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.
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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.
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