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Andreas Weinand

Publications and source records attributed to Andreas Weinand.

At least 19 recordsLinked to original sources
arXiv2026

Towards Energy Impact on AI-Powered 6G IoT Networks: Centralized vs. Decentralized

The emergence of sixth-generation (6G) technologies has introduced new challenges and opportunities for machine learning (ML) applications in Internet of Things (IoT) networks, particularly concerning energy efficiency. As model training and data transmission contribute significantly to energy consumption, optimizing these processes has become critical for sustainable system design. This study first conduct analysis on the energy consumption model for both centralized and decentralized architecture and then presents a testbed deployed within the German railway infrastructure, leveraging sensor data for ML-based predictive maintenance. A comparative analysis of distributed versus Centralized Learning (CL) architectures reveals that distributed models maintain competitive predictive accuracy (~90%) while reducing overall electricity consumption by up to 70%. These findings underscore the potential of distributed ML to improve energy efficiency in real-world IoT deployments, particularly by mitigating transmission-related energy costs.

cs.AI↗
arXiv2026

Deep Learning-Based Physical Layer Authentication Using 5G NR Sounding Reference Signals: A Temporal Generalization Study on Real Testbed Data

Physical Layer Authentication (PLA) exploits the spatial uniqueness of wireless channel characteristics in order to authenticate devices without recourse to higher-layer cryptographic protocols, which remain vulnerable to key compromise. This paper reports a comprehensive PLA system constructed on 5G New Radio (NR) Sounding Reference Signals (SRS) extracted from a real OpenAirInterface (OAI) testbed operating in band n78 (3.5 GHz) with 40 MHz bandwidth and 30 kHz subcarrier spacing. The proposed approach extracts a 2,531-dimensional feature vector per SRS probe, combining per-subcarrier channel state information (1,248 amplitude and 1,247 differential-phase coefficients), power delay profile taps, delay spread, Doppler statistics, and nonlinear dynamics indicators. A deep one-dimensional Residual Network (1D-ResNet) augmented with Squeeze-and-Excitation (SE) attention blocks is employed to classify each probe as either legitimate or spoofed. Evaluation is conducted on 20,317 over-the-air SRS probes acquired across four measurement sessions using a USRP B210 software-defined radio as the legitimate device and a commercial mobile handset as the attacker. Under a strict chronological train/validation/test split that eliminates temporal leakage, an Equal Error Rate (EER) of 3.92% is attained, with AUC = 0.962 on the held-out test set, and an authentication latency of less than 0.1 ms per probe, which is compatible with 5G Ultra-Reliable Low-Latency Communications (URLLC) requirements.

eess.SP↗
arXiv2020

Channel Estimation in C-V2X using Deep Learning

Channel estimation forms one of the central component in current OFDM systems that aims to eliminate the inter-symbol interference by calculating the CSI using the pilot symbols and interpolating them across the entire time-frequency grid. It is also one of the most researched field in the PHY with LS and MMSE being the two most used methods. In this work, we investigate the performance of deep neural network architecture based on CNN for channel estimation in vehicular environments used in 3GPP Rel.14 CV2X technology. To this end, we compare the performance of the proposed DL architectures to the legacy LS channel estimation currently employed in C-V2X. Initial investigations prove that the proposed DL architecture outperform the legacy CV2X channel estimation methods especially at high mobile speeds

eess.SP↗
arXiv2020

Link Level Performance Comparison of C-V2X and ITS-G5 for Vehicular Channel Models

V2X communications plays a significant role in increasing traffic safety and efficiency by enabling vehicles to exchange their status information with other vehicles and traffic entities in their proximity. In this regard, two technologies emerged as the main contenders for enabling V2X communications which have stringent requirements in terms of latency and reliability due to their apparent safety criticality. The first one is the DSRC standard (referred to as ITS-G5 in Europe) that is well researched since 20 years and has attained enough technical maturity for current deployment. The second one is the relatively new CV2X standard that is nevertheless, based on the 3GPP standard family that have successful deployments in almost every corner of the globe. In this work, we compare the link level performance of the PHY protocols for both the technologies for different vehicular fading channel models. To this end, we construct and simulate the PHY pipelines and show the performance results by means of BLER} versus SNR graphs. Our investigations show that CV2X performs better than ITS-G5 for almost all the considered channel models due to better channel coding and estimation schemes.

eess.SP↗
arXiv2019

Supervised Learning for Physical Layer based Message Authentication in URLLC scenarios

PHYSEC based message authentication can, as an alternative to conventional security schemes, be applied within \gls{urllc} scenarios in order to meet the requirement of secure user data transmissions in the sense of authenticity and integrity. In this work, we investigate the performance of supervised learning classifiers for discriminating legitimate transmitters from illegimate ones in such scenarios. We further present our methodology of data collection using \gls{sdr} platforms and the data processing pipeline including e.g. necessary preprocessing steps. Finally, the performance of the considered supervised learning schemes under different side conditions is presented.

eess.SP↗
arXiv2019

AI-assisted PHY technologies for 6G and beyond wireless networks

Machine Learning (ML) and Artificial Intelligence(AI) have become alternative approaches in wireless networksbeside conventional approaches such as model based solutionconcepts. Whereas traditional design concepts include the mod-elling of the behaviour of the underlying processes, AI basedapproaches allow to design network functions by learning frominput data which is supposed to get mapped to specific outputs(training). Additionally, new input/output relations can be learntduring the deployement phase of the function (online learning)and make AI based solutions flexible, in order to react to newsituations. Especially, new introduced use cases such as UltraReliable Low Latency Communication (URLLC) and MassiveMachine Type Communications (MMTC) in 5G make this ap-proach necessary, as the network complexity is further enhancedcompared to networks mainly designed for human driven traffic(4G, 5G xMBB). The focus of this paper is to illustrate exemplaryapplications of AI techniques at the Physical Layer (PHY) offuture wireless systems and therfore they can be seen as candidatetechnologies for e.g. 6G systems.

eess.SP↗
arXiv2019

A Controller for Network-Assisted CACC based Platooning

Platooning involves a set of vehicles moving in a cooperative fashion at equal inter-vehicular distances. Taking advantage of wireless communication technology, this paper aims to show the impact of network protocols on a platoon using a controller, based on the Cooperative Adaptive Cruise Control (CACC) principles. The network protocols used in this work are DSRC (Dedicated Short Range Communication) and LTE-V2V sidelink (Mode 4). The main focus of this work is to showcase the ability of the controller to maintain platoon stability despite having uncertainties in both, the platoon and the message delivery rates over the network protocols. The controller interacts with all vehicles using messages transmitted over the network protocols. The controller is designed to be responsible for micro-managing every vehicle in the platoon and to ensure that the platoon does not break under any circumstances. SUMO (Simulation of Urban MObility) is used as the simulation platform. Results indicate, that the controller manages to achieve platoon stability in all scenarios, unless a set number of consecutive messages are not transmitted, in which case it leads to collisions. This work also presents certain bottlenecks pertaining to wireless communication with vehicles.

cs.NI↗
arXiv2019

System-Level Simulator of LTE Sidelink C-V2X Communication for 5G

In recent years, Cellular-Vehicle-to-Everything (CV2X) has been an emerging area of interest attracting both the industry and academy societies to develop, which is also a prominent emerging service for the next generation of the cellular network (5G). In the time of the development, standardization, and further improvement of 5G, so simulations are essential to test and optimize algorithms and procedures prior to their implementation process of the equipment manufactures. And CV2X communication is used for information exchange among the traffic participants with network-assisted which can reduce traffic accidents and improve traffic efficiency. Moreover, it is also the primary enabler for cooperative driving. But CV2X communication has to meet different Quality of Service (QoS) requirements (e.g., ultra-high reliability (99.999%) and ultra-low latency). Guaranteeing high-level reliability is a big challenge. In order to assess system performance, accurate simulations of simple setups, as well as simulations of more complex systems via abstracted models are necessary for the CV2X communication. For checking the performance of the C-V2X communication on a highway scenario, a system-level simulator has been implemented. And, this simulation has been carried out on the network (system-level) context. Finally, the analysis and the simulation results for the C-V2X communication are presented, which shows that different objectives can be met via system-level simulation.

cs.NI↗
arXiv2018

Performance Analysis of Deep Learning based on Recurrent Neural Networks for Channel Coding

Channel Coding has been one of the central disciplines driving the success stories of current generation LTE systems and beyond. In particular, turbo codes are mostly used for cellular and other applications where a reliable data transfer is required for latency-constrained communication in the presence of data-corrupting noise. However, the decoding algorithm for turbo codes is computationally intensive and thereby limiting its applicability in hand-held devices. In this paper, we study the feasibility of using Deep Learning (DL) architectures based on Recurrent Neural Networks (RNNs) for encoding and decoding of turbo codes. In this regard, we simulate and use data from various stages of the transmission chain (turbo encoder output, Additive White Gaussian Noise (AWGN) channel output, demodulator output) to train our proposed RNN architecture and compare its performance to the conventional turbo encoder/decoder algorithms. Simulation results show, that the proposed RNN model outperforms the decoding performance of a conventional turbo decoder at low Signal to Noise Ratio (SNR) regions

eess.SP↗
arXiv2018

Security Solutions for Local Wireless Networks in Control Applications based on Physical Layer Security

The Design of new wireless communication systems for industrial applications, e.g. control applications, is currently a hot research topic, as they deal as a key enabler for more flexible solutions at a lower cost compared to systems based on wired communication. However, one of their main drawbacks is, that they provide a huge potential for miscellaneous cyber attacks due to the open nature of the wireless channel in combination with the huge economic potential they are able to provide. Therefore, security measures need to be taken into account for the design of such systems. Within this work, an approach for the security architecture of local wireless systems with respect to the needs of control applications is presented and discussed. Further, new security solutions based on Physical Layer Security are introduced in order to overcome the drawbacks of state of the art security technologies within that scope.

cs.NI↗
arXiv2018

Multi-RATs Support to Improve V2X Communication

As the next generation of wireless system targets at providing a wider range of services with divergent QoS requirements, new applications will be enabled by the fifth generation (5G) network. Among the emerging applications, vehicle-to-everything (V2X) communication is an important use case targeted by 5G to enable an improved traffic safety and traffic efficiency. Since the V2X communication requires a low end-to-end (E2E) latency and an ultra-high reliability, the legacy cellular networks can not meet the service requirement. In this work, we inspect on the system performance of applying the LTE-Uu and PC5 interfaces to enable the V2X communication. With the LTE-Uu interface, one V2X data packet is transmitted through the cellular network infrastructure, while the PC5 interface facilitates the direct V2X communication without involving the network infrastructure in user-plane. In addition, due to the high reliability requirement, the application of a single V2X transmission technology can not meet the targets in some scenarios. Therefore, we also propose a multi-radio access technologies (multi-RATs) scheme where the data packet travels through both the LTE-Uu and PC5 interfaces to obtain a diversity gain. Last but not least, in order to derive the system performance, a system level simulator is implemented in this work. The numerical results provide us insights on how the different technologies will perform in different scenarios and also validate the proposed multi-RATs scheme.

cs.NI↗
arXiv2018

On Partly Overloaded Spreading Sequences with Variable Spreading Factor

Future wireless communications systems are expected to support multi-service operation, i.e. especially multi-rate as well as multi-level quality of service (QoS) requirements. This evolution is mainly driven by the success of the Internet of Things (IoT) and the growing presence of machine type communication (MTC). Whereas in the last years information in wireless communication systems was mainly generated or at least requested by humans and was also processed by humans, we can now see a paradigm shift since so-called machine type communication is gaining growing importance. Along with these changes we also encounter changes regarding the quality of service requirements, data rate requirements, latency constraints, different duty cycles et cetera. The challenge for new communication systems will therefore be to enable different user types and their different requirements efficiently. In this paper, we present partly overloaded spreading sequences, i.e. sequences which are globally orthogonal and sequences which interfere with a subset of sequences while being orthogonal to the globally orthogonal sequences. Additionally, we are able to vary the spreading factor of these sequences, which allows us to flexibly assign appropriate sequences to different service types or user types respectively. We propose the use of these sequences for a CDMA channel access method which is able to flexibly support different traffic types.

eess.SP↗
arXiv2017

Application of Machine Learning for Channel based Message Authentication in Mission Critical Machine Type Communication

The design of robust wireless communication systems for industrial applications such as closed loop control processes has been considered manifold recently. Additionally, the ongoing advances in the area of connected mobility have similar or even higher requirements regarding system reliability and availability. Beside unfulfilled reliability requirements, the availability of a system can further be reduced, if it is under attack in the sense of violation of information security goals such as data authenticity or integrity. In order to guarantee the safe operation of an application, a system has at least to be able to detect these attacks. Though there are numerous techniques in the sense of conventional cryptography in order to achieve that goal, these are not always suited for the requirements of the applications mentioned due to resource inefficiency. In the present work, we show how the goal of message authenticity based on physical layer security (PHYSEC) can be achieved. The main idea for such techniques is to exploit user specific characteristics of the wireless channel, especially in spatial domain. Additionally, we show the performance of our machine learning based approach and compare it with other existing approaches.

cs.NI↗
arXiv2017

Physical Layer Authentication for Mission Critical Machine Type Communication using Gaussian Mixture Model based Clustering

The application of Mission Critical Machine Type Communication (MC-MTC) in wireless systems is currently a hot research topic. Wireless systems are considered to provide numerous advantages over wired systems in e.g. industrial applications such as closed loop control. However, due to the broadcast nature of the wireless channel, such systems are prone to a wide range of cyber attacks. These range from passive eavesdropping attacks to active attacks like data manipulation or masquerade attacks. Therefore it is necessary to provide reliable and efficient security mechanisms. Some of the most important security issues in such a system are to ensure integrity as well as authenticity of exchanged messages over the air between communicating devices. In the present work, an approach on how to achieve this goal in MC-MTC systems based on Physical Layer Security (PHYSEC) is presented. A new method that clusters channel estimates of different transmitters based on a Gaussian Mixture Model is applied for that purpose. Further, an experimental proof-of-concept evaluation is given and we compare the performance of our approach with a mean square error based detection method.

cs.NI↗
arXiv2017

Providing Physical Layer Security for Mission Critical Machine Type Communication

The design of wireless systems for Mission Critical Machine Type Communication (MC-MTC) is currently a hot research topic. Wireless systems are considered to provide numerous advantages over wired systems in industrial applications for example. However, due to the broadcast nature of the wireless channel, such systems are prone to a wide range of cyber attacks. These range from passive eavesdropping attacks to active attacks like data manipulation or masquerade attacks. Therefore it is necessary to provide reliable and efficient security mechanisms. One of the most important security issue in such a system is to ensure integrity as well as authenticity of exchanged messages over the air between communicating devices in order to prohibit active attacks. In the present work, an approach on how to achieve this goal in MC-MTC systems based on Physical Layer Security (PHYSEC), especially a new method based on keeping track of channel variations, will be presented and a proof-of-concept evaluation is given.

cs.NI↗
arXiv2017

Radio Link Enabler for Context-aware D2D Communication in Reuse Mode

Device-to-Device (D2D) communication is considered as one of the key technologies for the fifth generation wireless communication system (5G) due to certain benefits provided, e.g. traffic offload and low end-to-end latency. A D2D link can reuse resource of a cellular user for its own transmission, while mutual interference in between these two links is introduced. In this paper, we propose a smart radio resource management (RRM) algorithm which enables D2D communication to reuse cellular resource, by taking into account of context information. Besides, signaling schemes with high efficiency are also given in this work to enable the proposed RRM algorithm. Simulation results demonstrate the performance improvement of the proposed scheme in terms of the overall cell capacity.

cs.NI↗
arXiv2017

Direct Vehicle-to-Vehicle Communication with Infrastructure Assistance in 5G Network

Compared with today's 4G wireless communication network, the next generation of wireless system should be able to provide a wider range of services with different QoS requirements. One emerging new service is to exploit cooperative driving to actively avoid accidents and improve traffic efficiency. A key challenge for cooperative driving is on vehicle-to-vehicle (V2V) communication which requires a high reliability and a low end-to-end (E2E) latency. In order to meet these requirements, 5G should be evaluated by new key performance indicators (KPIs) rather than the conventional metric, as throughput in the legacy cellular networks. In this work, we exploit network controlled direct V2V communication for information exchange among vehicles. This communication process refers to packet transmission directly among vehicles without the involvement of network infrastructure in U-plane. In order to have a network architecture to enable direct V2V communication, the architecture of the 4G network is enhanced by deploying a new central entity with specific functionality for V2V communication. Moreover, a resource allocation scheme is also specifically designed to adapt to traffic model and service requirements of V2V communication. Last but not least, different technologies are considered and simulated in this work to improve the performance of direct V2V communication.

cs.NI↗