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2024 | OriginalPaper | Buchkapitel

Performance Enhancement and Scheduling in Communication Networks—A Review into Various Approaches

verfasst von : Priya Kumari, Nitin Jain

Erschienen in: Micro-Electronics and Telecommunication Engineering

Verlag: Springer Nature Singapore

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Abstract

Optimizing the communication network's performance under diverse service quality constraints to match the briskly expanding claims of wireless/mobile applications is the vital goal of imminent wireless networks. A conspicuous way to improve the network performance is through embodying several scheduling mechanisms. Though various scheduling schemes exist, improved schemes are still needed for performance breakthroughs. Therefore, this article provides intense research on scheduling and performance optimization of communication systems. It outlines the prime scope of scheduling resources and enhancing diverse performance measures for strengthening and facilitating wireless network performance. It investigates the existing studies on resource allotment, scheduling and performance enhancement of communication networks. This review work illuminates some vital performance metrics involved in performance upgradation. The paper finally presents the paramount research challenges explicitly involved in the performance betterment of communication networks for introducing and implementing optimal schemes and encouraging vast research in this direction.

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Literatur
3.
Zurück zum Zitat Rani P, Rohit S (2022) An experimental study of IEEE 802.11 n devices for vehicular networks with various propagation loss models. In: International conference on signal processing and integrated networks. Springer Nature Singapore, Singapore Rani P, Rohit S (2022) An experimental study of IEEE 802.11 n devices for vehicular networks with various propagation loss models. In: International conference on signal processing and integrated networks. Springer Nature Singapore, Singapore
7.
Zurück zum Zitat Kumar N, Rani P, Kumar V, Verma PK, Koundal D (2023) TEEECH:three-tier extended energy efficient clustering hierarchy protocol for heterogeneous wireless sensor network. Expert Syst Appl 216:119448 Kumar N, Rani P, Kumar V, Verma PK, Koundal D (2023) TEEECH:three-tier extended energy efficient clustering hierarchy protocol for heterogeneous wireless sensor network. Expert Syst Appl 216:119448
8.
Zurück zum Zitat Levinson J, Askeland J, Becker J, Dolson J, Held D, Kammel S, Kolter JZ, Langer D, Pink O, Pratt V, Sokolsky M, Stanek G, Stavens D, Teichman A, Werling M, Thrun S (2011) Towards fully autonomous driving: Systems and algorithms. In: 2011 IEEE intelligent vehicles symposium (IV), IEEE, pp 163–168. https://doi.org/10.1109/IVS.2011.5940562 Levinson J, Askeland J, Becker J, Dolson J, Held D, Kammel S, Kolter JZ, Langer D, Pink O, Pratt V, Sokolsky M, Stanek G, Stavens D, Teichman A, Werling M, Thrun S (2011) Towards fully autonomous driving: Systems and algorithms. In: 2011 IEEE intelligent vehicles symposium (IV), IEEE, pp 163–168. https://​doi.​org/​10.​1109/​IVS.​2011.​5940562
12.
Zurück zum Zitat Hussain N, Rani P, Kumar N, Chaudhary MG (2022) A deep comprehensive research architecture, characteristics, challenges, issues, and benefits of routing protocol for vehicular ad-hoc networks. Int J Distrib Syst Technol (IJDST) 13(8):1–23CrossRef Hussain N, Rani P, Kumar N, Chaudhary MG (2022) A deep comprehensive research architecture, characteristics, challenges, issues, and benefits of routing protocol for vehicular ad-hoc networks. Int J Distrib Syst Technol (IJDST) 13(8):1–23CrossRef
14.
Zurück zum Zitat Alqerm A (2018) Novel machine learning-based techniques for efficient resource allocation in next generation wireless networks (Doctoral dissertation) Alqerm A (2018) Novel machine learning-based techniques for efficient resource allocation in next generation wireless networks (Doctoral dissertation)
16.
Zurück zum Zitat Rani P, Sharma R (2023) Intelligent transportation system for internet of vehicles based vehicular networks for smart cities. Comput Electric Eng 105:108543 Rani P, Sharma R (2023) Intelligent transportation system for internet of vehicles based vehicular networks for smart cities. Comput Electric Eng 105:108543
17.
Zurück zum Zitat Kumar N, Rani P, Kumar V, Athawale SV, Koundal D (2022) THWSN: Enhanced energy-efficient clustering approach for three-tier heterogeneous wireless sensor networks. IEEE Sens J 22(20):20053–20062CrossRef Kumar N, Rani P, Kumar V, Athawale SV, Koundal D (2022) THWSN: Enhanced energy-efficient clustering approach for three-tier heterogeneous wireless sensor networks. IEEE Sens J 22(20):20053–20062CrossRef
25.
Zurück zum Zitat Zhang J, Xu X, Zhang K, Zhang B, Tao X, Zhang P (2019) Machine learning based flexible transmission time interval scheduling for eMBB and uRLLC coexistence scenario. IEEE Access 7:65811–65820CrossRef Zhang J, Xu X, Zhang K, Zhang B, Tao X, Zhang P (2019) Machine learning based flexible transmission time interval scheduling for eMBB and uRLLC coexistence scenario. IEEE Access 7:65811–65820CrossRef
26.
Zurück zum Zitat Comşa S, Muntean GM, Trestian R (2020) An innovative machine-learning-based scheduling solution for improving live UHD video streaming quality in highly dynamic network environments. IEEE Transact Broadcast 67(1):212–224CrossRef Comşa S, Muntean GM, Trestian R (2020) An innovative machine-learning-based scheduling solution for improving live UHD video streaming quality in highly dynamic network environments. IEEE Transact Broadcast 67(1):212–224CrossRef
27.
Zurück zum Zitat Yang T, Hu Y, Gursoy MC, Schmeink A, Mathar R (2018) Deep reinforcement learning based resource allocation in low latency edge computing networks. In: 2018 15th international symposium on wireless communication systems (ISWCS), IEEE, pp 1-–5 Yang T, Hu Y, Gursoy MC, Schmeink A, Mathar R (2018) Deep reinforcement learning based resource allocation in low latency edge computing networks. In: 2018 15th international symposium on wireless communication systems (ISWCS), IEEE, pp 1-–5
28.
Zurück zum Zitat Chinchali S, Hu P, Chu T, Sharma M, Bansal M, Misra R, Pavone M, Katti S (2018) Cellular network traffic scheduling with deep reinforcement learning. In: Thirty- second AAAI conference on artificial intelligence Chinchali S, Hu P, Chu T, Sharma M, Bansal M, Misra R, Pavone M, Katti S (2018) Cellular network traffic scheduling with deep reinforcement learning. In: Thirty- second AAAI conference on artificial intelligence
31.
Zurück zum Zitat Yang Z, Feng L, Chang Z, Lu J, Liu R, Kadoch M, Cheriet M (2020) Prioritized uplink resource allocation in smart grid backscatter communication networks via deep reinforcement learning. Electronics 9(4):622CrossRef Yang Z, Feng L, Chang Z, Lu J, Liu R, Kadoch M, Cheriet M (2020) Prioritized uplink resource allocation in smart grid backscatter communication networks via deep reinforcement learning. Electronics 9(4):622CrossRef
32.
Zurück zum Zitat Atallah R, Assi C, Khabbaz M (2017) Deep reinforcement learning-based scheduling for roadside communication networks. In: 2017 15th international symposium on modeling and optimization in mobile, Ad Hoc, and wireless networks (WiOpt), IEEE, pp 1–8. https://doi.org/10.23919/WIOPT.2017.7959912 Atallah R, Assi C, Khabbaz M (2017) Deep reinforcement learning-based scheduling for roadside communication networks. In: 2017 15th international symposium on modeling and optimization in mobile, Ad Hoc, and wireless networks (WiOpt), IEEE, pp 1–8. https://​doi.​org/​10.​23919/​WIOPT.​2017.​7959912
Metadaten
Titel
Performance Enhancement and Scheduling in Communication Networks—A Review into Various Approaches
verfasst von
Priya Kumari
Nitin Jain
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-99-9562-2_55

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