Abstract
Orthogonal frequency division multiplexing (OFDM) is a multicarrier modulation used in a variety of broadband systems, such as asymmetric digital subscriber lines, very-high-speed digital subscriber lines, digital video and audio broadcasting, and wide local area network standards. OFDM is a promising solution achieving high data rates in a mobile environment. In wireless communication systems, signal distortion and attenuation in transmission caused by multipath effects makes it necessary to obtain knowledge related to the channel impulse response using channel estimation to provide compensation. This study combined a back propagation neural network for the estimation of channel and compensation signals with a genetic algorithm to improve performance and the convergence rate. We compared bit error rates and the mean square error of the proposed neural network with that of the conventional neural network, the least square (LS) algorithm, and the minimum mean square error (MMSE) algorithm in existing OFDM channel environments. Our results demonstrate that the proposed algorithm outperforms the LS algorithm and is on par with the MMSE algorithm.
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References
Alias MY, Chen S, Hanzo L (2005) Multiple-antenna-aided OFDM employing genetic-algorithm-assisted minimum bit error rate multiuser detection. IEEE Trans Veh Technol 54:1713–1721
Amiri A, Niaki STA, Moghadam AT (2015) A probabilistic artificial neural network-based procedure for variance change point estimation. Soft Comput 19(3):691–700
Cho YS, Kim J, Yang WY, Kang CG (2010) MIMO-OFDM wireless communications with MATLAB. Wiley, New York
Cimini LJ (1985) Analysis and simulation of digital mobile channel using orthogonal frequency multiplex. IEEE Trans Commun 33:665–675
Cui T, Tellambura C (2004) Channel estimation for OFDM systems based on adaptive radial basis function network. In: IEEE 60th vehicular technology conference, pp 608–611
Guo P, Wang X, Han Y (2010) The enhanced genetic algorithms for the optimization design. International conference on biomedical engineering and informatics (BMEI), vol 7, pp 2990–2994
Ibrahim NK, Abdullah RSAR, Saripan MI (2009) Artificial neural network approach in radar target classification. J Comput Sci 5(1):23–32
Jeremic A, Thomas TA (2004) OFDM channel estimation in presence of interference. IEEE Trans Signal Proc 52(10):3429–3439
Jiang M, Hanzo L (2007) Multiuser MIMO-OFDM for next-generation wireless systems. Proc IEEE 95(7):1430–1469
Lee SH, Hung HL (2010) GA-based pilot-aided channel estimation without interpolation in OFDM systems. In: International Symposium on Computer Communication Control and Automation (3CA), vol 2, pp 466–469
Lippmann RP (1987) An introduction to computing with neural nets. IEEE ASSP Magazine 4(2):4–22
Li F, Zhou M, Li H (2011) A novel neural network optimized by quantum genetic algorithm for signal detection in MIMO-OFDM systems. In: IEEE symposium on computational intelligence in control and automation (CICA), pp 170–177
Mignone V, Morello A (1996) CD3-OFDM: a novel demodulation schemes for fixed and mobile receivers. IEEE Trans Commun 44:1144–1151
Nawaz SJ, Mohsin S, Ikaram AA (2009) Neural network based MIMO-OFDM channel equalizer using comb-type pilot arrangement. In: International conference on future computer and communication, pp 36–41
Ogiela L, Ogiela MR (2011) Semantic analysis processes in advanced pattern understanding systems, CCIS - communications in computer and information science, vol 195. Springer, Berlin, pp 26–30
Rahman QM, Ibnkahla M, Bayoumi MM (2005) Neural network based channel estimation and performance evaluation of time varying multipath satellite channel. In: Proceedings of the 3rd annual communication networks and services research conference, pp 74–79
Ramezani F, Nikoo M, Nikoo M (2015) Artificial neural network weights optimization based on social-based algorithm to realize sediment over the river. Soft Comput 19(2):375–387
Rojas R (1996) Neural networks: a systematic introduction. Springer, Berlin
Ruan G, Tan Y (2010) A three-layer back-propagation neural network for spam detection using artificial immune concentration. Soft Comput 14(2):139–150
Srivastava JR, Sudarshan TSB (2014) Energy-efficient cache node placement using genetic algorithm in wireless sensor networks. Soft Computing, pp 1–14
Taspinar N, Seyman MN (2010) Back propagation neural network approach for channel estimation in OFDM system. In: IEEE international conference on wireless communications, networking and information security (WCNIS), pp 265–268
Zhang TL (2011) Solving non-linear equation based on steepest descent method. In: International conference on information and computing (ICIC), pp 216–218
Zhang YM, Zhang YB, Lin LZ (2010) Application of genetic algorithm and RBF neural network in network flow prediction. In: IEEE international conference on computer science and information technology (ICCSIT), vol 2, pp 298–301
Zhou B, Wang X (2003) Channel estimation for OFDM systems using adaptive radial basis function networks. IEEE Trans Veh Technol 52:48–59
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Cheng, CH., Huang, YH. & Chen, HC. Channel estimation in OFDM systems using neural network technology combined with a genetic algorithm. Soft Comput 20, 4139–4148 (2016). https://doi.org/10.1007/s00500-015-1749-7
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DOI: https://doi.org/10.1007/s00500-015-1749-7