Network Architectures for Demanding 5G Performance Requirements: Tailored Toward Specific Needs of Efficiency and Flexibility

Schulz, Philipp; Wolf, Albrecht; Fettweis, Gerhard; Waswa, Abubaker Matovu; Soleymani, Dariush M.; Mitschele‐Thiel, Andreas; Dudda, Torsten; Dod, Markus; Rehme, Marco; Voigt, Jens; Riedel, Ines; Wankhede, Tushar; Nitzold, Walter; Almeroth, Bjoern

doi:10.1109/mvt.2019.2904185

Cited by 19 publications

(3 citation statements)

References 5 publications

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“…The transmission power distribution of D2I users is shown in Figure 8. In this paper, a traffic model based on the requirements given in [20] is considered (see Table 3). Figure 7 illustrates the distribution of traffic payload generated by D2D users during the simulation run.…”

Section: Simulation Parameters and Performance Metricsmentioning

confidence: 99%

Open Sub-Granting Radio Resources in Overlay D2D-Based V2V Communications

Soleymani¹,

Gholami²,

Galdo³

et al. 2021

Preprint

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Capacity, reliability, and latency are seen as key requirements of new emerging applications, namely Vehicle-to-Everything (V2X) and Machine Type Communication (MTC) in future cellular networks. Device-to-Device (D2D) communication is envisaged to be the enabler to accomplish the requirements for the applications as mentioned earlier. Due to the scarcity of radio resources, a hierarchical radio resource allocation, namely the sub-granting scheme, has been considered for the overlay D2D communication. In this paper, we investigate the assignment of underutilized radio resources from D2D communication to Device-to-Infrastructure (D2I) communication, which are moving in a dynamic environment. The sub-granting assignment problem is cast as a maximization problem of the uplink cell throughput. Firstly, we evaluate the sub-granting signaling overhead due to mobility in a centralized sub-granting resource algorithm, Dedicated Sub-Granting Radio Resource (DSGRR), and then a distributed heuristics algorithm, Open Sub-Granting Radio Resource (OSGRR) is proposed and compared with the DSGRR algorithm and no sub-granting case. Simulation results show improved cell throughput for the OSGRR compared with other algorithms. Besides, it is observed that the overhead incurred by the OSGRR is less than the DSGRR while the achieved cell throughput is yet close to the maximum achievable uplink cell throughput.

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Section: Simulation Parameters and Performance Metricsmentioning

confidence: 99%

Open Sub-Granting Radio Resources in Overlay D2D-Based V2V Communications

Soleymani¹,

Gholami²,

Galdo³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The transmission power distribution of D2I users is shown in Figure 8. In this paper, a traffic model based on the requirements given in [18] is considered (See Table 3). Figure 7 illustrates the distribution of traffic payload generated by D2D users over the simulation run.…”

Section: Overheadmentioning

confidence: 99%

Sub-Granting Radio Resources in Overlay D2D-Based V2V Communications

Soleymani

Gholami

Mueckenheim

et al. 2020

Preprint

Self Cite

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Abstract Capacity, reliability, and latency are seen as key requirements of new emerging applications, namely Vehicleto- Everthings (V2X) and Machine Type Communication (MTC) in future cellular networks. Device-to-Device (D2D) communication is envisaged to be the enabler to accomplish the requirements for the aforementioned applications. Due to the scarcity of radio resources, hierarchical radio resource allocation, namely the sub-granting scheme, has been considered for the overlay D2D communication. In this paper, we investigate the assignment of un-utilized radio resources to Device-to-Infrastructure (D2I) users, i.e., beneficiary user, for moving users in a dynamic environment. The sub-granting assignment problem is mathematically cast as the uplink cell throughput maximization problem. To this end, two heuristics are proposed: 1) Dedicated Sub-Granting Radio Resource (DSGRR) in a centralized manner, and 2) Open Sub-Granting Radio Resource (OSGRR) in a distributed fashion. Simulation results show improved cell throughput for the OSGRR compared with the DSGRR yet less overhead while having reasonable tightness to the maximum achievable uplink throughput.

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“…The next generation of cellular mobile networks, 5G, is being developed to address several application requirements, such as massive number of heterogeneous devices, high transmission rate, low latency, and high reliability [1]. Internet of Things (IoT), smart city, vehicular networks, Industry 4.0, and other scenarios bring new types of mobile devices with heterogeneous hardware specification that will requires different network specifications.…”

Section: Introductionmentioning

confidence: 99%

Using Reinforcement Learning to Allocate and Manage SFC in Cellular Networks

Santos

Kelner

Sadok

et al. 2020

2020 16th International Conference on Network and Service Management (CNSM)

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It is expected that the next generation cellular networks provide a connected society with fully mobility to empower the socio-economic transformation. Several other technologies will benefits of this evolution, such as Internet of Things, smart cities, smart agriculture, vehicular networks, healthcare applications, and so on. Each of these scenarios presents specific requirements and demands different network configurations. To deal with this heterogeneity, virtualization technology is key technology. Indeed, the network function virtualization (NFV) paradigm provides flexibility for the network manager, allocating resources according to the demand, and reduces acquisition and operational costs. In addition, it is possible to specify an ordered set of network virtual functions (VNFs) for a given service, which is called as service function chain (SFC). However, besides the advantages from service virtualization, it is expected that network performance and availability do not be affected by its usage. In this paper, we propose the use of reinforcement learning to deploy a SFC of cellular network service and manage the VNFs operation. We consider that the SFC is deployed by the reinforcement learning agent considering a scenarios with distributed data centers, where the VNFs are deployed in virtual machines in commodity servers. The NFV management is related to create, delete, and restart the VNFs. The main purpose is to reduce the number of lost packets taking into account the energy consumption of the servers. We use the Proximal Policy Optimization (PPO2) algorithm to implement the agent and preliminary results show that the agent is able to allocate the SFC and manage the VNFs, reducing the number of lost packets.

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Network Architectures for Demanding 5G Performance Requirements: Tailored Toward Specific Needs of Efficiency and Flexibility

Cited by 19 publications

References 5 publications

Open Sub-Granting Radio Resources in Overlay D2D-Based V2V Communications

Open Sub-Granting Radio Resources in Overlay D2D-Based V2V Communications

Sub-Granting Radio Resources in Overlay D2D-Based V2V Communications

Using Reinforcement Learning to Allocate and Manage SFC in Cellular Networks

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