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T. Necmi and S. M. Nuri, “Back Propagation Neural Network Approach for Channel Estimation in OFDM System, Wireless Communications, Networking and Information Security (WCNIS),” 2010 IEEE International Conference, Anchorage, 3-8 May 2010, pp. 265-268.

has been cited by the following article:

• TITLE: MAC Sub-Layer Analysis with Channel Estimation in Broadband Power Line Communication

  AUTHORS: Mohammad Khaled Andari, Seyed Ali Asghar Beheshti

  KEYWORDS: BPLC, Back-Propagation Neural Network (BPNN), Channel Estimation, Throughput, Access Delay

  JOURNAL NAME: Communications and Network, Vol.3 No.3, August 10, 2011

  ABSTRACT: Broadband power line communication (BPLC) gained a lot of interest because of low cost and high performance communication network in access area. In this paper physical (PHY) layer and medium access control (MAC) sub-layer of BPLC are considered. Furthermore, effects of bit error rate (BER) are analyzed in MAC sub-layer. Powerful turbo convolutional code (TCC) and wideband orthogonal frequency division multiplexing (OFDM) are used in PHY layer. Carrier sense multiple access (CSMA) and virtual slot multiple access (VSMA) are taken into consideration in MAC sub-layer. Multilayered perceptrons neural network with backpropagation (BP) learning channel estimator algorithm compare to classic algorithm in for channel estimating. The simulation results show that the proposed neural network estimation decreases bit error rate then in MAC sub-layer throughput increases and access delay is decreased.