Minimizing Latency for 5G Multimedia and V2X Applications using Mobile Edge Computing

K, Srinivasa R; Naidu, Naveen Kumar Srinivasa; Maheshwari, Sumit; Bharathi, C. R.; Kumar, Ashwani

doi:10.1109/icct46177.2019.8969038

Cited by 16 publications

(6 citation statements)

References 20 publications

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“…(1) 5G performance evaluations of data delivery using a V2V network [4][5]35] (2) Evaluations of delay, throughput, and packet loss in 5G using V2V and V2I with multi-access edge computing (MEC) [26,30] (3) 5G performance evaluations of V2V and V2I without MEC or a core network [12] [27-28-29] (4) Evaluations of 5G scheduling schemes using V2V and V2I [2,9] (5) Measurements of theoretical routing performance in 5G using V2V and V2I [14] (6) Network-slicing enhancement in 5G using V2V and V2I [40] To enhance the NSA mode in 5G, researchers have recently improved the beamforming [18][19]22], handover [3,11,15], or beamforming with handover [34]. However, to our knowledge, no 5G V2X evaluations in NSA mode in the V2V, V2I, and V2N environments of a highway scenario with VoIP applications have employed adaptive neural fuzzy neural models, which can simultaneously consider both beamforming and handover.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Latency in Fifth-Generation Vehicle-To-Tverything Communications Using Adaptive Neuro-Fuzzy Inference System Model

Sayedahmed,

Elgamal,

Hefny

2023

Preprint

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The fifth-generation new radio technology (5G NR) introduces improved functions to the air interface. In addition, the 5G NR non-standalone (NSA) will operate with long-term evolution, enabling vehicle-to-everything communications (V2X) for improved infotainment services. V2X includes four main classes of communications: vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-pedestrian devices, and vehicle-to-network. However, the stringent transmission frequency, latency, and throughput requirements of infotainment applications constrain the transmitting packets of 5G-V2X-based NSA in highway scenarios. In this paper, the latency is improved by preventing the physical layer of gNodeB and the user equipment (UE) from sending redundant packets for service in a highway scenario. The proposed approach adopts an adaptive neuro-fuzzy inference system (ANFIS), a powerful modeling technique based on artificial neural networks and a fuzzy inference system. The performance of ANFIS is compared with that of the traditional 5G V2X NSA architecture in a simulation study using Voice over Internet Protocol (VoIP) traffic. The delays, throughputs, and packet losses of both architectures are determined in radio link control (RLC) and VoIP applications. The switch-modes, signal-to-interference-noise ratios (SINRs), hybrid automatic repeat request (HARQ) error rate, channel quality indicators (CQIs), served blocks, and transmission-state of gNodeB are computed for the two architectures for device-to-device (D2D), uplink (UL) and downlink (DL) traffic directions. The simulation results show comparable SINRs, CQIs, served blocks and switch-modes in both scenarios, but the presented ANFIS model significantly outperforms the traditional architecture in delay by 66% in D2D, 29% in UL, 25% in DL, and packet loss by 21% in UL in RLC, the HARQ error rate by 9% in D2D, 30% in UL, 95% in DL, transmission-state in gNodeB by 29%, and the delay by 4% for UEs, and frame loss by 90% for UE in VoIP.

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Section: Related Workmentioning

confidence: 99%

Evaluating Latency in Fifth-Generation Vehicle-To-Tverything Communications Using Adaptive Neuro-Fuzzy Inference System Model

Sayedahmed,

Elgamal,

Hefny

2023

Preprint

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“…It is always motivating to develop new networking concepts, optimize and address the increasing needs of the modern intensive latency-sensitive applications running on battery powered mobile terminals [12]- [14]. There are many challenges of 5G management and orchestration across MEC network infrastructure resources as well as end-to-end service configuration [15]- [18]. To satisfy these needs, many researchers discussed these challenges from different perspective.…”

Section: Related Workmentioning

confidence: 99%

Orchestration of MEC Computation Jobs and Energy Consumption Challenges in 5G and Beyond

2022

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Mobile Edge Computing (MEC) technology philosophy inspires the next generation mobile networks to provide cloud computing capabilities in addition to a diverse range of Information Technology (IT) services with ultra-low latency and higher bandwidth at the edge. One of the most common challenges of 5G-MEC is the management and orchestration across all networks and infrastructure resources as well as end-to-end quality of experience. The decentralized architecture of MEC with independent and noncollaborative servers results in the situation of having underutilized servers with wasted energy. Moreover, the consequences of having highly utilized servers with highly consumed energy are not only the incapability to accommodate all the load of the computing jobs and the dramatic increase in the total OPEX cost, but it also creates some environmental problems. Orchestrating servers' workload and control offloading the computation jobs is one of the technical advantages of MEC since it satisfies the increasing requirements of modern mobile applications while optimizing the energy consumption and cost. In this work, we consider cluster-based energy-aware offloading framework. The proposed work consists of dual-tier domain divided into clusters of Edge Servers ES s . We have presented the results of our simulation as a proof of our concept that the formulated adaptive strategy to minimize the optimization problem calculation per cluster reduces the energy consumption and enhances the quality of experience while achieving the conservation of the related computing and storage resources cost.

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“…The various use-cases would also generate diversified data needs, where different throughput, latency, jitter, packet loss, and other SLA parameters will be critical. From a mobile network operator point of view, network slicing will be the solution to serve these needs [ 30 , 31 ]. Network slicing can help to prioritize traffic during end-to-end data transmission.…”

Section: Related Workmentioning

confidence: 99%

5G-Enabled Autonomous Driving Demonstration with a V2X Scenario-in-the-Loop Approach

Szalay

Ficzere

Tihanyi

et al. 2020

Sensors

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Autonomous vehicles are at the forefront of interest due to the expectations of changing transportation for the better. In order to make better decisions on the road, vehicles use information from various sources: their own sensors, messages arriving from surrounding vehicles and objects, as well as from centralized entities—including their own Digital Twin. Certain decisions require the information to arrive with low latency and some of this information (such as video) requires broadband communication. Furthermore, the vehicles can populate an area, so they can represent mass communication endpoints that still need low latency and massive broadband. The mobility of the vehicles obviously requires the complete coverage of the roads with reliable wireless communication technologies fulfilling the previously mentioned needs. The fifth generation of cellular mobile technologies, 5G, addresses these requirements. The current paper presents real-life scenarios—on the M86 highway and the ZalaZONE proving ground in Hungary—for the demonstration of vehicular communication with 5G support, where the cars exchange sensor and control information with each other, their environment, and their Digital Twins. The demonstrations were carried out through the Scenario-in-the-Loop (SciL) methodology, where some of the actionable triggers were not physically present around the vehicles, but sensed or simulated around their Digital Twin. The measurements around the demonstrations aim to reveal the feasibility of the 5G Non-Standalone Architecture for certain communication scenarios, and they mainly aim to reveal the current latency and throughput limitations under real-life conditions.

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Minimizing Latency for 5G Multimedia and V2X Applications using Mobile Edge Computing

Cited by 16 publications

References 20 publications

Evaluating Latency in Fifth-Generation Vehicle-To-Tverything Communications Using Adaptive Neuro-Fuzzy Inference System Model

Evaluating Latency in Fifth-Generation Vehicle-To-Tverything Communications Using Adaptive Neuro-Fuzzy Inference System Model

Orchestration of MEC Computation Jobs and Energy Consumption Challenges in 5G and Beyond

5G-Enabled Autonomous Driving Demonstration with a V2X Scenario-in-the-Loop Approach

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