DOI QR코드

DOI QR Code

Resource scheduling scheme for 5G mmWave CP-OFDM based wireless networks with delay and power allocation optimizations

  • Marcus Vinicius G. Ferreira (Instituto de Informatica, Universidade Federal de Goias) ;
  • Flavio H. T. Vieira (Instituto de Informatica, Universidade Federal de Goias) ;
  • Alisson A. Cardoso (Escola de Engenharia Eletrica e da Computacso, Universidade Federal de Goias)
  • Received : 2020.04.21
  • Accepted : 2022.10.21
  • Published : 2023.02.20

Abstract

In this paper, to optimize the average delay and power allocation (PA) for system users, we propose a resource scheduling scheme for wireless networks based on Cyclic Prefix Orthogonal Frequency Division Multiplexing (CP-OFDM) according to the first fifth-generation standards. For delay minimization, we solve a throughput maximization problem that considers CPOFDM systems with carrier aggregation (CA). Regarding PA, we consider an approach that involves maximizing goodput using an effective signal-to-noise ratio. An algorithm for jointly solving delay minimization through computation of required user rates and optimizing the power allocated to users is proposed to compose the resource allocation approach. In wireless network simulations, we consider a scenario with the following capabilities: CA, 256-Quadrature Amplitude Modulation, millimeter waves above 6 GHz, and a radio frame structure with 120 KHz spacing between the subcarriers. The performance of the proposed resource allocation algorithm is evaluated and compared with those of other algorithms from the literature using computational simulations in terms of various Quality of Service parameters, such as the throughput, delay, fairness index, and loss rate.

Keywords

References

  1. X. Liu, M. Jia, X. Zhang, and W. Lu, A novel multichannel internet of things based on dynamic spectrum sharing in 5G communication, IEEE Internet Things J. 6 (2019), no. 4, 5962-5970.  https://doi.org/10.1109/JIOT.2018.2847731
  2. X. Liu and X. Zhang, Noma-based resource allocation for cluster-based cognitive industrial internet of things, IEEE Trans. Ind. Inf. 16 (2020), no. 8, 5379-5388.  https://doi.org/10.1109/TII.2019.2947435
  3. S. Rostami, K. Arshad, and P. Rapajic, A joint resource allocation and link adaptation algorithm with carrier aggregation for 5 G LTE -advanced network, (2015 22nd International Conference on Telecommunications, Sydney, Australia), 2015, pp. 102-106. 
  4. O. Jo, J. Kim, J. Yoon, D. Choi, and W. Hong, Exploitation of dual-polarization diversity for 5G millimeter-wave mimo beamforming systems, IEEE Trans. Antennas Propag. 65 (2017), no. 12, 6646-6655.  https://doi.org/10.1109/TAP.2017.2761979
  5. N. Guan, Y. Zhou, L. Tian, G. Sun, and J. Shi, Qo S guaranteed resource block allocation algorithm for LTE systems, (IEEE 7th International Conference on Wireless and Mobile Computing, Networking and Communications, Shanghai, China), 2011, pp. 307-312. 
  6. L. Su, P. Wang, and F. Liu, Particle swarm optimization based resource block allocation algorithm for downlink lte systems, (18th Asia-Pacific Conference on Communications, Jeju, Rep. of Korea), 2012, pp. 970-974. 
  7. M. V. G. Ferreira, F. H. T. Vieira, M. Castro, S. Araujo, and F. G. C. Rocha, Alocacao de blocos de recurso em redes lte comestimativa de limitante de retardo atraves de calculo de rede, (XXXIII Simposio Brasileiro de Telecomunicacoes (sbrt)), 2015. 
  8. P. Del Fiorentino, C. Vitiello, V. Lottici, F. Giannetti, and M. Luise, A robust resource allocation algorithm for packet BIC-UFMC 5G wireless communications, (24th European Signal Processing Conference, Budapest, Hungary), 2016, pp. 843-847. 
  9. C. Zhou and G. Wunder, Delay and throughput optimal scheduling for OFDM broadcast channels, (Proc. IEEE globecom 2007. IEEE International Symposium on Information Theory (isit2007)), 2007. 
  10. A. Gupta and R. K. Jha, A survey of 5G network: Architecture and emerging technologies, IEEE Access 3 (2015), 1206-1232.  https://doi.org/10.1109/ACCESS.2015.2461602
  11. M. V. G. Ferreira and F. H. T. Vieira, Optimizing resource allocation of wireless networks with carrier aggregation using evolutionary programming, Advances in nature-inspired computing and applications, S. K. Shandilya, S. Shandilya, and A. K. Nagar, (eds.), Springer International Publishing, Cham, 2019, pp. 27-41. https://doi.org/10.1007/978-3-319-96451-5_2 
  12. I. Stupia, V. Lottici, F. Giannetti, and L. Vandendorpe, Link resource adaptation for multiantenna bit-interleaved coded multicarrier systems, IEEE Trans. Signal Processing 60 (2012), no. 7, 3644-3656.  https://doi.org/10.1109/TSP.2012.2192110
  13. 3GPP, 3GPP TS 36.213 version 10.4.0 release 10. LTE; evolved universal terrestrial radio access (E-UTRA); physical layer procedures, 2012. 
  14. 3GPP, 3GPP TS 36.300 version 11.5.0 release 11. LTE; evolved universal terrestrial radio access (E-UTRA) and evolved universal terrestrial radio access network (E-UTRAN); overall description, 2013. 
  15. 3GPP, 3GPP TS 36.213 version 13.0.0 release 13. LTE; evolved universal terrestrial radio access (E-UTRA); physical layer procedures, 2016. 
  16. 3GPP, 3GPP TR 38.901 version 14.0.0 release 14. 5G; study on channel model for frequencies from 0.5 to 100 ghz, 2017. 
  17. 3GPP, 3GPP TS 38.211 version 15.2.0 release 15. 5G; NR; physical channels and modulation, 2018. 
  18. 3GPP, 3GPP TS 38.104 version 15.2.0 release 15. 5G; NR; base station (bs) radio transmission and reception, 2018. 
  19. M. V. G. Ferreira, F. H. T. Vieira, and M. N. L. Carvalho, A resource allocation scheme with delay optimization considering mmwave wireless networks, Int. J. Commun., Netw. Syst. Sci. 13 (2020), 105-119.  https://doi.org/10.4236/ijcns.2020.137007
  20. M. V. G. Ferreira and F. H. T. Vieira, Delay minimization based uplink resource allocation for device-to-device communications considering mmwave propagation, Peer J. Comput. Sci. 7 (2021), e462. 
  21. S. Boyd, S. P. Boyd, L. Vandenberghe, and C. U. Press, Convex optimization, Berichte uber verteilte messysteme, Cambridge University Press, 2004. 
  22. D. P. Bertsekas, Nonlinear programming, Athena Scientific, 1995. 
  23. F. Darbari, R. W. Stewart, and I. A. Glover, Mimo channel modelling, Signal processing, S. Miron, (ed.), IntechOpen, Rijeka, 2010. https://doi.org/10.5772/8530 
  24. Wand Network Research Group University of Waikato, Series reais de trafego tcp/ip, 2019. http://wand.net.nz/wits/waikato/8/, Jun 01, 2019. 
  25. D. C. Abrah, 2018. Escalonamento de recursos em redes lte utilizando processo envelope de trafego multifractal e curva de servico minima, Ph.D. Thesis, 2018, Escola de Engenharia Eletrica, Mecanica e de Computacao - EMC (RG). http://repositorio.bc.ufg.br/tede/handle/tede/9221 
  26. M. Ni, X. Xu, and R. Mathar, A channel feedback model with robust sinr prediction for lte systems, (7th European Conference on Antennas and Propagation, Gothendurg, Sweden), 2013, pp. 1866-1870. 
  27. Tecnoblog, mmwave: o que sao as ondas milimetricas que farao o 5g funcionar em altissimas frequencias. May 2019. 
  28. Ericsson, In the race to 5g, cp-ofdm triumphs!, 2019. https://www.ericsson.com/en/blog/2017/5/in-the-race-to-5g-cp-ofdmtriumphs, https://tecnoblog.net/270324/5g-nr-mmwave-altasfrequencias-ondas-milimetricas/, May 27, 2019. 
  29. A. A. Zaidi, R. Baldemair, H. Tullberg, H. Bjorkegren, L. Sundstrom, J. Medbo, C. Kilinc, and I. Da Silva, Waveform and numerology to support 5G services and requirements, IEEE Commun. Mag. 54 (2016), no. 11, 90-98.  https://doi.org/10.1109/MCOM.2016.1600336CM
  30. M. K. Samimi and T. S. Rappaport, 3-D millimeter-wave statistical channel model for 5G wireless system design, IEEE Trans. Microw. Theory Tech. 64 (2016), no. 7, 2207-2225.  https://doi.org/10.1109/TMTT.2016.2574851
  31. Y. Li, J. Luo, W. Xu, N. Vucic, E. Pateromichelakis, and G. Caire, A joint scheduling and resource allocation scheme for millimeter wave heterogeneous networks, (IEEE Wireless Communications and Networking Conference, San Francisco, CA, USA), 2017, pp. 1-6.