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author:

Zhao, Y. (Zhao, Y..) [1] | Ji, H. (Ji, H..) [2] | Chen, Z. (Chen, Z..) [3]

Indexed by:

Scopus

Abstract:

High-speed train communication system is a typical high-mobility wireless communication network. Resource allocation problem has a great impact on the system performance. However, conventional resource allocation approaches in cellular network cannot be directly applied to this kind of special communication environment. A multi-domain resource allocation strategy was proposed in the orthogonal frequency-division multiple access (OFDMA) of high-speed. By analyzing the effect of Doppler shift, sub-channels, antennas, time slots and power were jointly considered to maximize the energy efficiency under the constraint of total transmission power. For the purpose of reducing the computational complexity, noisy chaotic neural network algorithm was used to solve the above optimization problem. Simulation results showed that the proposed resource allocation method had a better performance than the traditional strategy. © 2014, Tianjin University and Springer-Verlag Berlin Heidelberg.

Keyword:

high-speed train; noisy chaotic neural network; orthogonal frequency-division multiple access (OFDMA); resource allocation

Community:

  • [ 1 ] [Zhao, Y.]Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, 100876, China
  • [ 2 ] [Ji, H.]Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing, 100876, China
  • [ 3 ] [Chen, Z.]College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350116, China

Reprint 's Address:

  • [Ji, H.]Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and TelecommunicationsChina

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Source :

Transactions of Tianjin University

ISSN: 1006-4982

Year: 2014

Issue: 5

Volume: 20

Page: 368-374

6 . 7 0 0

JCR@2023

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count: 2

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 3

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