arXiv subjects

Peter Rost

Publications and source records attributed to Peter Rost.

18 recordsLinked to original sources

arXiv2026

Physical Layer Message Prediction for 5G Radio Access Network Protocols

Protocol reverse engineering stands as the cutting-edge approach in security research. This paper presents a framework capable of reverse engineering the communications within a mobile communication system. Our focus is on systems released by the 3GPP, with an emphasis on 5G NR. Our approach leverages the available context and syntax of the 5G standard to predict subsequent messages. This approach relies on a Transformer model and is trained based on an open-source 5G system implementation, emulating a base station and several user equipments. The prediction targets messages at the physical layer.

cs.NI↗

arXiv2025

Packet-Level Traffic Modeling with Heavy-Tailed Payload and Inter-Arrival Distributions for Digital Twins

Digital twins of radio access networks require packet-level traffic generators that reproduce the size and timing of packets while remaining compact and easy to recalibrate as traffic changes. We address this need with a hybrid generator that combines a small hidden Markov model, which captures buffering, streaming, and idle states, with a mixture density network that models the joint distribution of payload length and inter-arrival time (IAT) in each state using Student-t mixtures. The state space and emission family are designed to handle heavy-tailed IAT by anchoring an explicit idle state in the tail and allowing each component to adapt its tail thickness. We evaluate the model on public traces of web, smart home, and encrypted media traffic and compare it with recent neural network and transformer based generators as well as hidden Markov baselines. Across most datasets and metrics, including average per-flow cumulative distribution functions, autocorrelation based measures of temporal structure, and Wasserstein distances between flow descriptors, the proposed generator matches the real traffic most closely in the majority of cases while using orders of magnitude fewer parameters. The full model occupies around 0.2 MB in our experiments, which makes it suitable for deployment inside digital twins where memory footprint and low-overhead adaptation are critical.

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arXiv2025

State Aware Traffic Generation for Real-Time Network Digital Twins

Digital twins (DTs) enable smarter, self-optimizing mobile networks, but they rely on a steady supply of real world data. Collecting and transferring complete traces in real time is a significant challenge. We present a compact traffic generator that combines hidden Markov model, capturing the broad rhythms of buffering, streaming and idle periods, with a small feed forward mixture density network that generates realistic payload sizes and inter-arrival times to be fed to the DT. This traffic generator trains in seconds on a server GPU, runs in real time and can be fine tuned inside the DT whenever the statistics of the generated data do not match the actual traffic. This enables operators to keep their DT up to date without causing overhead to the operational network. The results show that the traffic generator presented is able to derive realistic packet traces of payload length and inter-arrival time across various metrics that assess distributional fidelity, diversity, and temporal correlation of the synthetic trace.

cs.NI↗

arXiv2025

A Deep Reinforcement Learning-based Approach for Adaptive Handover Protocols

The use of higher frequencies in mobile communication systems leads to smaller cell sizes, resulting in the deployment of more base stations and an increase in handovers to support user mobility. This can lead to frequent radio link failures and reduced data rates. In this work, we propose a handover optimization method using proximal policy optimization (PPO) to develop an adaptive handover protocol. Our PPO-based agent, implemented in the base stations, is highly adaptive to varying user equipment speeds and outperforms the 3GPP-standardized 5G NR handover procedure in terms of average data rate and radio link failure rate. Additionally, our simulation environment is carefully designed to ensure high accuracy, realistic user movements, and fair benchmarking against the 3GPP handover method.

cs.NI↗

arXiv2023

Resource Allocation with Stability Constraints of an Edge-cloud controlled AGV

The paper proposes Resource Allocation (RA) schemes for a closed loop feedback control system by analysing the control-communication dependencies. We consider an Automated Guided Vehicle (AGV) that communicates with a controller located in an edge-cloud over a wireless fading channel. The control commands are transmitted to an AGV and the position state is feedback to the controller at every time-instant. A control stability based scheduling metric 'Probability of Instability' is evaluated for the resource allocation. The performance of stability based RA scheme is compared with the maximum SNR based RA scheme and control error first approach in an overloaded and non-overloaded scenario. The RA scheme with the stability constraints significantly reduces the resource utilization and is able to schedule more number of AGVs while maintaining its stability. Moreover, the proposed RA scheme is independent of control state and depends upon consecutive packet errors, the control parameters like sampling time and AGV velocity. Furthermore, we also analyse the impact of RA schemes on the AGV's stability and error performance, and evaluated the number of unstable AGVs.

eess.SY↗

arXiv2021

Error Convergence Analysis and Stability of a Cloud Control AGV

In this paper, we present a cloud based Automated Guided vehicle (AGV) control system. A controller in an Edge cloud sends the control inputs to an AGV to follow a predefined reference track over a wireless channel. The AGV feedback the position update via uplink channel. The objective of this paper is to evaluate the stability criterion of an AGV control system in presence of an uplink channel outages. Moreover, we also analyse the impact of feedback control parameters on the error convergence. The results show error convergence at higher rate with optimal selection of feedback parameters. The optimal feedback parameters that converges the error with critical damping is evaluated for two scenarios; with limited AGV velocity and without the limitation on AGV velocity. Furthermore, the paper describe the discretization process of a continuous control AGV system.

eess.SY↗

arXiv2021

Impact of Bit Allocation Strategies on Machine Learning Performance in Rate Limited Systems

Intelligent entities such as self-driving vehicles, with their data being processed by machine learning units (MLU), are developing into an intertwined part of networks. These units handle distorted input but their sensitivity to noisy observations varies for different input attributes. Since blind transport of massive data burdens the system, identifying and delivering relevant information to MLUs leads in improved system performance and efficient resource utilization. Here, we study the integer bit allocation problem for quantizing multiple correlated sources providing input of a MLU with a bandwidth constraint. Unlike conventional distance measures between original and quantized input attributes, a new Kullback-Leibler divergence based distortion measure is defined to account for accuracy of MLU decisions. The proposed criterion is applicable to many practical cases with no prior knowledge on data statistics and independent of selected MLU instance. Here, we examine an inverted pendulum on a cart with a neural network controller assuming scalar quantization. Simulation results present a significant performance gain, particularly for regions with smaller available bandwidth. Furthermore, the pattern of successful rate allocations demonstrates higher relevancy of some features for the MLU and the need to quantize them with higher accuracy.

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arXiv2020

Integration of 5G with TSN as Prerequisite for a Highly Flexible Future Industrial Automation: Time Synchronization based on IEEE 802.1AS

Industry 4.0 brings up new types of use cases, whereby mobile use cases play a significant role. These use cases have stringent requirements on both automation and communication systems that cannot be achieved with recent shop floor technologies. Therefore, novel technologies such as IEEE time-sensitive networking (TSN) and Open Platform Communications Unified Architecture (OPC UA) are being introduced. In addition, for the realization of mobile use cases, wireline technologies cannot be used and have to be replaced by wireless connections, which have to meet the high demands of the industrial landscape. Here, 5th generation wireless communication system (5G) is seen as a promising candidate. Especially encouraging and similarly challenging is the cooperative work of mobile robots, where particularly high demands on time synchronization arise. Therefore, this paper introduces a concept for the integration of TSN time synchronization (IEEE 802.1AS) conform with 5G to fulfill the requirements of these use cases. Furthermore, the paper describes a testbed for discrete manufacturing, consisting pre-dominantly of industrial equipment, in order to evaluate the presented approach.

cs.NI↗

arXiv2020

Impact of Short Blocklength Coding on Stability of an AGV Control System in Industry 4.0

With the advent of 5G and beyond, using wireless communication for closed-loop control and automation processes is one of the main aspects of the envisioned Industry 4.0. In this regard, a major challenge is to ensure a robust and stable control system over an unreliable wireless channel. One of the main use-cases in this context is Automated Guided Vehicle (AGV) control in a future factory. Specifically, we consider a system where an AGV controller is placed in an edge cloud in the factory network infrastructure and the control commands are sent over a time-correlated Rayleigh fading channel. In an industrial control, short packets are exchanged between the controller and the actuator. Therefore, in this case, Shannon's assumption for an infinite block length is not applicable. The objective is to analyse the stability performance of an AGV control system in a Finite Block-Length (FBL) regime. We evaluate the coding rate required to maintain a stable edge cloud based AGV system. The results illustrate that adapting the control parameters can lower the stringent requirements on the coding rate. It reveals that a constant stability performance can be achieved even at higher coding rate by increasing the AGV's velocity. Moreover, this paper also determines the maximum number of AGVs that can be served seamlessly over the available communication resources while maintaining a stable control system.

eess.SY↗

arXiv2019

Cloud Control AGV over Rayleigh Fading Channel -- The Faster The Better

This paper analyzes the stability of the control system of an Autonomous Guided Vehicle (AGV) using a central controller. The control commands are transmitted to an AGV over a Rayleigh fading channel causing potential packet drops. This paper analyzes the mutual dependencies of control system and mobile communication system. Among the important parameters considered are the sampling time of the discrete control system, the maximum tolerable outages for the control system, the AGV velocity, the number of users, as well as mobile communication channel conditions. It is shown that increasing the velocity of an AGV leads to a lower risk of instability due to the higher time-variance of the mobile channel. While this still is a 'sandbox' example, it shows the potential for a manifold co-optimization of control systems operated over imperfect mobile communication channels.

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arXiv2018

Delay constrained Energy Optimization for Edge Cloud Offloading

Resource limited user-devices may offload computation to a cloud server, in order to reduce power consumption and lower the execution time. However, to communicate to the cloud server over a wireless channel, additional energy is consumed for transmitting the data. Also a delay is introduced for offloading the data and receiving the response. Therefore, an optimal decision needs to be made that would reduce the energy consumption, while simultaneously satisfying the delay constraint. In this paper, we obtain an optimal closed form solution for these decision variables in a multi-user scenario. Furthermore, we optimally allocate the cloud server resources to the user devices, and evaluate the minimum delay that the system can provide, for a given bandwidth and number of user devices.

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arXiv2017

Device-centric Energy Optimization for Edge Cloud Offloading

A wireless system is considered, where, computationally complex algorithms are offloaded from user devices to an edge cloud server, for the purpose of efficient battery usage. The main focus of this paper is to characterize and analyze, the trade-off between the energy consumed for processing the data locally, and for offloading. An analytical framework is presented, that minimizes the in-device energy consumption, by providing an optimal offloading decision for multiple user devices. A closed form solution is obtained for the offloading decision. The solution also provides the amount of computational data that should be offloaded, for the given computational and communication resources. Consequently, reduction in the energy consumption is observed.

cs.DC↗

arXiv2015

Computationally Aware Sum-Rate Optimal Scheduling for Centralized Radio Access Networks

In a centralized or cloud radio access network, certain portions of the digital baseband processing of a group of several radio access points are executed at a central data center. Centralizing the processing improves the flexibility, scalability, and utilization of computational assets. However, the performance depends critically on how the limited data processing resources are allocated to serve the needs of the different wireless devices. As the processing load imposed by each device depends on its allocated transmission rate and channel quality, the rate-allocation aspect of the scheduling should take into account the available computing. In this paper, two computationally aware schedulers are proposed that have the objective of maximizing the sum-rate of the system while satisfying a constraint on the offered computational load. The first scheduler optimally allocates resources and is implemented according to a water-filling algorithm. The second scheduler is suboptimal, but uses a simpler and intuitive complexity-cut-off approach. The performance of both schedulers is evaluated using an LTE-compliant system level simulator. It is found that both schedulers avoid outages that are caused by an overflow of computational load (i.e., computational outages) at the cost of a slight loss of sum-rate.

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arXiv2015

The Complexity-Rate Tradeoff of Centralized Radio Access Networks

In a centralized RAN, the signals from multiple RAPs are processed centrally in a data center. Centralized RAN enables advanced interference coordination strategies while leveraging the elastic provisioning of data processing resources. It is particularly well suited for dense deployments, such as within a large building where the RAPs are connected via fibre and many cells are underutilized. This paper considers the computational requirements of centralized RAN with the goal of illuminating the benefits of pooling computational resources. A new analytical framework is proposed for quantifying the computational load associated with the centralized processing of uplink signals in the presence of block Rayleigh fading, distance-dependent path-loss, and fractional power control. Several new performance metrics are defined, including computational outage probability, outage complexity, computational gain, computational diversity, and the complexity-rate tradeoff. The validity of the analytical framework is confirmed by comparing it numerically with a simulator compliant with the 3GPP LTE standard. Using the developed metrics, it is shown that centralizing the computing resources provides a higher net throughput per computational resource as compared to local processing.

cs.IT↗

arXiv2015

Are Heterogeneous Cloud-Based Radio Access Networks Cost Effective?

Mobile networks of the future are predicted to be much denser than today's networks in order to cater to increasing user demands. In this context, cloud based radio access networks have garnered significant interest as a cost effective solution to the problem of coping with denser networks and providing higher data rates. However, to the best knowledge of the authors, a quantitative analysis of the cost of such networks is yet to be undertaken. This paper develops a theoretic framework that enables computation of the deployment cost of a network (modeled using various spatial point processes) to answer the question posed by the paper's title. Then, the framework obtained is used along with a complexity model, which enables computing the information processing costs of a network, to compare the deployment cost of a cloud based network against that of a traditional LTE network, and to analyze why they are more economical. Using this framework and an exemplary budget, this paper shows that cloud-based radio access networks require approximately 10 to 15% less capital expenditure per square kilometer than traditional LTE networks. It also demonstrates that the cost savings depend largely on the costs of base stations and the mix of backhaul technologies used to connect base stations with data centers.

cs.NI↗

arXiv2014

The Role of Computational Outage in Dense Cloud-Based Centralized Radio Access Networks

Centralized radio access network architectures consolidate the baseband operation towards a cloud-based platform, thereby allowing for efficient utilization of computing assets, effective inter-cell coordination, and exploitation of global channel state information. This paper considers the interplay between computational efficiency and data throughput that is fundamental to centralized RAN. It introduces the concept of computational outage in mobile networks, and applies it to the analysis of complexity constrained dense centralized RAN networks. The framework is applied to single-cell and multi-cell scenarios using parameters drawn from the LTE standard. It is found that in computationally limited networks, the effective throughput can be improved by using a computationally aware policy for selecting the modulation and coding scheme, which sacrifices spectral efficiency in order to reduce the computational outage probability. When signals of multiple base stations are processed centrally, a computational diversity benefit emerges, and the benefit grows with increasing user density.

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arXiv2010

Protocols and Performance Limits for Half-Duplex Relay Networks

In this paper, protocols for the half-duplex relay channel are introduced and performance limits are analyzed. Relay nodes underly an orthogonality constraint, which prohibits simultaneous receiving and transmitting on the same time-frequency resource. Based upon this practical consideration, different protocols are discussed and evaluated using a Gaussian system model. For the considered scenarios compress-and-forward based protocols dominate for a wide range of parameters decode-and-forward protocols. In this paper, a protocol with one compress-and-forward and one decode-and-forward based relay is introduced. Just as the cut-set bound, which operates in a mode where relays transmit alternately, both relays support each other. Furthermore, it is shown that in practical systems a random channel access provides only marginal performance gains if any.

cs.IT↗

arXiv2010

Application Driven Joint Uplink-Downlink Optimization in Wireless Communications

This paper introduces a new mathematical framework, which is used to derive joint uplink/downlink achievable rate regions for multi-user spatial multiplexing between one base station and multiple terminals. The framework consists of two models: the first one is a simple transmission model for uplink and downlink, which is capable to give a lower bound on the capacity for the case that the transmission is subject to imperfect CSI. A detailed model for concrete channel estimation and feedback schemes provides parameter input to the former model and covers the most important aspects such as pilot design optimization, linear channel estimation, feedback delay, and feedback quantization. We apply this framework to determine optimal pilot densities and CSI feedback quantity, given that a weighted sum of uplink and downlink throughput is to be maximized for a certain user velocity. We show that for low speed, and if downlink throughput is of particular importance, a significant portion of the uplink should be invested into CSI feedback. At higher velocity, however, downlink performance becomes mainly affected by CSI feedback delay, and hence CSI feedback brings little gain considering the inherent sacrifice of uplink capacity. We further show that for high velocities, it becomes beneficial to use no CSI feedback at all, but apply random beamforming in the downlink and operate in time-division duplex.

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