Use Cases and Standardisation Activities for eMBB and V2X Scenarios

Κωστόπουλος, Αλέξανδρος; Chochliouros, Ioannis P.; Ferragut, Jaime; Ma, Yi; Kutila, Matti; Gavras, Anastasius; Horsmanheimo, Seppo; Zhang, Kai; Ladid, Latif; Dardamanis, Athanassios; Kourtis, Michail-Alexandros

doi:10.1109/iccworkshops49005.2020.9145377

Cited by 10 publications

(6 citation statements)

References 2 publications

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“…Following the description of the Katana slicing framework [9], a set of experiments were carried out to evaluate the impact of slicing technologies [10] regarding the energy domain. Katana was previously evaluated in terms of performance in [11,12] and achieved end-to-end latency down to ~10 ms. Related studies [13][14][15][16] have defined the minimal requirement for end-to-end URLLC slicing to be at 20 ms [17,18]; therefore, the presented work significantly improves expected latency performance. For the needs of these experiments, a 5G system configured in Standalone Mode (SA) was used, with a set of 5 UEs.…”

Section: G Slicing Energy Evaluationmentioning

confidence: 99%

Conceptual Evaluation of a 5G Network Slicing Technique for Emergency Communications and Preliminary Estimate of Energy Trade-Off

et al. 2021

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The definition of multiple slicing types in 5G has created a wide field for service innovation in communications. However, the advantages that network slicing has to offer remain to be fully exploited by today’s applications and users. An important area that can potentially benefit from 5G slicing is emergency communications for First Responders. The latter consists of heterogeneous teams, imposing different requirements on the connectivity network. In this paper, the RESPOND-A platform is presented, which provides First Responders with network-enabled tools on top of 5G on-scene planning, with enhanced service slicing capabilities tailored to emergency communications. Furthermore, a mapping of emergency services and communications to specific slice types is proposed to identify the current challenges in the field. Additionally, the proposed tentative mechanism is evaluated in terms of energy efficiency. Finally, the approach is summarized by discussing future steps in the convergence of 5G network slicing in various areas of emergency vertical applications.

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Section: G Slicing Energy Evaluationmentioning

confidence: 99%

Conceptual Evaluation of a 5G Network Slicing Technique for Emergency Communications and Preliminary Estimate of Energy Trade-Off

et al. 2021

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“…under the respect of ∑ P k k∈K ≤ P max (5) Which implies that the overall transmitted power by the K vehicles is limited by the maximum threshold Pmax which is helpful to ensure energy efficiency improvement which reminds waterfilling approach. Correspondly, the resolution of the optimization problem relies on Waterfilling policy performed in iterative manner such that the power is allocated with enhancing the data rate.…”

Section: Figure 1 Congestion Architecture Of Nr 5g V2v Modelmentioning

confidence: 99%

“…Otherwise, the contribution [4] investigated roads congestion whenever LTE-V (Long Term Evolution Vehicle) Mode 4 was used as enabler for car to car communiation. The proposal in [5] investigated V2X (Vehicle to Everything) context following enhanced mobile broadband standardization use case following drive 5G project. The idea in [6] was about diminishing redandancy information in the way to ensure reliable communication towards a highway scenario.…”

Section: Iiintroductionmentioning

confidence: 99%

Congestion Control investigation into 5G V2V

Chihi

Boualegue²

2021

Preprint

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Congestion control is a hot research topic according to the continuous increase of traffic density together with great mobility of cars. In this context, in this survey we present an overview about the different vehicle to vehicle (V2V) communication enablers by describing the difference between them performing better driving assistance together with efficient congestion control ensuring driving safety. New radio (NR) 5G is the heuristic enabler which provides scalability and reliability towards vehicular communication. Indeed, NR 5G is the best solution supporing the great increase of network density. 5G technology is expected to allow much advanced services and application with a huge capacity of data exchange. Congestion occurance impacts the quality of service of vehicular network, for this accurate quantification is required. In our analysis we consider channel busy rate CBR as indicator of network density. Channel load control is mondatory to overcome traffic jam which could be highlighted by different metrics. The connectivity between cars is modeled with a game theory framework considering a non cooperative vehicular mobility. Each vehicle adjusts its transmitted power in dynamic way taking into account the traffic density measured by channel busy rate (CBR). Consequently, we follow a theoretical modeling to determine the efficient allocated power ensuring awareness messages exchange without saturating the communication channel under the respect of a fixed threshold. Therefore, channel saturation control is investigated following a theoretical analysis exploiting game theory under the assumption of a non cooperative strategy. Each vehicle acts as a player presenting a specific driving behavior different to the other cars participating into the same scenario. The optimization framework is about determining the optimal allocated power for congestion minimization.

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“…Other applications include Machine-Type Communication (MTC) and Industry 4.0, in which LDM is considered as a tool to deliver critical control services and best-effort monitoring services [6]- [8]; as well as vehicle-toeverything (V2X) communications [9]. In V2X systems, traffic light status and pedestrian detection information in intelligent intersections [10] can be broadcast together with highdefinition map transmission for enhanced autonomous driving [11].…”

Section: Introductionmentioning

confidence: 99%

Learning to Broadcast for Ultra-Reliable Communication with Differential Quality of Service via the Conditional Value at Risk

Karasik¹,

Simeone²,

Jang³

et al. 2021

Preprint

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Broadcast/multicast communication systems are typically designed to optimize the outage rate criterion, which neglects the performance of the fraction of clients with the worst channel conditions.Targeting ultra-reliable communication scenarios, this paper takes a complementary approach by introducing the conditional value-at-risk (CVaR) rate as the expected rate of a worst-case fraction of clients. To support differential quality-of-service (QoS) levels in this class of clients, layered division multiplexing (LDM) is applied, which enables decoding at different rates. Focusing on a practical scenario in which the transmitter does not know the fading distribution, layer allocation is optimized based on a dataset sampled during deployment. The optimality gap caused by the availability of limited data is bounded via a generalization analysis, and the sample complexity is shown to increase as the designated fraction of worst-case clients decreases. Considering this theoretical result, meta-learning is introduced as a means to reduce sample complexity by leveraging data from previous deployments. Numerical experiments demonstrate that LDM improves spectral efficiency even for small datasets; that, for sufficiently large datasets, the proposed mirror-descentbased layer optimization scheme achieves a CVaR rate close to that achieved when the transmitter knows the fading distribution; and that meta-learning can significantly reduce data requirements.

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Use Cases and Standardisation Activities for eMBB and V2X Scenarios

Cited by 10 publications

References 2 publications

Conceptual Evaluation of a 5G Network Slicing Technique for Emergency Communications and Preliminary Estimate of Energy Trade-Off

Conceptual Evaluation of a 5G Network Slicing Technique for Emergency Communications and Preliminary Estimate of Energy Trade-Off

Congestion Control investigation into 5G V2V

Learning to Broadcast for Ultra-Reliable Communication with Differential Quality of Service via the Conditional Value at Risk

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