5G New Radio for Public Safety Mission Critical Communications

Li, Jingya; Nagalapur, Keerthi Kumar; Stare, Erik; Dwivedi, Satyam; Ashraf, Shehzad; Eriksson, Per; Engström, Ulrika; Lee, Woonghee; Lohmar, Thorsten

doi:10.1109/mcomstd.0002.2100036

Cited by 21 publications

(7 citation statements)

References 7 publications

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“…Additionally, mission-critical users have the ability to utilize the available licensed and unlicensed channels, sharing them with Wi-Fi networks. The achievable bit rate for (1) Where 𝐿𝐿 𝑚𝑚,𝑠𝑠 is the number of session generated by mission-critical user m in cell n, and 𝛿𝛿s is the size of each session s generated by user m in cell n. 𝑇𝑇 𝑠𝑠 is the duration of each session s generated by user m. The total traffic demand for cell n, given by 𝐷𝐷 𝑛𝑛 (𝑡𝑡) can then be determined as the aggregate of the traffic demands of all users accessing cell n as follows:…”

Section: System Modelmentioning

confidence: 99%

“…The increasing demand for mission-critical communication services such as public safety, transportation, and healthcare that require consistent and efficient communications during emergencies and mission-critical situations has been experienced over the past few years [1,2]. The mission-critical services require persistent connectivity, low latency, and high reliability to guarantee a safe environment, and the smooth operation of critical systems.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Reinforcement Learning–based Seamless Multi-Connectivity Solution for Heterogeneous 5GNR and Wi-Fi Networks

2024

IJIES

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The integration of 5G New Radio (5G-NR) and Wi-Fi networks in heterogeneous environments holds significant promise for delivering critical services with relatively high reliability and low latency. In this paper, we tackle the critical challenge of establishing robust multi-connectivity within heterogeneous systems integrating 5G NR and Wi-Fi systems. In this context, our focus is on optimizing a new, distributed multi-Connectivity solution, with a main emphasis on identifying the ideal base stations (BSs) and Wi-Fi access points (APs) with their respective spectrum associations. This solution is essential for ensuring low-latency and reliable communication required, particularly for mission-critical services. The simulation results demonstrate the significant advantages of the proposed algorithm compared to reference schemes. It leads to network performance improvements by enhancing system utility function, reducing service access time, and increasing packet success rates, through high-quality connectivity with relatively low interference probability. In particular, for mission-critical services, the proposed algorithm leads to a 16 % and 41% increase in effective network throughput when the offered load is 190 Mbps, which outperforms the benchmark approaches. Furthermore, it improves the utility function by approximately 0.12, and 0.19 compared to the reference approaches. This reflects a substantial improvements in success delivery rates for mission-critical services by 10 % and 18 %, respectively, when compared to the reference approaches, particularly evident at an offered load of approximately 190Mbps.

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Section: System Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Reinforcement Learning–based Seamless Multi-Connectivity Solution for Heterogeneous 5GNR and Wi-Fi Networks

2024

IJIES

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“…Facilitating the interaction of PPDR vertical systems with 5G services is imperative to demonstrate to relevant operators and organizations that the new network is indeed MC capable (Critical Communications Association, 2019). To further accelerate the transition to broadband, it is also important to demonstrate advanced MC applications, which offer enhanced capabilities, and are able to address known challenges and current capability gaps inherent in the use of the current narrowband PPDR networks (Li et al, 2022). For this reason, one of the key objectives of the 5G-EPICENTRE project has been to demonstrate 5G features for a diverse range of MC applications and modern enablers, including real-time group communications, situational awareness platforms, and augmented reality solutions.…”

Section: Qos Management For Ppdr Vertical Systems Via Network Applica...mentioning

confidence: 99%

A network application approach towards 5G and beyond critical communications use cases

Apostolakis,

Valera-Muros,

di Pietro

et al. 2024

Front. Comms. Net.

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Low latency and high bandwidth heralded with 5G networks will allow transmission of large amounts of Mission-Critical data over a short time period. 5G hence unlocks several capabilities for novel Public Protection and Disaster Relief (PPDR) applications, developed to support first responders in making faster and more accurate decisions during times of crisis. As various research initiatives are giving shape to the Network Application ecosystem as an interaction layer between vertical applications and the network control plane, in this article we explore how this concept can unlock finer network service management capabilities that can be leveraged by PPDR solution developers. In particular, we elaborate on the role of Network Applications as means for developers to assure prioritization of specific emergency flows of data, such as high-definition video transmission from PPDR field users to remote operators. To demonstrate this potential in future PPDR-over-5G services, we delve into the transfer of network-intensive PPDR solutions to the Network Application model. We then explore novelties in Network Application experimentation platforms, aiming to streamline development and deployment of such integrated systems across existing 5G infrastructures, by providing the reliability and multi-cluster environments they require.

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“…, M }. The users are assumed to be located in a 2D plane and have high quality of service requirements [43]. The geographical locations of the users are denoted by y = [y 1 , y 2 , ..., y M ] T , where…”

Section: System Modelmentioning

confidence: 99%

Resource Scheduling Based on Deep Reinforcement Learning in UAV Assisted Emergency Communication Networks

Wang

Deng

et al. 2022

IEEE Trans. Commun.

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The advent of fifth generation (5G) networks has opened new avenues for enhancing connectivity, particularly in challenging environments like remote areas or disaster-struck regions. Unmanned aerial vehicles (UAVs) have been identified as a versatile tool in this context, particularly for improving network performance through the Integrated access and backhaul (IAB) feature of 5G. However, existing approaches to UAV-assisted network enhancement face limitations in dynamically adapting to varying user locations and network demands. This paper introduces a novel approach leveraging deep reinforcement learning (DRL) to optimize UAV placement in real-time, dynamically adjusting to changing network conditions and user requirements. Our method focuses on the intricate balance between fronthaul and backhaul links, a critical aspect often overlooked in current solutions. The unique contribution of this work lies in its ability to autonomously position UAVs in a way that not only ensures robust connectivity to ground users but also maintains seamless integration with central network infrastructure. Through various simulated scenarios, we demonstrate how our approach effectively addresses these challenges, enhancing coverage and network performance in critical areas. This research fills a significant gap in UAV-assisted 5G networks, providing a scalable and adaptive solution for future mobile networks.

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5G New Radio for Public Safety Mission Critical Communications

Cited by 21 publications

References 7 publications

Distributed Reinforcement Learning–based Seamless Multi-Connectivity Solution for Heterogeneous 5GNR and Wi-Fi Networks

Distributed Reinforcement Learning–based Seamless Multi-Connectivity Solution for Heterogeneous 5GNR and Wi-Fi Networks

A network application approach towards 5G and beyond critical communications use cases

Resource Scheduling Based on Deep Reinforcement Learning in UAV Assisted Emergency Communication Networks

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