Research on paging enhancements for 5G-A downlink transmission energy saving^☆

Abstract

5G-Advanced (5G-A), an evolutionary iteration of 5G, effectively enhances 5G services. The increasing complexity in downlink services scenarios stresses the necessity for research into the integration of efficient communication with low-carbon solutions. Historically, there has been an emphasis on reliability and precision, at the expense of power consumption. Although energy-saving technologies like Idle mode-Discontinuous Reception (IDRX) and Paging Early Indication (PEI) have been introduced to reduce power consumption in UE, they have not been fully tailored to the paging characteristics of 5G-A downlink services. In this paper, we take full account of the impact of paging message density on energy saving measures and propose an enhanced paging technology, termed Predictive-PEI (PPEI), which is designed to reduce UE overhead while minimizing latency whenever possible. Towards this end, we design a dual threshold decision framework founded on machine learning, mainly involving two steps. We first use the LSTM-FNN neural network to forecast the arrival moment of upcoming paging messages based on past real information. Then, the output of the initial prediction is as the input of the next dual threshold decision algorithm, to determine the optimal moment for transmitting the PEI. The restrictive factors, encompass average delay threshold and cache capacity threshold, playing a role in decisions regarding paging message caching and decoding. Compared to the existing schemes, our PPEI scheme flexibly sends efficient PEI according to the actual paging characteristics by introducing machine learning, resulting in substantial power savings of up to 38.89% while concurrently ensuring effective latency control.

Keywords

5G-A / Machine learning / IDRX / PEI

CRediT authorship contribution statement

Wenxin Ma: Writing - original draft, Formal analysis, Data curation, Conceptualization. Weidong Gao: Writing - review & editing, Supervision. Jiaqi Liu: Writing - review & editing, Software. Kaisa Zhang: Writing - review & editing. Xu Zhao: Visualization, Validation. Bingfeng Cui: Project administration, Investigation. Shujuan Sun: Resources, Methodology. Shurong Li: Project administration, Funding acquisition.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The research in this article is supported in part by the State Grid Corporation Headquarters Science and Technology Project (Project Code: 5108-202218280A-2-410-XG) and in part by Beijing New Generation Information and Communication Technology Innovation Project (Project Code: Z231100005923026).

References

[1] S. Vij, A. Jain,5G: evolution of a secure mobile technology,in:2016 3rd Interna-tional Conference on Computing for Sustainable Global Development (INDIACom), 2016, pp. 2192-2196.

[2] M. Li, M. Huo, X. Cheng, L. Xu, Research and application of AI in 5G network operation and maintenance, in: 2020 IEEE Intl Conf on Parallel & Distributed Pro-cessing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/Sus-tainCom), 2020, pp. 1420-1425.

[3] M. Yoon, J. Park, T. Park, J. Seo, J.-K. Yun, K. Cho,An Adaptive Handover Scheme to support UE with various movement speeds in 5G network, in: 2022 IEEE Interna-tional Conference on Consumer Electronics-Asia (ICCE-Asia), 2022, pp. 1-4.

[4] C.E. Daily, China mobile leads 6G architecture white paper and DOICT network convergence development initiative, http://www.cena.com.cn/5-g/20210917/113186.html, 2021. (Accessed 17 September 2021).

[5] T. Trainer, Becoming 5G-advanced: the 3GPP roadmap, https://www.telecomtrainer.com/becoming-5-g-advanced-the-3gpp-roadmap/, 2023. (Ac-cessed 16 March 2023).

[6] A. Fouda, R. Keating, H.-S. Cha, Toward cm-level accuracy: carrier phase position-ing for IIoT in 5G-advanced NR networks, in: 2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), 2022, pp. 782-787.

[7] S. Choi, J. Yoo, C. Kim,Implementation of UPF supporting ultra reliable low latency communication for 5G core, in:2022 13th International Conference on Information and Communication Technology Convergence (ICTC), 2022, pp. 2315-2318.

[8] S.S. Amiri, M. Rahmani, J.D. McDonald,An updated review on distribution manage-ment systems within a smart grid structure, in:2021 11th Smart Grid Conference (SGC), 2021, pp. 1-5.

[9] A.S. Gamal, H.K. Mohamed,Performance modelling of IoT in smart agriculture, in:2023 International Conference on Advances in Electronics, Communication, Com-puting and Intelligent Information Systems (ICAECIS), 2023, pp. 93-98.

[10] K.M. Hasan, M.T. Hasan, S.S. Newaz, Abdullah-Al-Nahid, M.S. Ahsan, Design and development of an autonomous pesticides spraying agricultural drone, in: 2020 IEEE Region 10 Symposium (TENSYMP), 2020, pp. 811-814.

[11] W. Lu, J. Li, H. Qin, L. Shu, A. Song, On dual-mode driving control method for a novel unmanned tractor with high safety and reliability, IEEE/CAA J. Autom. Sin. 10 (1) (2023) 254-271.

[12] D.B. Dash, G. Ponnamreddy, U.C. Baskar, D.P. Basavaraj, Adaptive DRX mechanism to improve energy efficiency and to reduce page delay for VoWiFi devices, in: 2022 IEEE Women in Technology Conference (WINTECHCON), 2022, pp. 1-5.

[13] M. Tayyab, N. Kolehmainen, M.M. Butt, A. Khlass, R. Ratasuk,Energy efficient RRM relaxation for reduced capability UEs in 5G networks, in: GLOBECOM 2022-2022 IEEE Global Communications Conference, 2022, pp. 99-104.

[14] Paging enhancement(s) for UE power saving in IDLE/inactive mode,3GPP TSG RAN WG 1 Meeting #103-e, R1-2007600 (2020).

[15] M.K. Maheshwari, E. Rastogi, A. Roy, N. Saxena, D.R. Shin, DRX in NR unlicensed for B5G wireless: modeling and analysis, IEEE Trans. Mob. Comput. 22 (9) (2023) 5184-5197.

[16] M.T. Abbas, J. Eklund, A. Brunstrom, S. Alfredsson, M. Rajiullah, K.-J. Grinnemo, G. Caso, K.Kousias, o. Alay, On the energy-efficient use of discontinuous reception and release assistance in NB-IoT, in: 2022 IEEE 8th World Forum on Internet of Things (WF-IoT), 2022, pp. 1-7.

[17] H. Ramazanali, A. Vinel, E. Yavuz, M. Jonsson, Modeling of LTE DRX in RRC idle state, in: 2017 IEEE 22nd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 2017, pp. 1-5.

[18] C.-W. Chang, J.-C. Chen, Adjustable extended discontinuous reception cycle for idle-state users in LTE-A, IEEE Commun. Lett. 20 (11) (2016) 2288-2291.

[19] Final Report of 3GPP TSG RAN WG1 #103-e v1.0.0, 3GPP TSG RAN WG1 Meeting #103-e ( 2020).

[20] Final Report of 3GPP TSG RAN WG1 #102-e v1.0.0, 3GPP TSG RAN WG1 Meeting #102-e ( 2020).

[21] A. Agiwal, M. Agiwal, Enhanced paging monitoring for 5G and beyond 5G networks, IEEE Access 10 (2022) 27197-27210.

[22] J. Wang, J. Tang, Z. Xu, Y. Wang, G. Xue, X. Zhang, D. Yang,Spatiotemporal model-ing and prediction in cellular networks: a big data enabled deep learning approach, in: IEEE INFOCOM 2017 - IEEE Conference on Computer Communications, 2017, pp. 1-9.

[23] S. Mahajan, H. R, K. Kotecha, Prediction of Network Traffic in Wireless Mesh Net-works Using Hybrid Deep Learning Model, vol. 10, 2022, pp. 7003-7015.

[24] M.L. Memon, M.K. Maheshwari, N. Saxena, A. Roy, D.R. Shin, Artificial intelligence-based discontinuous reception for energy saving in 5G networks, Electronics 8 (7) (2019) 778.

[25] Y.-H. Xu, X. Liu, W. Zhou, G. Yu, Generative adversarial LSTM networks learning for resource allocation in UAV-served M2M communications, IEEE Wirel. Commun. Lett. 10 (7) (2021) 1601-1605.

[26] H. Lee, S. Yoo, Y.-W. Kim,An energy management framework for smart factory based on context-awareness, in:2016 18th International Conference on Advanced Communication Technology (ICACT), 2016, pp. 685-688.

[27] Y.-K. Ke, C.-C. Li, Y.-H. Liao, Energy saving in smart grid, in: 2019 IEEE Workshop on Wide Bandgap Power Devices and Applications in Asia (WiPDA Asia), 2019, pp. 1-6.

[28] S. Mochizuki, N. Komuro, Power saving method using compressed sensing technique for IoT-based time-series environment monitoring system, in: 2021 IEEE Interna-tional Conference on Consumer Electronics-Taiwan (ICCE-TW), 2021, pp. 1-2.

[29] K. Liu, G. Cui, Q. Li, S. Zhang, W. Wang, X. Li, An optimal PSM duration dalculation algorithm for NB-IoT, in: 2019 IEEE 5th International Conference on Computer and Communications (ICCC), 2019, pp. 447-452.

[30] Z. Zhu, Z. Li, Z. Chu, G. Sun, W. Hao, P. Liu, I. Lee, Resource allocation for intelligent reflecting surface assisted wireless powered IoT systems with power splitting, IEEE Trans. Wirel. Commun. 21 (5) (2022) 2987-2998.

[31] Z. Zhu, J. Li, Z. Chu, J. Liang, H. Niu, D. Mi, C. Yin, P. Liu, Active reconfigurable in-telligent surface enhanced Internet of medical things, IEEE J. Biomed. Health Inform. (2023) 1-10.

[32] Z. Zhu, Z. Li, Z. Chu, Q. Wu, J. Liang, Y. Xiao, P. Liu, I. Lee, Intelligent reflecting surface-assisted wireless powered heterogeneous networks, IEEE Trans. Wirel. Com-mun. 22 (12) (2023) 9881-9892.

[33] V. Braun, K. Schober, E. Tiirola, 5G NR physical downlink control channel: design, performance and enhancements, in: 2019 IEEE Wireless Communications and Net-working Conference (WCNC), 2019, pp. 1-6.

[34] User Equipment (UE) Procedures in Idle Mode and RRC Inactive state, 3GPP TS 38.304 version 17.0.0 Release 17 (2022).

[35] J. Liu, K. Au, A. Maaref, J. Luo, H. Baligh, H. Tong, A. Chassaigne, J. Lorca, Ini-tial access, mobility, and user-centric multi-beam operation in 5G new radio, IEEE Commun. Mag. 56 (3) (2018) 35-41.

[36] M. Agiwal, A. Agiwal, M.K. Maheshwari, S. Muralidharan, Split PO for paging in B5G networks, J. Netw. Comput. Appl. 205 (2022) 103430.

[37] Y.-N.R. Li, M. Chen, J. Xu, L. Tian, K. Huang, Power saving techniques for 5G and beyond, IEEE Access 8 (2020) 108675-108690.

[38] Y. Liu, Y. Sun, C. Fu, Q. Wen,Research on end-to-end bearer scheme for precise con-trol of transmission network based on 5G communication technology, in:2022 3rd International Conference on Computer Vision, Image and Deep Learning & Inter-national Conference on Computer Engineering and Applications (CVIDL & ICCEA), 2022, pp. 499-501.

[39] M. Anagnostopoulos, G. Spathoulas, B. Viaño, J. Augusto-Gonzalez, Tracing your smart-home devices conversations: a real world IoT traffic data-set, Sensors 20 (22) (2020) 6600.

[40] Y. Shu, M. Yu, O. Yang, J. Liu, H. Feng, Wireless traffic modeling and prediction using seasonal ARIMA models, IEICE Trans. Commun. 88 (10) (2005) 3992-3999.

[41] J. Si, S.L. Harris, E. Yfantis, A dynamic ReLU on neural network, in: 2018 IEEE 13th Dallas Circuits and Systems Conference (DCAS), 2018, pp. 1-6.

[42] D.P. Kingma, J. Ba,Adam: a method for stochastic optimization, arXiv preprint, arXiv :1412.6980, 2014.

[43] M. Lauridsen, L.L. Sanchez, D. Laselva, J. Kaikkonen,Study of paging enhancements for UE energy saving in 5G new radio, in: 2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring), 2021, pp. 1-6.

[44] J. Liu, K. Au, A. Maaref, J. Luo, H. Baligh, H. Tong, A. Chassaigne, J. Lorca, Ini-tial access, mobility, and user-centric multi-beam operation in 5G new radio, IEEE Commun. Mag. 56 (3) (2018) 35-41.

[45] Final Report of 3GPP TSG RAN WG 1 #102-e v1.0.0 ( 2020).

[46] Study on User Equipment (UE) power saving in NR,3GPP TR 38.840 V16.0.0 (2019).

[47] F. Gers, J. Schmidhuber, F. Cummins,Learning to forget: continual prediction with LSTM,in:1999 Ninth International Conference on Artificial Neural Networks ICANN 99 (Conf. Publ. No.470), vol. 2, 1999, pp. 850-855.

[48] D.E. Ruíz-Guirola, C.A. Rodríguez-López, S. Montejo-Sánchez, R.D. Souza, O.L.A. López, H. Alves, Energy-efficient wake-up signalling for machine-type devices based on traffic-aware long short-term memory prediction, IEEE Int. Things J. 9 (21) (2022) 21620-21631.

[49] E. Tsironi, P. Barros, C. Weber, S. Wermter, An analysis of convolutional long short-term memory recurrent neural networks for gesture recognition, Neurocomputing 268 (2017) 76-86.

[50] Z. Zhu, J. Xu, G. Sun, W. Hao, Z. Chu, C. Pan, I. Lee, Robust beamforming design for IRS-aided secure SWIPT terahertz systems with non-linear EH model, IEEE Wirel. Commun. Lett. 11 (4) (2022) 746-750.

[51] H. Niu, Z. Chu, F. Zhou, Z. Zhu, L. Zhen, K.-K. Wong, Robust design for intelligent re-flecting surface-assisted secrecy SWIPT network, IEEE Trans. Wirel. Commun. 21 (6) (2022) 4133-4149.

[52] Z. Zhu, M. Ma, G. Sun, W. Hao, P. Liu, Z. Chu, I. Lee, Secrecy rate optimization in nonlinear energy harvesting model-based mmWave IoT systems with SWIPT, IEEE Syst. J. 16 (4) (2022) 5939-5949.