare these two techniques through simulations and the numerical results justify that CFmMIMO is superior to UMMIMO in both spectral and energy efficiency at THz frequencies.

cs.IT↗

arXiv2024

Unified Modeling and Performance Comparison for Cellular and Cell-Free Massive MIMO

Cell-free massive multi-input multi-output (MIMO) has recently gained a lot of attention due to its high potential in sixth-generation (6G) wireless systems. The goal of this paper is to first present a unified modeling for massive MIMO, encompassing both cellular and cell-free architectures with a variable number of antennas per access point. We derive signal transmission models and achievable spectral efficiency in both the downlink and uplink using zero-forcing and maximal-ratio schemes. We also provide performance comparisons in terms of per-user and sum spectral efficiency.

cs.IT↗

arXiv2024

Act as a Honeytoken Generator! An Investigation into Honeytoken Generation with Large Language Models

With the increasing prevalence of security incidents, the adoption of deception-based defense strategies has become pivotal in cyber security. This work addresses the challenge of scalability in designing honeytokens, a key component of such defense mechanisms. The manual creation of honeytokens is a tedious task. Although automated generators exists, they often lack versatility, being specialized for specific types of honeytokens, and heavily rely on suitable training datasets. To overcome these limitations, this work systematically investigates the approach of utilizing Large Language Models (LLMs) to create a variety of honeytokens. Out of the seven different honeytoken types created in this work, such as configuration files, databases, and log files, two were used to evaluate the optimal prompt. The generation of robots.txt files and honeywords was used to systematically test 210 different prompt structures, based on 16 prompt building blocks. Furthermore, all honeytokens were tested across different state-of-the-art LLMs to assess the varying performance of different models. Prompts performing optimally on one LLMs do not necessarily generalize well to another. Honeywords generated by GPT-3.5 were found to be less distinguishable from real passwords compared to previous methods of automated honeyword generation. Overall, the findings of this work demonstrate that generic LLMs are capable of creating a wide array of honeytokens using the presented prompt structures.

cs.CR↗

arXiv2024

Hierarchical Cell-Free Massive MIMO for High Capacity with Simple Implementation

Cell-free massive multi-input multi-output (MIMO) has recently gained much attention for its potential in shaping the landscape of sixth-generation (6G) wireless systems. This paper proposes a hierarchical network architecture tailored for cell-free massive MIMO, seamlessly integrating co-located and distributed antennas. A central base station (CBS), equipped with an antenna array, positions itself near the center of the coverage area, complemented by distributed access points spanning the periphery. The proposed architecture remarkably outperforms conventional cell-free networks, demonstrating superior sum throughput while maintaining a comparable worst-case per-user spectral efficiency. Meanwhile, the implementation cost associated with the fronthaul network is substantially diminished.

cs.IT↗

arXiv2024

A Secure and Robust Approach for Distance-Based Mutual Positioning of Unmanned Aerial Vehicles

Unmanned aerial vehicle (UAV) is becoming increasingly important in modern civilian and military applications. However, its novel use cases is bottlenecked by conventional satellite and terrestrial localization technologies, and calling for complementary solutions. Multi-UAV mutual positioning can be a potential answer, but its accuracy and security are challenged by inaccurate and/or malicious measurements. This paper proposes a novel, robust, and secure approach to address these issues.

eess.SP↗

arXiv2023

A Short Overview of 6G V2X Communication Standards

We are on the verge of a new age of linked autonomous cars with unheard-of user experiences, dramatically improved air quality and road safety, extremely varied transportation settings, and a plethora of cutting-edge apps. A substantially improved Vehicle-to-Everything (V2X) communication network that can simultaneously support massive hyper-fast, ultra-reliable, and low-latency information exchange is necessary to achieve this ambitious goal. These needs of the upcoming V2X are expected to be satisfied by the Sixth Generation (6G) communication system. In this article, we start by introducing the history of V2X communications by giving details on the current, developing, and future developments. We compare the applications of communication technologies such as Wi-Fi, LTE, 5G, and 6G. we focus on the new technologies for 6G V2X which are brain-vehicle interface, blocked-based V2X, and Machine Learning (ML). To achieve this, we provide a summary of the most recent ML developments in 6G vehicle networks. we discuss the security challenges of 6G V2X. We address the strengths, open challenges, development, and improving areas of further study in this field.

eess.SP↗

arXiv2023

Enabling Mobility-Oriented JCAS in 6G Networks: An Architecture Proposal

Sensing plays a crucial role in autonomous and assisted vehicular driving, as well as in the operation of autonomous drones. The traditional segregation of communication and onboard sensing systems in mobility applications is due to be merged using Joint Communication and Sensing (JCAS) in the development of the 6G mobile radio standard. The integration of JCAS functions into the future road traffic landscape introduces novel challenges for the design of the 6G system architecture. Special emphasis will be placed on facilitating direct communication between road users and aerial drones. In various mobility scenarios, diverse levels of integration will be explored, ranging from leveraging communication capabilities to coordinate different radars to achieving deep integration through a unified waveform. In this paper, we have identified use cases and derive five higher-level Tech Cases (TCs). Technical and functional requirements for the 6G system architecture for a device-oriented JCAS approach will be extracted from the TCs and used to conceptualize the architectural views.

cs.NI↗

arXiv2023

Coverage Hole Elimination System in Industrial Environment

The paper proposes a framework to identify and avoid the coverage hole in an indoor industry environment. We assume an edge cloud co-located controller that followers the Automated Guided Vehicle (AGV) movement on a factory floor over a wireless channel. The coverage holes are caused due to blockage, path-loss, and fading effects. An AGV in the coverage hole may lose connectivity to the edge-cloud and become unstable. To avoid connectivity loss, we proposed a framework that identifies the position of coverage hole using a Support- Vector Machine (SVM) classifier model and constructs a binary coverage hole map incorporating the AGV trajectory re-planning to avoid the identified coverage hole. The AGV's re-planned trajectory is optimized and selected to avoid coverage hole the shortest coverage-hole-free trajectory. We further investigated the look-ahead time's impact on the AGV's re-planned trajectory performance. The results reveal that an AGV's re-planned trajectory can be shorter and further optimized if the coverage hole position is known ahead of time

cs.NI↗