Direct sequence estimation: a functional network approach

Abstract

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.