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Future Mobile Communications Reaching For Ever Increasing Data Rates OFDM & MC-CDMA technique System
Authors: Rubi Gupta and Prof Ranjeet Prajapati
Number of views: 482
ABSTRACT: A MC-CDMA system can be configuration play important role in performance improvement in
transmitter and receiver. One configuration is transmit/receive diversity (TRD) which has been widely used
due to its simplicity and good performance in huge load traffic. The performance of MC-CDMAsystems with
optimal TRD depends on their operational environments. By providing the receiver with multiple copies of
the transmitted signal in space, MC-CDMAsystems achieve spatial diversity and effectively mitigate
multipath fading, thereby improves the quality and performance of the receiver. The desired spatial diversity
order depends on the channel diversity, specially, the channel interference. In a big scattering environment, a
transmitter with an antenna array may transmit multiple independent data streams within the bandwidth of
operation, and the receiver with an antenna array can successfully separate the data streams. MCCDMAsystems,
therefore, Oder an increase in data rate through spatial multiplexing. Multipath scattering is
commonly seen as detrimental to wireless communications. However, with the emergence of MCCDMAsystems,
multipath have been effectively converted into a benefit for wireless communications. Due to
this big advantage, MC-CDMAtechnology is considered key to future of wireless communications. After
analyzing the shortcomings of current feature extraction and fault diagnosis technologies, a new approach
based on wavelet packet decomposition (WPD) and empirical mode decomposition (EMD) are combined to
extract fault feature frequency and neural network for rotating machinery early fault diagnosis is proposed.
Acquisition signals with fault frequency feature are decomposed into a series of narrow bandwidth using
WPD method for de-noising, then, the intrinsic mode functions (IMFs), which usually denoted the features of
corresponding frequency bandwidth can be obtained by applying EMD method. Thus, the component of IMF
with signal feature can be separated from all IMFs and the energy moment of IMFs is proposed as
eigenvector to effectively express the failure feature. The classical three layers BP neural network model
taking the fault feature frequency as target input of neural network, the 5 spectral bandwidth energy of
vibration signal spectrum as characteristic parameter, and the 10 types of representative rotor fault as output
can be established to identify the fault pattern of a machine. Lastly, the fault identification model of rotating
machinery with rotor lateral early crack based on BP neural network is taken as an example. The results
show that the proposed method can effectively get the signal feature to diagnose the occurrence of early fault
of rotating machinery.