تخصیص کارای توان الکتریکی در شبکه های دسترسی رادیویی ابری با رویه انتخاب فناوری
محورهای موضوعی : فناوری اطلاعات و ارتباطاتعلی اصغر انصاری 1 , محسن اسلامی 2 , محمد جواد دهقانی 3 , سعیده پارسایی فرد 4
1 - دانشگاه صنعتی شیراز
2 - استادیار دانشگاه
3 - هیات علمی
4 - دانشگاه تورنتو
کلید واژه: Multi access technology selection approach(MATSA) , C-RAN, OFDMA, and Massive MIMO,
چکیده مقاله :
در این مقاله، ما یک روش تخصیص بهره ور منابع با رویکرد اقتصادی را که در آن یک MNO با تعدادی از MVNO ها با یک مدل تجاری از پیش تعیین شده در تعامل هستند را بر مسیر فراسو یک شبکه بی سیم مجازی چند سلولی با معماری C-RAN در نظر می‌گیریم. در هر سلول از این شبکه، دو نوع فناوری، شامل OFDMA و Massive MIMO با قیمت‌های متفاوت برای عرضه به MVNOها توسط MNOموجود می‌باشد. روش پیشنهادی در واقع رویه انتخاب مناسب فناوری دسترسی چندگانه (MATSA) از میان دو نوع فناوری فوق با هدف کاهش هزینه های عملیاتی و به حداکثر رساندن سود MVNOs تحت یک مجموعه از قیود کمی و کیفی است. فرمول بندی این روش از تخصیص منابع، با یک تابع مطلوب جدید ارائه شده است. با توجه به وجود متغیرهای پیوسته و دودیی در مساله و همچنین تداخل بین سلولی در توابع نرخ داده، این مساله بهینه سازی از نوع غیر محدب با پیچیدگی محاسباتی بسیار بالا خواهد بود. برای حل این مساله، با استفاده از برنامه نویسی هندسی مکمل (CGP) و تقریب محدب متوالی (SCA)، یک الگوریتم تکرار شونده دو مرحله ای موثر برای تبدیل مساله بهینه سازی به دو زیر مساله برای یافتن فناوری و هزینه بهینهِ هر کاربر را توسعه داده‌ایم. نتایج شبیه سازی نشان می دهد که روش پیشنهادی ما (MATSA) از نظر افزایش کل بهره‌وری انرژی، کارآمدتر از سیستم‌های مجهز با یک نوع فناوری است بطوری که سود MVNO ها بیش از 13 درصد در مقایسه با قبل افزایش یافته است.
: In this paper, we consider an uplink economy-efficient resource allocation in a multicellular virtual wireless network with a C-RAN architecture where a MNO interacts with a number of MVNOs with a predetermined business model. In each cell of this system, two types of multiple access technologies, namely OFDMA and Massive MIMO, are available for MVNO at two different prices. In this setup, we propose a multi access technology selection approach (MATSA) with the objective to reduce operating costs and maximize the profit of the MVNOs subject to a set of constraints, and formulate this resource allocation problem with the new utility function. Due to the existence of continuous and binary variables in the formulated optimization problem and also the interference between cells in data rate functions, this optimization problem will be non-convex with very high computational complexity. To tackle this problem, by applying the complementary geometric programming (CGP) and the successive convex approximation (SCA), an effective two-step iterative algorithm is developed to convert the optimization problem into two sub problems with the aim to find optimum technology selection and power consumption parameters for each user in two steps, respectively. The simulation results demonstrate that our proposed approach (MATSA) with novel utility function is more efficient than the traditional approach, in terms of increasing total EE and reducing total power consumption. The simulation results illustrate that the profit of the MVNOs is enhanced more than 13% compared to that of the traditional approach.
[1] Cisco public (2020). Cisco Annual Internet Report (2018–2023). Available: https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.pdf
[2] ITU-R M.2370-0 (2015). IMT traffic estimates for the years 2020 to 2030, .https://www.itu.int/dms_pub/itu-r/opb/rep/R-REP-M.2370-2015-PDF-E.pdf.
[3] impacts-5g-productivity-economic-growth, https://www.communications.gov.au/publications/impacts-5g-productivity-and-economic-growth.
[4] Wu, Q., Li, G. Y., Chen, W., Ng, D. W. K., & Schober, R. (2017). An overview of sustainable green 5G networks. IEEE Wireless Communications, 24(4), 72-80.
[5] Peng, M., Sun, Y., Li, X., Mao, Z., & Wang, C. (2016). Recent advances in cloud radio access networks: System architectures, key techniques, and open issues. IEEE Communications Surveys & Tutorials, 18(3), 2282-2308.
[6] Wu, J., Zhang, Z., Hong, Y., & Wen, Y. (2015). Cloud radio access network (C-RAN): a primer. IEEE network, 29(1), 35-41.
[7] Peng, M., Li, Y., Zhao, Z., & Wang, C. (2015). System architecture and key technologies for 5G heterogeneous cloud radio access networks. IEEE network, 29(2), 6-14.
[8] Peng, M., Li, Y., Jiang, J., Li, J., & Wang, C. (2014). Heterogeneous cloud radio access networks: A new perspective for enhancing spectral and energy efficiencies. IEEE wireless communications, 21(6), 126-135.
[9] Peng, M., Zhang, K., Jiang, J., Wang, J., & Wang, W. (2014). Energy-efficient resource assignment and power allocation in heterogeneous cloud radio access networks. IEEE Transactions on Vehicular Technology, 64(11), 5275-5287.
[10] Kitindi, E. J., Fu, S., Jia, Y., Kabir, A., & Wang, Y. (2017). Wireless network virtualization with SDN and C-RAN for 5G networks: Requirements, opportunities, and challenges. IEEE Access, 5, 19099-19115.
[11] Topoloi, S. G., & Borcoci, E. (2018, June). Software Defined Networking and Network Function Virtualisation Cooperation-Experiments. In 2018 International Conference on Communications (COMM) (pp. 281-286). IEEE.
[12] Chartsias, P. K., Amiras, A., Plevrakis, I., Samaras, I., Katsaros, K., Kritharidis, D., & Escalona, E. (2017, June). SDN/NFV-based end to end network slicing for 5G multi-tenant networks. In 2017 European Conference on Networks and Communications (EuCNC) (pp. 1-5). IEEE.
[13] Marzouk, F., Barraca, J. P., & Radwan, A. (2020). On Energy Efficient Resource Allocation in Shared RANs: Survey and Qualitative Analysis. IEEE Communications Surveys & Tutorials, 22(3), 1515-1538.
[14] Oladejo, S. O., & Falowo, O. E. (2017, October). 5G network slicing: A multi-tenancy scenario. In 2017 Global Wireless Summit (GWS) (pp. 88-92). IEEE.
[15] Björnson, E., Hoydis, J., & Sanguinetti, L. (2017). Massive MIMO networks: Spectral, energy, and hardware efficiency. Foundations and Trends in Signal Processing, 11(3-4), 154-655.
[16] Wang, L., Wong, K. K., Elkashlan, M., Nallanathan, A., & Lambotharan, S. (2016). Secrecy and energy efficiency in massive MIMO aided heterogeneous C-RAN: A new look at interference. IEEE Journal of Selected Topics in Signal Processing, 10(8), 1375-1389.
[17] Zhou, F., Wu, Y., Hu, R. Q., Wang, Y., & Wong, K. K. (2018). Energy-efficient NOMA enabled heterogeneous cloud radio access networks. IEEE Network, 32(2), 152-160.
[18] Al-Abbasi, Z. Q., Rabie, K., & So, D. K. C. (2021). EE Optimization for Downlink NOMA-based Multi-Tier CRANs. IEEE Transactions on Vehicular Technology.
[19] Schneir, J. R., Konstantinou, K., Bradford, J., Zimmermann, G., Droste, H., Palancar, R. C., & Ajibulu, A. (2020). Cost assessment of multi-tenancy for a 5G broadband network in a dense urban area. Digital Policy, Regulation and Governance.
[20] Schneir, J. R., Konstantinou, K., Bradford, J., Zimmermann, G., Droste, H., Canto, R., & Ajibulu, A. (2017). Cost analysis of a 5G network with Multi-Tenancy options.
[21] Baghani, M., Parsaeefard, S., Derakhshani, M., & Saad, W. (2019). Dynamic non-orthogonal multiple access and orthogonal multiple access in 5G wireless networks. IEEE Transactions on Communications, 67(9), 6360-6373.
[22] Amani, N., Pedram, H., Taheri, H., & Parsaeefard, S. (2018). Energy-efficient resource allocation in heterogeneous cloud radio access networks via BBU offloading. IEEE Transactions on Vehicular Technology, 68(2), 1365-1377.
[23] Zhao, W., & Wang, S. (2016, May). Remote radio head selection for power saving in cloud radio access networks. In 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring) (pp. 1-5). IEEE.
[24] Tohidi, M., Bakhshi, H., & Parsaeefard, S. (2020). Joint uplink and downlink delay‐aware resource allocation in C‐RAN. Transactions on Emerging Telecommunications Technologies, 31(3), e3778.
[25] Kazmi, S. A., Tran, N. H., Ho, T. M., & Hong, C. S. (2017). Hierarchical matching game for service selection and resource purchasing in wireless network virtualization. IEEE Communications Letters, 22(1), 121-124.
[26] Ye, J., & Zhang, Y. J. (2019). Pricing-based resource allocation in virtualized cloud radio access networks. IEEE Transactions on Vehicular Technology, 68(7), 7096-7107.
[27] Wang, G., Feng, G., Tan, W., Qin, S., Wen, R., & Sun, S. (2017, December). Resource allocation for network slices in 5G with network resource pricing. In GLOBECOM 2017-2017 IEEE Global Communications Conference (pp. 1-6). IEEE.
[28] Jiang, M., Condoluci, M., & Mahmoodi, T. (2017, May). Network slicing in 5G: An auction-based model. In 2017 IEEE International Conference on Communications (ICC) (pp. 1-6). IEEE.
[29] Morcos, M., Chahed, T., Chen, L., Elias, J., & Martignon, F. (2018). A two-level auction for resource allocation in multi-tenant C-RAN. Computer Networks, 135, 240-252.
[30] Zhang, Y., Bi, S., & Zhang, Y. J. (2016, December). A two-stage spectrum leasing optimization framework for virtual mobile network operators. In 2016 IEEE International Conference on Communication Systems (ICCS) (pp. 1-6). IEEE.
[31] Zhu, K., & Hossain, E. (2015). Virtualization of 5G cellular networks as a hierarchical combinatorial auction. IEEE Transactions on Mobile Computing, 15(10), 2640-2654.
[32] Hao, Y., Ni, Q., Li, H., & Hou, S. (2017). On the energy and spectral efficiency tradeoff in massive MIMO-enabled HetNets with capacity-constrained backhaul links. IEEE Transactions on Communications, 65(11), 4720-4733.
[33] Parsaeefard, S., Dawadi, R., Derakhshani, M., Le-Ngoc, T., & Baghani, M. (2017). Dynamic resource allocation for virtualized wireless networks in massive-MIMO-aided and fronthaul-limited C-RAN. IEEE Transactions on Vehicular Technology, 66(10), 9512-9520.
[34] Liu, Y., Derakhshani, M., Parsaeefard, S., Lambotharan, S., & Wong, K. K. (2018). Antenna Allocation and Pricing inVirtualized Massive MIMO Networks via Stackelberg Game. IEEE Transactions on Communications, 66(11), 5220-5234.
[35] Wang, X. (2017). Spectrum and energy efficiency of uplink massive MIMO system with D2D underlay. Future Internet, 9(2), 12.
[36] Li, Y., Tao, C., Mezghani, A., Swindlehurst, A. L., Seco-Granados, G., & Liu, L. (2016). Optimal design of energy and spectral efficiency tradeoff in one-bit massive MIMO systems. arXiv preprint arXiv:1612.03271.
[37] Hu, Y., Ji, B., Huang, Y., Yu, F., & Yang, L. (2015). Energy-efficient resource allocation in uplink multiuser massive MIMO systems. International Journal of Antennas and Propagation, 2015.
[38] Parsaeefard, S., Dawadi, R., Derakhshani, M., & Le-Ngoc, T. (2016). Joint user-association and resource-allocation in virtualized wireless networks. IEEE Access, 4, 2738-2750.
[39] Chiang, M. (2005). Geometric programming for communication systems. Now Publishers Inc.
[40] Chiang, M., Tan, C. W., Palomar, D. P., O'neill, D., & Julian, D. (2007). Power control by geometric programming. IEEE transactions on wireless communications, 6(7), 2640-2651.
[41] Xu, G. (2014). Global optimization of signomial geometric programming problems. European journal of operational research, 233(3), 500-510.
[42] Tuy, H. (1995). DC optimization: theory, methods and algorithms. In Handbook of global optimization (pp. 149-216). Springer, Boston, MA.
[43] Grant, M. and Boyd, S. (2017). CVX: MATLAB software for disciplined convex programming, in Version 2.1. http://cvxr.com/cvx.
