Abstract
At present, the development of value chain mainly shows that the added value is positive; otherwise, the industrial value chain will be eliminated. Therefore, the construction and measurement of the industrial value chain height can effectively avoid the collapse of the industrial value chain caused by the low system value of the value chain. In this paper, the mobile virtualization channel allocation algorithm is used to construct the multi-objective industrial value chain height index, and the actual industrial value chain height in China is measured. This paper first describes the channel allocation algorithm of mobile virtualization, extracts the allocation rules of the algorithm, constructs the model of multi-objective industrial value chain, then measures the value chain of eight regions in China combined with the elements of technology, production, and marketing in the height of value chain, and quantitatively compares the results of value chain height of different regions according to the embeddedness and depth. Finally, it puts forward the improvement scheme for the measurement results and the problems faced by the value chain. The research shows that the average value chain height of South China is about 70.86, while the average value chain height of Northwest China is about 46.58, ranging from 40.58 to 60.45. Through quantitative data, the value chain height of South China is generally higher than that of Northwest China by more than 50%, which may be related to the innovation of technology and the imitation form of production. Therefore, this paper proposes to strengthen the technological innovation in the upstream of the value chain, so as to drive the development of the regional value chain.
Similar content being viewed by others
Data availability
Enquiries about data availability should be directed to the authors.
References
Butt RA, Jawaid M, Ashraf MW (2019) Load adaptive dynamic wavelength and bandwidth assignment for TWDM PON IEEE. In: Proceedings of 2019 international workshop on fiber optics in access networks (FOAN 2019), Sarajevo: IEEE, vol 7, pp 101–105.
Chen Y, Ai B, Niu Y (2018) Resource allocation for device-to-device communications underlaying heterogeneous cellular networks using coalitional games. IEEE Trans Wireless Commun 17(6):4163–4176
Dash SP, Joshi S (2020) Performance analysis of a cooperative D2D communication network with NOMA. IET Commun 14(16):2731–2739
Eslami L, Mirjalily G, Davidson TN (2020) Spectrum-efficient QoS-aware resource assignment for FFR-based D2D-enabled heterogeneous networks. IEEE Access 8(56):218186–218198
Fouli K, Maier M, Médard M (2019) Network coding in next-generation passive optical networks. IEEE Commun Magaz 49(9):38–46
Gu W, Zhu Q (2019) Stackelberg game based social-aware resource allocation for NOMA enhanced D2D communications. Electronics 8(11):1360–1361
Gu RT, Zhang SZ, Ji YF (2020) Efficient software-defined passive optical network with network coding. Photon Netw Commun 31(2):239–250
Han B, Li J, Su J, Cao J (2012) Self-supported cooperative networking for emergency services in multi-hop wireless networks. IEEE J Select Areas Commun 30(2):450–457
Hayati M, Kalbkhani H, Shayesteh MG (2021) Energy-efficient relay selection and power allocation for multi-source multicast network-coded D2D communications. AEU Int J Elect Commun 128(1):153–154
Huu P, Arfaoui MA, Sharafeddine S (2020) A low-complexity framework for joint user pairing and power control for cooperative NOMA in 5G and beyond cellular networks. IEEE Trans Commun 68(11):6737–6749
Jain P, Gupta A, Tanwar S (2020a) Customized NOMA and sector model for battery efficient beyond 5G green networks. IEEE Netw 34(6):281–287
Jain DK, Zareapoor M, Jain R, Kathuria A, Bachhety S (2020b) GAN-Poser: an improvised bidirectional GAN model for human motion prediction. Neural Comput Appl 32(18):14579–14591
Jung SY, Lee JH, Nam W, Kim BW (2020) "Complementary color barcode-based optical camera communications. Wireless Commun Mob Comput 2020:8
Lin Z, Song H, Pan D (2019) A joint power and channel scheduling scheme for underlay D2D communications in the cellular network. Sensors 19(21):4799–4801
Liu Z, Feng R, Li X, Wang W, Wu X (2021) Gradient-sensitive optimization for convolutional neural networks. Comput Intell Neurosci 2021:16
Ma Y, Liu T, Cui L (2020) Robust resource allocation with power outage guarantees for energy harvesting aided device-to-device communication. IEEE Access 8(5):124563–124578
Park JH, Kwon D, Kim DK (2020) Resource allocation for GBR services in D2D-enabled communication. Electronics 9(10):1585–1587
Razmkhah A, Rahbar AG (2019) Dynamic bandwidth allocation in heterogeneous WDM EPONs. Telecommun Syst 60(3):393–403
Ren DP, Wu SS, Zhang LJ (2020) A new wavelength optimization and energy-saving scheme based on network coding in software-defined WDM-PON networks. J Opt Commun 37(3):265–269
Shuai QJ, Ansari N (2019) Scheduling hybrid WDM/TDM EPONs with heterogeneous propagation delays IEEE. In: Proceedings of IEEE international conference on communications (ICC 2014), Sydney: IEEE, vol 7, pp 3877–3882.
Tian F, Chen X (2019) Multiple-antenna techniques in nonorthogonal multiple access: a review. Front Inform Technol Elect Eng 20(12):1665–1697
Wei P, Gu RT, Ji YF (2019) Dynamic bandwidth allocation algorithm for next-generation time division multiplexing passive optical networks with network coding. Opt Eng 52(8):1–12
Xu Y, Wang G, Li B (2019) Performance of D2D aided uplink coordinated direct and relay transmission using NOMA. IEEE Access 7(4):151090–151102
Xu Z, Zhu G, Metawa N, Zhou Q (2022) Machine learning based customer meta-combination brand equity analysis for marketing behavior evaluation. Inf Process Manag 59(1):102800
Yang J, Li T, Xie P (2020) Energy efficiency optimization algorithm for heterogeneous cellular network based on Stackelberg game. J Signal Process 36(11):1923–1930
Yeh JY, Chen CH (2019) A machine learning approach to predict the success of crowdfunding fintech project. J Enterp Inform Manag. https://doi.org/10.1108/JEIM-01-2019-0017
Yuan Y, Yang T, Feng H (2018) An iterative matching-Stackelberg game model for channel power allocation in D2D underlaid cellular networks. IEEE Trans Wireless Commun 17(11):7456–7471
Zhao J, Liu Y, Chai KK (2020) NOMA-based D2D communications: towards 5G. In: Proceedings of the 59th annual IEEE global communications conference (IEEE GLOBECOM). Washington, vol 5, pp 1–6.
Funding
This work was supported by the Ministry of Education of Humanities and Social Science project (Grant No. 17YJC630234).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Ethical statement
This article does not contain any studies with animals performed by any of the authors. This article does not contain any studies with human participants or animals performed by any of the authors.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Zhou, M., Li, S. Construction and measurement of multi-objective industry value chain height index based on channel allocation algorithm of mobile network virtualization. Soft Comput 26, 5593–5606 (2022). https://doi.org/10.1007/s00500-022-07088-7
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-022-07088-7