The exercise should be finished in English.
2. According to Prof. Zhang s requirement, this exercise mainly focuses on the BER performance of some wireless communication system using specific coding and modulation type through the AWGN channel. Signal-to-Noise ration (SNR) varies from 5dB to 20dB.
The software is capable to simulate space time code [1] for QPSK modulation using different number of state. Examples of generator matrix up to 256 stetes are provided. Variable signal to noise ratio (SNR) might be applied to produce bit error rate (BER) or frame error rate (FER) curves.
Designers of signal receiver systems often need to performcascaded chain analysis of system performancefrom the antenna all the way to the ADC. Noise is a criticalparameter in the chain analysis because it limits theoverall sensitivity of the receiver. An application’s noiserequirement has a signifi cant infl uence on the systemtopology, since the choice of topology strives to optimizethe overall signal-to-noise ratio, dynamic range andseveral other parameters. One problem in noise calculationsis translating between the various units used by thecomponents in the chain: namely the RF, IF/baseband,and digital (ADC) sections of the circuit.
This program includes:
[5 7] convolutional code (encoder) + BPSK + AWGN + MAP (decoder). It evaluates Bit Error Rate and plots it versus SNR(signal to Noise Ratio).
We address the problem of blind carrier frequency-offset (CFO) estimation in quadrature amplitude modulation,
phase-shift keying, and pulse amplitude modulation
communications systems.We study the performance of a standard
CFO estimate, which consists of first raising the received signal to
the Mth power, where M is an integer depending on the type and
size of the symbol constellation, and then applying the nonlinear
least squares (NLLS) estimation approach. At low signal-to noise
ratio (SNR), the NLLS method fails to provide an accurate CFO
estimate because of the presence of outliers. In this letter, we derive
an approximate closed-form expression for the outlier probability.
This enables us to predict the mean-square error (MSE) on CFO
estimation for all SNR values. For a given SNR, the new results
also give insight into the minimum number of samples required in
the CFO estimation procedure, in order to ensure that the MSE
on estimation is not significantly affected by the outliers.
