Showing papers by "Johan Torsner published in 2022"
DOI•
TL;DR: Evaluations for the deployment of 5G in realistic industrial production environments with realistic deployment settings conclude that the requirements for very low latencies can be achieved with high reliability guarantees, as required in some of the most stringent industrial IoT applications.
Abstract: Wireless- and 5G-enabled industrial automation is expected to include a plethora of different applications with a wide variety of requirements. In this article, evaluations are undertaken for the deployment of 5G in realistic industrial production environments with realistic deployment settings. Both deployments using commercial 5G systems and a 5G prototype system including pre-commercial and standard compliant URLLC functionality have been investigated. Systematic latency and reliability measurements were performed, over the air and in live networks, for different packet sizes, different devices, and networks with different capabilities (at different sites) to characterize the expected performance. The results indicate that today’s 5G latency performance significantly depends on packet size, transmission direction (uplink or downlink), and network configuration as well as on the end device’s design and capabilities. Our over-the-air measurements also empirically show that 5G technology and future networks have the capability of providing one-way latency of around 1 ms in both uplink and downlink for the various packet sizes tested. It is concluded that the requirements for very low latencies can be achieved with high reliability guarantees, as required in some of the most stringent industrial IoT applications.
26 citations
TL;DR: In this article , the authors developed a mobility-centric analytical methodology for multi-RAT deployments and characterized the convergence time of the distributed optimization algorithms based on reinforcement learning to reduce the signaling overheads.
4 citations
04 Jul 2022
TL;DR: Measurements in the form of time-of-flight and gNodeB angular information in the context of 5G New Radio networks are utilized to show that radio positioning is feasible with accuracy in the order of 1 meter, or even below, also in challenging NLOS scenarios if the data and measurement uncertainties are small.
Abstract: In this paper, we address the challenging problem of radio positioning in non-line-of-sight (NLoS) conditions. To this end, we utilize measurements in the form of time-of-flight and gNodeB angular information in the context of 5G New Radio (NR) networks. Such measurements are processed by artificial neural networks with different snapshot and sequence-processing architectures to track the positions of the terminals. For model training, we consider a crowdsensing data acquisition scheme to effortlessly gather the desired measurements with the synchronized location tags. Realistic ray-tracing based evaluations on the so-called Madrid map at 28 GHz millimeter-wave band are provided, to assess the achievable performance while also varying the amount of uncertainties within the data. The obtained results show that radio positioning is feasible with accuracy in the order of 1 meter, or even below, also in challenging NLOS scenarios if the data and measurement uncertainties are small. The results also show that the sequence processing approach offers superior performance under practical measurement uncertainties.
3 citations