This code was used for making the practical measurements in section 2.3 of my thesis. This Matlab code allows an OFDM signal to be generated based on an input data file. The data can be random data, a grey scale image, a wave file, or any type of file. The generated OFDM signal is stored as a windows wave file, allowing it to be viewed, listened to and manipulated in other programs. The modified wave file can then be decoded by the receiver software to extract the original data. This code was developed for the experiments that I performed in my honours thesis, and thus has not been fully debugged.
This is the original code developed for the thesis and so has several problems with it. The BER performance given by the simulations is infact Symbol Error Rate.
in this paper,wo propose an extension of the zerotree-based space-frequency quantization algorithm by adding a wedgelet Symbol to its tree-pruning optimization.
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.
Obtain the CDF plots of PAPR from an OFDM BPSK transmission specified per IEEE 802.11a specification
Per the IEEE 802.11a specifications, we 52 have used subcarriers. Given so, the theoretical maximum expected PAPR is 52 (around 17dB). However, thanks to the scrambler, all the subcarriers in an OFDM Symbol being equally modulated is unlikely.
Using a small script, the cumulative distribution of PAPR from each OFDM Symbol, modulated by a random BPSK signal is obtained
I ve written some many years ago dynamic Huffman algorithm to compress and decompress data. It is mainly targeted to data with some Symbols occuring more often than the rest (e.g. having some data file consisted of 3 different Symbols and their total number of occurence in that file s1(1000), s2(200), s3(30) so the total length of file is 1000+200+30=1230 bytes, it will be encoded assigning one bit to s1 and 2 bits to s2, s3 so the encoded length will be 1*1000+2*(200+30)=1460 bits=182 bytes). In the best case the file consisted of just one Symbol will be encoded with compression ratio as 1:8. Huffman coding is used in image compression, however in JPEG2000 arithmetic codec is imployed.
