Cosistency Axioms of Choice for Ismail’s Entropy Formalism(IEF) Combined with Information-Theoretic(IT) Applications to advance 6G Networks

Yıl 2023, Cilt: 13 Sayı: 2, 207 - 213, 31.12.2023

Ismail A Mageed

https://doi.org/10.36222/ejt.1299311

Cited By: 1

Öz

An exposition is undertaken to analytically validate the credibility of IEF, by investigating the four axioms of consistency on IEF. More fundamentally, IE is by default the ultimate generalization to most entropy measures in the literature. The current paper also provides some applications of Information Theory to 6G Networks. Additionally, the latter review consolidates more foundational motivations and insights into further employment of information-theoretic advancements to 6G networks. Conclusions, open problems, and future directions are given.

Anahtar Kelimeler

IEF , 6G Networks , Applications

Kaynakça

• I.A.Mageed, and Q. Zhang, “An Information Theoretic Unified Global Theory For a Stable M/G/1 Queue With Potential Maximum Entropy Applications to Energy Works”, 2022 Global Energy Conference (GEC). IEEE, 2022.
• S.Lahmiri, and S. Bekiros, “Renyi entropy and mutual information measurement of market expectations and investor fear during the COVID-19 pandemic”, Chaos, Solitons & Fractals 139: 110084, 2020. Methods in Modern Physics. 2016 May 31;13(05):1650063. Normal Distribution: Theory And Applications”, Infinite Study, 2021.
• M.Alkhalaileh, et al. "Performance Analysis of Mobile, Edge and Cloud Computing Platforms for Distributed Applications" ,Mobile Edge Computing. Springer, Cham, 2021. 21-45. J.M.Smith, “Properties and performance modelling of finite buffer M/G/1/K networks”, Computers & Operations Research. 2011 Apr 1;38(4):740-54. N. Kawasaki, “Parametric study of thermal and chemical nonequilibrium nozzle flow,” M.S. thesis, Dept. Electron. Eng., OsakaUniv., Osaka, Japan, 1993 I.A, Mageed, and D.D. Kouvatsos, “The Impact of Information Geometry on the Analysis of the Stable M/G/1 Queue Manifold”, 2021, Icores conference.
• D.D.Kouvatsos and S.A.Assi, “On the Analysis of Queues with HeavyTails: A Non-Extensive Maximum Entropy Formalism and a Generalization of the Zipf-Mandelbrot Distribution”, 2011,K.A. Hummel et al. (Eds.): PERFORM 2010 (Haring Festschrift), LNCS 6821, IFIP, pp. 99-111. W.Rödder , et al, “ Power genesis in social networks: An entropy- driven decision support model with conditional data”, Decision Analytics Journal. 2021 Nov 1;1:100003. Q.Sibo,et al. “Online video popularity regression prediction model with multichannel dynamic scheduling based on user behavior” ,Chinese Journal of Electronics 30.5 (2021): 876-884.
• M. Karsai, et al, “ Bursty human dynamics”, Berlin: Springer International Publishing, 2018.
• G.Allon, et al, “Behavioral foundations of queueing systems”, The handbook of behavioral operations , 2018,9325: 325-366. R.L.Summers, “An Action Principle for Biological Systems”, Journal of Physics: Conference Series. Vol. 2090. No. 1. IOP Publishing, 2021. I.A.Mageed, and Q. Zhang, “The Rényian-Tsallisian Formalisms of the Stable M/G/1 Queue with Heavy Tails Entropian Threshold Theorems for the Squared Coefficient of Variation”, electronic Journal of Computer Science and Information Technology 9.1 (2023): 7-14. D.D.Kouvatsos, I.A.Mageed, V.Anisimov, and N.Limnios, “Non- Extensive Maximum Entropy Formalisms and Inductive Inferences of Stable M/G/1 Queue with Heavy Tails”, In“Advanced Trends in Queueing Theory” 2021 Mar (Vol. 2). Sciences by ISTE & J. Wiley.
• C. Abdulkadir, et al, “Topology optimization for 6G networks: A network information-theoretic approach”,IEEE vehicular technology magazine 15.4 (2020): 83-92.
• M.Chafii, et al, “Twelve scientific challenges for 6G: Rethinking the foundations of communications theory”,IEEE Communications Surveys & Tutorials (2023).
• A. Lazaridou, and M. Baroni, “Emergent multi-agent communication in the deep learning era,” arXiv preprint arXiv:2006.02419, 2020.
• D. Sim˜oes, et al, “Multi-agent deep reinforcement learning with emergent communication,” in 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, 2019, pp. 1–8.
• A. Dorri, et al, “Multi-agent systems: A survey,” IEEE Access, vol. 6, pp. 28 573–28 593, 2018.
• M. Arnold, et al, “Maxray: A raytracing-based integrated sensing and communication framework,” in 2022 2nd IEEE International Symposium on Joint Communications & Sensing (JC&S). IEEE, 2022, pp. 1–7.
• D. Gale and L. S. Shapley, “College admissions and the stability of marriage,” Amer. Math. Monthly, vol. 69, no. 1, pp. 9–15, 1962. doi:10.2307/2312726.
• G. Iosifidis and I. Koutsopoulos, “Challenges in auction theory driven spectrum management,” IEEE Commun. Mag., vol. 49, no. 8, pp.128-135, Aug. 2011. doi: 10.1109/MCOM.2011.5978426.