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Access probability optimization for streaming media transmission in heterogeneous cellular networks

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Abstract

FemtoCaching technology, aiming at maximizing the access probability of streaming media transmission in heterogeneous cellular networks, is investigated in this paper. Firstly, five kinds of streaming media deployment schemes are proposed based on the network topology and the relationship between users and streaming media. Secondly, a matching algorithm for adaptive streaming media deployment is proposed, where the FemtoCaching can be adjusted dynamically. Thirdly, a joint problem is formulated combined with the channel assignment, the power allocation, and the caching deployment. To address this problem, we propose a joint optimization algorithm combining matching algorithm and genetic algorithm to maximize the access probability of streaming media transmission. Simulation experiments demonstrate that: (1) the average access probability of all users accessing streaming media in the network based on the proposed algorithm compared with recent works can be greatly improved, and (2) the performance increases with increasing the number of channels and the storage capacity of micro base stations, but decreases with increasing the number of users.

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References

  1. Cisco Systems, I. (2011). Cisco visual networking index : Global mobile data traffic forecast update, 2010-2015. http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/

  2. Seufert, M., Egger, S., Slanina, M., Zinner, T., Hoßfeld, T., & Tran-Gia, P. (2015). A survey on quality of experience of http adaptive streaming. IEEE Communications Surveys Tutorials, 17(1), 469–492. https://doi.org/10.1109/COMST.2014.2360940

    Article  Google Scholar 

  3. Merwaday, A., & Guvenc, I.(2015). UAV assisted heterogeneous networks for public safety communications. In: 2015 IEEE Wireless Communications and Networking Conference Workshops (WCNCW), pp. 329–334. https://doi.org/10.1109/WCNCW.2015.7122576

  4. Argyriou, A., Kosmanos, D., & Tassiulas, L. (2015). Joint time-domain resource partitioning, rate allocation, and video quality adaptation in heterogeneous cellular networks. IEEE Transactions on Multimedia, 17(5), 736–745. https://doi.org/10.1109/TMM.2015.2408254

    Article  Google Scholar 

  5. Argyriou, A., Poularakis, K., Iosifidis, G., & Tassiulas, L. (2017). Video delivery in dense 5G cellular networks. IEEE Network, 31(4), 28–34. https://doi.org/10.1109/MNET.2017.1600298

    Article  Google Scholar 

  6. Jiang, W., Feng, G., & Qin, S. (2017). Optimal cooperative content caching and delivery policy for heterogeneous cellular networks. IEEE Transactions on Mobile Computing, 16(5), 1382–1393. https://doi.org/10.1109/TMC.2016.2597851

    Article  Google Scholar 

  7. Bastug, E., Bennis, M., & Debbah, M. (2014). Living on the edge: The role of proactive caching in 5G wireless networks. IEEE Communications Magazine, 52(8), 82–89. https://doi.org/10.1109/MCOM.2014.6871674

    Article  Google Scholar 

  8. Shanmugam, K., Golrezaei, N., Dimakis, A. G., Molisch, A. F., & Caire, G. (2013). Femtocaching: wireless content delivery through distributed caching helpers. IEEE Transactions on Information Theory, 59(12), 8402–8413. https://doi.org/10.1109/TIT.2013.2281606.

    Article  MathSciNet  MATH  Google Scholar 

  9. Liu, D., Chen, B., Yang, C., & Molisch, A. F. (2016). Caching at the wireless edge: design aspects, challenges, and future directions. IEEE Communications Magazine, 54(9), 22–28. https://doi.org/10.1109/MCOM.2016.7565183

    Article  Google Scholar 

  10. Moghimi, M., Zakeri, A., Javan, M.R., Mokari, N., & Ng, D.W.K. (2020). Joint radio resource allocation and cooperative caching in PD-NOMA-based hetnets. IEEE Transactions on Mobile Computing, 1–1 (2020). https://doi.org/10.1109/TMC.2020.3034618

  11. Zhou, L., Dong, Y., Hong, M., & Shi, Q. (2020) . Joint channel assignment and power allocation for multi-UAVs communication systems. In: 2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1–5. https://doi.org/10.1109/SPAWC48557.2020.9154272

  12. Golrezaei, N., Molisch, A. F., Dimakis, A. G., & Caire, G. (2013). Femtocaching and device-to-device collaboration: a new architecture for wireless video distribution. IEEE Communications Magazine, 51(4), 142–149. https://doi.org/10.1109/MCOM.2013.6495773.

    Article  Google Scholar 

  13. Liu, D., & Yang, C. (2016). Cache-enabled heterogeneous cellular networks: Comparison and tradeoffs. In: 2016 IEEE International Conference on Communications (ICC), pp. 1–6 . https://doi.org/10.1109/ICC.2016.7510749

  14. Vo, N., Duong, T.Q., & Guizani, M. (2016). Qoe-oriented resource efficiency for 5G two-tier cellular networks: A femtocaching framework. In: 2016 IEEE Global Communications Conference (GLOBECOM), pp. 1–6. https://doi.org/10.1109/GLOCOM.2016.7842185

  15. Tan, Y., Yuan, Y., Yang, T., Xu, Y., & Hu, B. (2016). Femtocaching in wireless video networks: Distributed framework based on exact potential game. In: 2016 IEEE/CIC International Conference on Communications in China (ICCC), pp. 1–6 . https://doi.org/10.1109/ICCChina.2016.7636817

  16. Hajiakhondi-Meybodi, Z., Mohammadi, A., & Abouei, J. (2021). Deep reinforcement learning for trustworthy and time-varying connection scheduling in a coupled uav-based femtocaching architecture. IEEE Access, 9, 32263–32281. https://doi.org/10.1109/ACCESS.2021.3060323

    Article  Google Scholar 

  17. Wang, K., Cui, J., Ding, Z., & Fan, P. (2019). Stackelberg game for user clustering and power allocation in millimeter wave-NOMA systems. IEEE Transactions on Wireless Communications, 18(5), 2842–2857. https://doi.org/10.1109/TWC.2019.2908642

    Article  Google Scholar 

  18. Łukowa, A., & Venkatasubramanian, V. (2019). Centralized UL/DL resource allocation for flexible TDD systems with interference cancellation. IEEE Transactions on Vehicular Technology, 68(3), 2443–2458. https://doi.org/10.1109/TVT.2019.2893061

    Article  Google Scholar 

  19. Sun, X., & Wang, S. (2015). Resource allocation scheme for energy saving in heterogeneous networks. IEEE Transactions on Wireless Communications, 14(8), 4407–4416. https://doi.org/10.1109/TWC.2015.2420558

    Article  Google Scholar 

  20. Zhang, H., Li, H., & Lee, J. H. (2017). Efficient subchannel allocation based on clustered interference alignment in ultra-dense femtocell networks. China Communications, 14(4), 1–10. https://doi.org/10.1109/CC.2017.7927568

    Article  Google Scholar 

  21. Xu, H., Zhang, L., Zhou, X., & Han, Z. (2018). Stackelberg differential game based power control in small cell networks powered by renewable energy. In: 2018 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6 . https://doi.org/10.1109/WCNC.2018.8376996

  22. Ji, P., Jia, J., & Chen, J. (2019). Joint optimization on both routing and resource allocation for millimeter wave cellular networks. IEEE Access, 7, 93631–93642. https://doi.org/10.1109/ACCESS.2019.2928690

    Article  Google Scholar 

  23. Al-Tous, H., & Barhumi, I. (2019). Distributed reinforcement learning algorithm for energy harvesting sensor networks. In: 2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom), pp. 1–3 . https://doi.org/10.1109/BlackSeaCom.2019.8812862

  24. Wang, X., Zhang, Y., Shen, R., Xu, Y., & Zheng, F.-C. (2020). DRL-based energy-efficient resource allocation frameworks for uplink NOMA systems. IEEE Internet of Things Journal, 7(8), 7279–7294. https://doi.org/10.1109/JIOT.2020.2982699

    Article  Google Scholar 

  25. Hu, J., Wang, X., Li, D., & Xu, Y. (2020). Multi-agent drl-based resource allocation in downlink multi-cell OFDMA system. In: 2020 International Conference on Wireless Communications and Signal Processing (WCSP), pp. 257–262. https://doi.org/10.1109/WCSP49889.2020.9299746

  26. Li, W., Feng, L., Lin, Y., Zhao, Q., & Ou, Q. (2020). Network slicing cache deployment and resource allocation strategy for maximizing network revenue in 5GC-RANs. In: 2020 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), pp. 1–3. https://doi.org/10.1109/BMSB49480.2020.9379692

  27. Chen, M., Saad, W., & Yin, C. (2019). Liquid state machine learning for resource and cache management in lte-u unmanned aerial vehicle (UAV) networks. IEEE Transactions on Wireless Communications, 18(3), 1504–1517. https://doi.org/10.1109/TWC.2019.2891629

    Article  Google Scholar 

  28. Liu, X., Zhang, H., Long, K., Nallanathan, A., & Leung, V. C. M. (2021). Energy efficient user association, resource allocation and caching deployment in fog radio access networks. IEEE Transactions on Vehicular Technology, 1–1,. https://doi.org/10.1109/TVT.2021.3131720

  29. Tran, T. D., & Le, L. B. (2018). Joint resource allocation and content caching in virtualized content-centric wireless networks. IEEE Access, 6, 11329–11341. https://doi.org/10.1109/ACCESS.2018.2804902

    Article  Google Scholar 

  30. Tun, Y. K., Ndikumana, A., Pandey, S. R., Han, Z., & Hong, C. S. (2020). Joint radio resource allocation and content caching in heterogeneous virtualized wireless networks. IEEE Access, 8, 36764–36775. https://doi.org/10.1109/ACCESS.2020.2974287

    Article  Google Scholar 

  31. Nath, S., & Wu, J. (2020). Deep reinforcement learning for dynamic computation offloading and resource allocation in cache-assisted mobile edge computing systems. Intelligent and Converged Networks, 1(2), 181–198. https://doi.org/10.23919/ICN.2020.0014

    Article  Google Scholar 

  32. Zhang, T., Wang, Z., Liu, Y., Xu, W., & Nallanathan, A. (2020). Caching placement and resource allocation for cache-enabling UAV NOMA networks. IEEE Transactions on Vehicular Technology, 69(11), 12897–12911. https://doi.org/10.1109/TVT.2020.3015578

    Article  Google Scholar 

  33. Peethala, D., Kaiser, T., & Vinck, A. J. H. (2019). Reliability analysis of centralized radio access networks in non-line-of-sight and line-of-sight scenarios. IEEE Access, 7, 18311–18318. https://doi.org/10.1109/ACCESS.2019.2896410

    Article  Google Scholar 

  34. Jia, J., Deng, Y., Chen, J., Aghvami, A.-H., & Nallanathan, A. (2017). Availability analysis and optimization in CoMP and CA-enabled hetnets. IEEE Transactions on Communications, 65(6), 2438–2450. https://doi.org/10.1109/TCOMM.2017.2679747

    Article  Google Scholar 

  35. Dilli, R.(2020). Analysis of 5G wireless systems in FR1 and FR2 frequency bands. In: 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 767–772. https://doi.org/10.1109/ICIMIA48430.2020.9074973

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants No. 61972079, 62172084, 62132004, 61772126, 62032013, and 61872073, in part by the Major Research Plan of National Natural Science Foundation of China under Grant No. 92167103, in part by the Fundamental Research Funds for the Central Universities under Grants N2216009, N2016004, N2116004, N2216006, and N2224001-7, in part by the Central Government Guided Local Science and Technology Development Fund Project under Grant 2020ZY0003, in part by the Science and Technology Plan Project of Inner Mongolia Autonomous Region of China under Grant 2020GG0189, in part by the LiaoNing Revitalization Talents Program under Grants No. XLYC1902010 and XLYC2007162, in part by the Young and Middle-aged Scientific and Technological Innovation Talent Support Program of Shenyang under Grant RC200548.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, 100190, Liaoning, China

    Jie Jia, Linjiao Xia, Pengshuo Ji, Jian Chen & Xingwei Wang

  2. Engineering Research Center of Security Technology of Complex Network System, Ministry of Education, Shenyang, China

    Jie Jia

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  1. Jie Jia
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Jia, J., Xia, L., Ji, P. et al. Access probability optimization for streaming media transmission in heterogeneous cellular networks. Wireless Netw 28, 3231–3245 (2022). https://doi.org/10.1007/s11276-022-03014-9

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