Abstract
In future wireless communications, there will be a large number of devices equipped with several different types of sensors need to access networks with diverse quality of service requirements. In cellular network evolution, the long term evolution advanced (LTE-A) networks has standardized Machine-to-Machine (M2M) features. Such M2M technology can provide a promising infrastructure for Internet of things (IoT) sensing applications, which usually require real-time data reporting. However, LTE-A is not designed for directly supporting such low-data-rate devices with optimized energy efficiency since it depends on core technologies of LTE that are originally designed for high-data-rate services. This paper investigate the maximum energy efficient data packets M2M transmission with uplink channels in LTE-A network. We formulate it into a jointed problem of Modulation and-Coding Scheme (MCS) assignment, resource allocation and power control, which can be expressed as a NP-hard mixed-integer linear fractional programming problem. Then we propose a global optimization scheme with Charnes-Cooper transformation and Glover linearization. The numerical experiment results show that with limited resource blocks, our algorithm can maintain low data packets dropping ratios while achieving optimal energy efficiency for a large number of M2M nodes, comparing with other typical counterparts.
Supported in part by grants from the National Natural Science Foundation of China (51877060), Fundamental Research Funds for the Central Universities, and ANHUI Province Key Laboratory of Affective Computing & Advanced Intelligent Machine, Grant No. ACAIM180102.
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Li, Q., Ge, Y., Yang, Y., Zhu, Y., Sun, W., Li, J. (2019). An Energy Efficient Uplink Scheduling and Resource Allocation for M2M Communications in SC-FDMA Based LTE-A Networks. In: Leung, V., Zhang, H., Hu, X., Liu, Q., Liu, Z. (eds) 5G for Future Wireless Networks. 5GWN 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 278. Springer, Cham. https://doi.org/10.1007/978-3-030-17513-9_9
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DOI: https://doi.org/10.1007/978-3-030-17513-9_9
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