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Energy-Efficient Power Allocation Scheme Based on Discrete-Rate Adaptive Modulation in Distributed Antenna System

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Abstract

In this paper, the energy efficiency (EE) for a distributed antenna system (DAS) with discrete-rate adaptive modulation (AM) is investigated, and an optimal adaptive power allocation (PA) scheme for maximizing EE is developed. First of all, the system model of DAS based on discrete-rate AM is presented. Then, subject to transmit power per antenna and target bit error rate (BER), a constrained optimized problem is formulated to maximize EE of DAS. By solving KKT conditions, we derive the optimal solution as a closed form. The obtained closed-form expression is applicable to DAS with an arbitrary number of distributed antennas (DA) ports and general per-DA port power and target BER constraints. To illustrate the validity of the developed scheme, the exhaustive search method is used in the simulation to compare with the developed scheme. As a result, the proposed power allocation method produces the EE and spectrum efficiency (SE) identical to the exhaustive search method with remarkably reduced computational complexity. Moreover, the EE and SE of the DAS with AM increase as the target BER increases.

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Acknowledgement

This work is partially supported by National Natural Science Foundation of China (61601220), Open Research Fund of National Mobile Communications Research Laboratory of Southeast University (2017D03), and Six Talent Peaks Project in Jiangsu (2015-DZXX-007).

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Correspondence to Xi Wang .

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Wang, X., Yu, X., Teng, T., Wang, G. (2018). Energy-Efficient Power Allocation Scheme Based on Discrete-Rate Adaptive Modulation in Distributed Antenna System. In: Meng, L., Zhang, Y. (eds) Machine Learning and Intelligent Communications. MLICOM 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 251. Springer, Cham. https://doi.org/10.1007/978-3-030-00557-3_29

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  • DOI: https://doi.org/10.1007/978-3-030-00557-3_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-00556-6

  • Online ISBN: 978-3-030-00557-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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