DOI: https://doi.org/10.20535/kpi-sn.2020.3.203863

RESOURCE ALLOCATION FOR LOW-POWER DEVICES OF M2M TECHNOLOGY IN 5G NETWORKS

Andrew V. Bulashenko

Abstract


Background. One of the key tasks in 5G networks is the resource allocation for machine communication devices (MCD), since they affect the battery life of devices and the quality of service for applications. The MCD system usually consists of small devices and sensors. MCD cannot afford high energy consumption, as their battery life is limited due to the difficulty of replacing or charging them.

Objective. The purpose of the work is to increase energy efficiency while increasing the autonomous distribution resource on MCD. To achieve this goal, the task is allotted to create a resource allocation scheme using a controlled power limit of M2M small devices of 5G communication system.

Methods. The article proposes a new resource allocation algorithm that uses traffic capacity controlled access. This is an uplink resource allocation technique in which devices decide to allocate resource blocks based on their priority and power level of the corresponding application.

Results. The method consists of two stages. At the first stage, the number of carriers that should be allocated for a certain MCD device with low energy consumption is selected in order to increase its service life. At the second stage, an effective solution is implemented by introducing a traffic capacity value. A specific traffic capacity value is selected based on the QoS metric. This traffic capacity improves carrier selection for less powerful devices.

Conclusions. The presented method is used for the physical layer of a 5G network. The results obtained show that the proposed algorithm is simpler and achieves better performance compared to existing ones, as it provides shorter delay times, greater energy efficiency and greater traffic capacity.


Keywords


Machine-to-machine; Machine-type communication; Energy efficiency; Quality of service; Traffic capacity

References


V. Gazis, “A survey of standards for machine-to-machine and Internet of Things”, IEEE Communications Surveys and Tutorials, vol. 19, no. 1, pp. 482–511, 2016. doi: 10.1109/COMST.2016.2592948

Z. Yang et al., “Energy efficient resource allocation in machine-to-machine communications with multiple access and energy harvesting for IoT”, IEEE Internet of Things Journal, vol. 5, no. 1, pp. 229–245, 2018. doi: 10.1109/JIOT.2017.2778766

R. Chai et al., “Energy efficient optimization-based joint resource allocation and clustering algorithm for M2M communication system”, IEEE Access, vol. 7, pp. 168507–168519, 2019. doi: 10.1109/ACCESS.2019.2954713

Y. Wu et al., “Non-ortogonal random access and data transmission Scheme for machine-to-machine communications in cellular networks”, IEEE Access, vol. 8, pp. 27687–27704, 2020. doi: 10.1109/ACCESS.2020.2972064

A.V. Bulashenko and M.O. Dragan, “Energy efficiency of М2М systems in cellular networks”, in Proc. V All-Ukrainian Sci. Tech. Conf., Shostka, Ukraine, April 23, 2020, pp. 178–179.

H. Shariatmadari et al., “Machine-type communications: current status and future perspectives toward 5G systems”, IEEE Communications Magazine, vol. 53, no. 9, pp. 10–17, 2015. doi: 10.1109/MCOM.2015.7263367

A. Ali et al., “Energy efficient uplink MAC protocol for M2M devices”, IEEE Access, vol. 7, pp. 35952–35962, 2019. doi: 10.1109/ACCESS.2019.2903647

K. Selvam and K. Kumar, “A novel energy efficient resource allocation algorithm for non-orthogonal multiple access based M2M communication”, in Proc. IEEE 7th Int. Conf. On Signal Processing and Integrated networks, 2020, ID 9071332. doi: 10.1109/SPIN48934.2020.9071332

M. Li et al., “Energy efficient machine-to-machine communications in virtualized cellular networks with mobile edge computing”, IEEE Transactions on Mobile Computing, vol. 18, no. 7, pp. 1541–1555, 2019. doi: 10.1109/TMC.2018.2865312

Y. Mehmood et al., “M2M communications in 5G: state of the art architecture, recent advances and research challenges”, IEEE Communications Magazine, vol. 55, no. 9, pp. 194–201, 2017. doi: 10.1109/MCOM.2017.1600559

A.V. Bulashenko and V.V. Gladun, “Providing the required delay time in 5G networks”, in Proc. III All-Ukrainian Sci. Tech. Conf. “Radio Electronics in the XXI Century”, Kyiv, Ukraine, May 12–15, 2020, pp. 9–12.

A.V. Bulashenko and I.V. Demchenko, “Slicing technology for 5G networks”, in Proc. XXIV Int. Forum of Young Scientists “Radio Electronics and Youth in the XXI Century”, Kharkiv, Ukraine, April 16–18, 2019, vol. 4, pp. 129–130.

A.V. Bulashenko and S.V. Tsapkov, “Cross entropy method to reduce the PAPR ratio in OFDM systems”, in Proc. Int. Sci. Tech. Conf. “Radio fields, signals, devices and systems”, Kyiv, Ukraine, November 18–24, 2019, pp. 51–53.

D. Malak et al., “Optimizing data aggregation for uplink machine-to-machine communications networks”, IEEE Transactions on Communications, vol. 64, no. 3, pp. 1274–1290, 2016. doi: 10.1109/TCOMM.2016.2517073

M. Aziz and P.E. Caines, “A mean field game computational methodology for decentralized cellular network optimization”, IEEE Transactions on Control Systems Technology, vol. 25, no. 2, pp. 563–576, 2016. doi: 10.1109/TCST.2016.2558458

V. Angelakis et al., “Allocation of heterogeneous resources of an IoT device to Flexible services”, IEEE Internet of Things Journal, vol. 3, no. 5, pp. 691–700, 2016. doi: 10.1109/JIOT.2016.2535163

Q. Li et al., “Energy efficient computation offloading and resource allocation in fog computing for Internet of Everything”, China Communications, vol. 16, no. 3, pp. 32–41, 2019.

N. Zhang et al., “Resource allocation in a new random access for M2M communications”, IEEE Communications Letters, vol. 19, no. 5, pp. 843–846, 2015. doi: 10.1109/LCOMM.2015.2413961

S. Li et al., “Energy efficient resource allocation for industrial cyber-physical IoT systems in 5G era”, IEEE Transactions on Industrial Informatics, vol. 14, no. 6, pp. 2618–2628, 2018. doi: 10.1109/TII.2018.2799177

J.S. Kumar and M.A. Zaveri, “Graph-based resource allocation for disaster management in IoT environment”, in Proc. IEEE Second Int. Conf. On Advanced Wireless Information, 2017, ID 3231830.3231842. doi: 10.1145/3231830.3231842

S.K. Sharma and X. Wang, “Toward massive machine type communications in ultra-dense cellular IoT networks: current issues and machine learning-assisted solutions”, IEEE Communications Surveys and Tutorials, vol. 22, no. 1, pp. 426–471, 2020. doi: 10.1109/COMST.2019.2916177

F. Schaich and T. Wild, “Waveform contenders for 5G – OFDM vs. FBMC vs. UFMC”, in Proc. IEEE 6th Int. Conf. on Communications, Control and Signak Processing, 2014, ID 6877912. doi: 10.1109/ISCCSP.2014.6877912

M. Condoluci et al., “Enabling the IoT machine age with 5G: machine-type multicast services for innovative real-time applications”, IEEE Access, vol. 4, pp. 5555–5569, 2016. doi: 10.1109/ACCESS.2016.2573678

A.I. Sulyman et al., “Radio propagation path loss models for 5G cellular networks in the 28 GHZ and 38 GHZ millimeter-wave bands”, IEEE Commun. Mag., vol. 52, no. 9, pp. 78–86, 2014. doi: 10.1109/MCOM.2014.6894456

A.B. Bulashenko, “Evaluation of D2D Communications in 5G networks”, Visnik NTUU KPI Seriia Radiotekhnika, Radioaparatobuduvannia, vol. 81, pp. 21–29, 2020. doi: 10.20535/RADAP.2020.81.21-29


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