ESTIMATION OF CUBIC NONLINEAR BANDPASS CHANNELS IN ORTHOGONAL FREQUENCY-DIVISION MULTIPLEXING SYSTEMS

    Abstract—Modeling and compensation of nonlinear communication channels has long been an important research topic in digital communications. A nonlinear bandpass channel is commonly modeled by a baseband equivalent Volterra series which relates the complex envelopes of the channel input and output. In this paper, we propose a novel method to estimate the frequency-domain baseband equivalent Volterra kernels of cubically nonlinear bandpass channels in orthogonal frequencydivision multiplexing (OFDM) systems. We recognize that the input signal for an OFDM system employing QAM or PSK modulations satisfies the properties of a kind of random multisine signal. By exploring the higher-order auto-moment spectra of the random multisine signal, a computationally efficient algorithm for determining the frequency-domain baseband equivalent Volterra kernels is derived. The obtained kernel estimates are optimal in the minimum mean square error (MMSE) sense. The proposed method can be used to estimate nonlinear bandpass channels for OFDM systems employing pure QAMs, pure PSKs, or a mixture of QAMs and PSKs. The effectiveness of the proposed method is demonstrated by applying it to estimate the nonlinear bandpass channel of an example OFDM system. Nonlinear channel compensators based on the Volterra model can benefit from the proposed method.

DOWNLOAD PAPER

RELATED VIDEO                                                                                                                         

RESEARCH ON THE SAFETY ASSESSMENT OF BRIDGES BASED ON FUZZY-NEURAL NETWORK

    Abstract--Fuzzy theory is integrated with Artificial Neural Network to create a bridge safety assessment model, through which the Fuzzy-Neural Network is improved in the light of sample data simulation. First, determine network layers interms of the seven critiria for bridge safety assessment. Then enter sample data at the input layer; study sample at the fuzzy reasoning layer by BP calculation method; obtain professional experience and ways of thinking about bridage safety assessment via the network. Finally, compare the assessment results from the network with those from professionals. The comparison proves the artificial fuzzy-neural network's feasibility and efficiency in assessing bridge safety.

DOWNLOAD PAPER

RELATED VIDEO                                                                                                                       

PREVIOUS PAGE|NEXT PAGE