Direct sequence estimation: a functional network approach
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Functional networks (FNs) have shown excellent performance in probability, statistics, engineering applications, etc., but so far no methods of direct sequence estimation (DSE) for communication systems using FN have been published. The paper presents a new DSE approach using FN, which can be applied to cases with plural source signal sequence, short sequence or even the absence of training sequence. The proposed method can estimate the source sequence directly from the observed output data without training sequence and pre-estimating the channel impulse response. Firstly, a multiple-input multiple-output FN (MIMOFN), in which the initial input vector is devised via QR decomposition of receiving signal matrix, is adopted to solve the special issue. Meantime, a design method of the neural function for this special MIMOFN is proposed. Then, the learning rule for the parameters of neural functions is trained and updated by back-propagation learning algorithm. Finally, a simulation experiment is performed, the feasibility and accuracy of the method are showed from the experimental results, and some special simulation phenomena of the algorithm are observed.
KeywordsDirect sequence estimation MIMO Functional networks Neural function QAM
The authors would like to acknowledge the financial support of this work from the National Natural Science Foundation of China (NSFC) (Grant Nos. 61671329, 61201426, 61501331), the Zhejiang Provincial Natural Science Foundation of China (Grant Nos. LQ16F010010 and LY16F010016), the Scientific Research Project of Education Department of Zhejiang Province of China (Grant Nos. Y201327231 and Y201430529). The authors also appreciate anonymous reviewers for their valuable and insightful comments, which were helpful for improving the paper.
- 1.Robet S, Lutz L, Wolfgang HG (2001) Noncoherent sequence estimation combined with noncoherent adaptive channel estimation. http://www.lit.lnt.de/papers/ew00cr.pdf
- 6.Ruan X, Zhang Y (2012) Blind optical baseband signals detection using recurrent neural network based on continuous multi-valued neurons. Acta Opt Sin 32(11):1-10Google Scholar
- 9.Iglesias A, Arcay B, Cotos JM, Taboada JA, Dafonte C (2004) A comparison between functional networks and artificial neural networks for the prediction of fishing catches. Neural Comput Appl 13:21–31Google Scholar
- 10.Zhou Y, He D, Nong Z (2007) Application of functional network to solving classification problems. World Acad Sci Eng Technol 12:12–24Google Scholar
- 12.Alonso-Betanzos A, Castillo E, Fontenla-Romero O, Sáchez-Maroño N (2004) Sheer strength prediction using dimensional analysis and functional networks. In: ESANN’2004 proceedings: European symposium on artificial neural networks Bruges (Belgium), pp 251–256Google Scholar