Modified power line system-based energy efficient routing protocol to improve network life time in 5G networks

Alqahtani, Hamed; Niranjan, L; Parthasarathy, P.; Mubarakali, Azath

doi:10.1016/j.compeleceng.2022.108564

Cited by 24 publications

(5 citation statements)

References 12 publications

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“…In addition, some works propose energy-efficient virtual resource allocation methods using Deep Reinforcement Learning such as the work in [ 13 ]. Other works propose an energy-efficient routing protocol in 5G such as the work in [ 14 ], whereas the authors in [ 15 ] introduce an energy-efficient scheme by solving an optimization problem that aims to reduce power consumption. Nevertheless, these works do not tackle the energy-efficient aspect from a BWP radio resource allocation perspective and do not consider the multi-slice users.…”

Section: Related Workmentioning

confidence: 99%

“…ST u,SN = ∑ s∈{eb,uc} V u,s Thr u (14) Figure 2 displays the number of users associated with each strategy using the exhaustive search algorithm for 20 users. Results show that a higher number of users prefer the single-numerology scheme, as it is more adapted to these users from an energy-efficiency perspective, since users scan a single and narrower BWP without BWP switching.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Energy-Efficient BWP Configuration for Multi-Slice Users

Saad,

Khawam,

Yassin

et al. 2024

Sensors

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Fifth Generation (5G) mobile networks introduce the concept of slicing to ensure isolation among the various supported heterogeneous services. The User Equipment (UE) can be connected to multiple slices simultaneously. Additionally, the notion of a Bandwidth Part (BWP) was also instigated to reduce power consumption. A BWP is a small chunk of the bandwidth scanned by the UE to retrieve its service data. Therefore, a UE connected to multiple services can be configured with multiple BWPs each associated with a given service. Such UEs find themselves scanning multiple BWPs, which can be time consuming and highly energy intensive. Hence, it is paramount to study the appropriate choice of the BWP configuration from an energy-efficiency perspective for multi-slice users depending on their battery level. In this paper, two energy-efficient BWP selection solutions are proposed for users connected to multiple slices. The first solution is based on a centralized approach where UEs are stirred optimally to the best BWP configuration, while the second solution relies on a user-centric distributed approach using non-cooperative game theory. The proposed schemes take into account the users’ battery level and their sojourn time in the network as well as the scanned BWP size. Both solutions are compared with one another and against the legacy solution. Intensive simulation results demonstrate the efficiency of our proposition in terms of users’ energy efficiency and quality of service.

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Section: Related Workmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Energy-Efficient BWP Configuration for Multi-Slice Users

Saad,

Khawam,

Yassin

et al. 2024

Sensors

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show abstract

“…In general, recent work is limited to a subset of 5G network requirements [29] [30] [31] [32]. Unlike previous work, the algorithm we use, which was initially proposed in [18], chooses the best optimal path with considering the 5G requirements such as a spectral and energy efficiency target.…”

Section: Related Workmentioning

confidence: 99%

Parallel Deployment and Performance Analysis of a Multi-Hop Routing Protocol for 5G Backhaul Networks Using Cloud and HPC Platforms

Aboulrous,

Abdelsamea,

El-Moursy

et al. 2024

IEEE Access

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The main goals of fifth generation (5G) systems are to significantly increase the network capacity and to support new 5G service requirements. Ultra network densification with small cells is among the key pillars for 5G evolution. The inter-small-cell 5G backhaul network involves massive data traffic. Hence, it is important to have a centralized, efficient multi-hop routing protocol for backhaul networks to manage and speed up the routing decisions among small cells, while considering the 5G service requirements. This paper proposes a parallel multi-hop routing protocol to speed up routing decisions in 5G backhaul networks. To this end, we study the efficiency of utilizing the parallel platforms of cloud computing and high-performance computing (HPC) to manage and speed up the parallel routing protocol for different communication network sizes and set recommendations for utilizing cloud resources to adopt the parallel protocol. Our numerical results indicate that the HPC parallel implementation outperforms the cloud computing implementation, in terms of routing decision speed-up and scalability to large network sizes. In particular, for a large network size with 2048 nodes, our HPC implementation achieves a routing speed-up of 37x. However, the best routing speed-up achieved using our cloud computing implementation is 15.5x, and is recorded using one virtual machine (VM) for a network size of 1024 nodes. In summary, there is a trade-off between a better performance for HPC vs. flexible resources of cloud computing. Thus, choosing best fit platform for 5G routing protocols depends on the deployment scenarios at 5G core or edge network.INDEX TERMS 5G routing protocol, Cloud Radio Access Networks, Cloud computing, HPC, Ultra-Dense Network.

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“…Fast time-wavering distinctive channel and low solidity flyer of vehicular communications IEEE 802.11p makes estimation of channel frequency response very complex. In [24] chronological spectral, deep learning based scheme is combined to estimate channel frequency response with truncation. Time-wavering patterns of traffic and spatial dependencies in network creates complication in forecasting traffic, considering road as graph and applying appropriate framework may address this issue.…”

Section: Related Workmentioning

confidence: 99%

Developing a pervasive edge computing environment for Vehicular Communication using modified Reinforcement Learning in Routing and Dynamic Traffic Flow Prediction

Alqahtani,

Ramakrishnan,

Saravanan

et al. 2023

Preprint

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Vehicular networking in smart autonomous connected vehicle communications evolved with high mobility and due to high dynamics in an urban environment, new challenges are addressed by academicians and researchers for providing better support. Dynamic changes of vehicular nodes position, routing in Vehicular Adhoc Network (VANET) using existing traditional networking routing algorithms may not provide optimal solution for efficient communication. Also predicting or forecasting traffic flow in VANET can be improved through sharing traffic information in real-time using intelligent transportation systems. In this paper we proposed modified reinforcement learning algorithm that supports for optimal route identification for dynamically disconnected vehicles in urban environment by considering its previous state and predicts flow of traffic generated by vehicles in various time interval. Experimental result shows better performance in routing parameters like packet delivery ratio, routing overhead, latency and predicting traffic flow by proposed algorithms achieves significant accomplishments comparing to existing algorithms.

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Modified power line system-based energy efficient routing protocol to improve network life time in 5G networks

Cited by 24 publications

References 12 publications

Energy-Efficient BWP Configuration for Multi-Slice Users

Energy-Efficient BWP Configuration for Multi-Slice Users

Parallel Deployment and Performance Analysis of a Multi-Hop Routing Protocol for 5G Backhaul Networks Using Cloud and HPC Platforms

Developing a pervasive edge computing environment for Vehicular Communication using modified Reinforcement Learning in Routing and Dynamic Traffic Flow Prediction

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