@inproceedings{Busch2023,

title = {Deep Reinforcement Learning for the Joint Control of Traffic Light Signaling and Vehicle Speed Advice},

author = {Johannes V. S. {Busch} and Robert {Voelckner} and Peter {Sossalla} and Christian Leonard {Vielhaus} and Roberto {Calandra} and Frank H. P. {Fitzek}},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

journal = {IEEE International Conference on Machine Learning and Applications (ICMLA)},

publisher = {IEEE},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{jiajing2023HORA,

title = {HORA: Joint Handover and Resource Allocation on 3D Networks for Industrial IoT},

author = {Jiajing {Zhang} and Christian {Hartmann} and Peter {Sossalla} and Giang T. {Nguyen} and Frank H. P. {Fitzek}},

year  = {2023},

date = {2023-01-01},

urldate = {2023-01-01},

booktitle = {European Wireless (EW)},

address = {Rome, Italy},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Soss2212:Optimizing,

title = {Optimizing Edge SLAM: Judicious Parameter Settings and Parallelized Map Updates},

author = {Peter {Sossalla} and Johannes {Hofer} and Justus {Rischke} and Johannes V. S. {Busch} and Giang T. {Nguyen} and Martin {Reisslein} and Frank H. P. {Fitzek}},

doi = {10.1109/GLOBECOM48099.2022.10001128},

year  = {2022},

date = {2022-12-01},

urldate = {2022-12-01},

booktitle = {2022 IEEE Global Communications Conference: IoT and Sensor Networks 

(Globecom 2022 IoTSN)},

address = {Rio de Janeiro, Brazil},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Risc2211:Comparison,

title = {Comparison of UPF acceleration technologies and their tail-latency for URLLC},

author = {Justus {Rischke} and Christian Leonard {Vielhaus} and Peter {Sossalla} and Junyue {Wang} and Frank H. P. {Fitzek}},

doi = {10.1109/NFV-SDN56302.2022.9974798},

year  = {2022},

date = {2022-11-14},

urldate = {2022-11-14},

booktitle = {2022 IEEE Conference on Network Function Virtualization and Software 

Defined Networks (NFV-SDN) (NFV-SDN'22)},

address = {Phoenix, USA},

abstract = {The industry is striving to deploy private cellular networks in their 

factories. 

The envisioned use cases, such as industrial automation, require 

Ultra-Reliable and Low-Latency Communications (URLLC). 

In 5G, the User Plane Function (UPF) handles user traffic and hence 

significantly influences the performance in terms of latency and 

throughput. 

However, present UPFs are often user-space applications running on 

commercial off-the-shelf (COTS) servers introducing computational delays 

that follow long-tailed distributions. 

In this work, we compare modern packet processing acceleration technologies 

such as XDP, DPDK, and P4 and their respective tail-latency performance. 

Our results demonstrate the clear advantage of hardware-based P4 

implementations in terms of reduced tail-latency of about 6 μs at a 

percentile of 99.999% for small packets. 

Furthermore, we also demonstrate that software-based frameworks can compete 

with hardware-based approaches. 

For example, DPDK achieves tail-latencies of approximately 23 μs at a 

percentile of 99.999%. 

Based on our findings, we illustrate the trade-offs between low-cost 

software- and high-performant hardware-based packet processing systems.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Soss2206:5GMobile,

title = {Private 5G Solutions for Mobile Industrial Robots: A Feasibility Study},

author = {Peter {Sossalla} and Justus {Rischke} and Fabian {Baier} and Sebastian A. W. {Itting} and Giang T. {Nguyen} and Frank H. P. {Fitzek}},

doi = {10.1109/ISCC55528.2022.9912791},

year  = {2022},

date = {2022-10-19},

urldate = {2022-06-30},

booktitle = {27th IEEE Symposium on Computers and Communications (ISCC 2022)},

address = {Rhodes island, Greece},

abstract = {Mobile robots are an essential part of the vision of flexible production in a smart factory. To monitor and connect the robots, reliable and low latency communication is necessary. In this work, we conduct packet-based active measurements to evaluate the performance of a state-of-the-art 5G standalone system in a production environment. The focus is on whether 5G connections can meet the requirements specified by 3GPP in terms of delay and reliability. The results indicate that without cross-traffic, the requirement of a delay of less than 10 ms for 99.9 % of the packets can be met for the remote control and fleet management of mobile robots. However, as soon as cross-traffic is injected, especially in the uplink, the upper percentiles of the delay increase significantly, thus failing to hold the reliability requirements.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Risc2206:Empirical,

title = {Empirical Study of 5G Downlink \& Uplink Scheduling and its Effects on Latency},

author = {Justus {Rischke} and Christian Leonard {Vielhaus} and Peter {Sossalla} and Sebastian A. W. {Itting} and Giang T. {Nguyen} and Frank H. P. {Fitzek}},

doi = {10.1109/WoWMoM54355.2022.00017},

year  = {2022},

date = {2022-06-14},

urldate = {2022-06-14},

booktitle = {2022 IEEE 23rd International Symposium on a World of Wireless, Mobile and 

Multimedia Networks (WoWMoM) (WoWMoM 2022)},

address = {Belfast, United Kingdom (Great Britain)},

abstract = {5G campus networks, whose advantages include 

flexible deployment, can be a promising candidate for production 

plants to complement existing Wifi-based networks. Toward 

that goal, 5G has to satisfy strict requirements about real-time 

communication to facility novel use cases. However, the realtime- 

capability of 5G is not well understood yet. In this work, we 

deliver insights into the functioning of 5G NR RAN Release 

15, which includes actual one-way delay and Round-Trip Time 

(RTT) measurements for Downlink and Uplink in a private 5G 

Standalone campus network. The extensive measurement results 

reveal that these delays are correlated, and the corresponding 

RTT, i.e. the sum of Downlink and Uplink delays, is discreetly 

clustered, ranging between 12ms and 40ms. The measurements 

also show that the distribution of RTTs is mainly dependent on 

the packet rates and their inter-arrival times. Our study helps 

expand the current understanding of 5G used for latency-critical 

applications. We make the code and the measurement data traces 

publicly available as the IEEE DataPort 5G Campus Networks: 

Measurement Traces dataset (DOI 10.21227/xe3c-e968).},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}

@inproceedings{Soss2201:OWD,

title = {Enhanced One-Way Delay Monitoring with OpenFlow},

author = {Peter {Sossalla} and Justus {Rischke} and Frank H. P. {Fitzek}},

doi = { 10.1109/ICOIN53446.2022.9687289},

year  = {2022},

date = {2022-01-12},

urldate = {2022-01-12},

booktitle = {2022 IEEE 36th International Conference on Information Networking (ICOIN 2022)},

address = {Jeju Island, Korea},

abstract = {Network monitoring is an important task in future networks to mitigate outages, guarantee low latency and high throughput, and quickly adapt to changes of network conditions. Software-Defined Networking (SDN) enables dynamic adaptation to changing network environments through the ability to program network behavior. However, to react properly to changing environments, precise and credible information about performance metrics such as latency is necessary. Recent research activities exploited OpenFlow, an SDN protocol, to measure delays inside the network, without the need for additional hard- or software. In this paper, we propose an improved method to measure one-way delays, which can handle asymmetrical delays between the SDN controller and the switches. We implemented our approach in a hardware testbed to evaluate it in a setup with asymmetrical delays and traffic loads. The results show that we are able to measure asymmetrical delays in the network. Additionally, the proposed solution is capable of discovering the network topology and therefore gives together with the performance metrics the information for efficient routing decisions. In addition, we make the source code of our implementation publicly available.},

keywords = {},

pubstate = {published},

tppubtype = {inproceedings}

}