./viterbimain 3 7 5 1011 More Features. It supports reading input from stdin or from commandline arguments.
When reading from stdin, it ignores blank lines or comment lines (which start with #). It supports any number of generator polynomials. It can perform convolutional encoding by providing -encode commandline flag.
It supports a different notation of generator polynomials by providing -reversepolynomials commandline flag. Here are more options to run the program.
Show help message./viterbimain 3 7 5 1011./viterbimain 3 6 5 00101011./viterbimain 7 91 11./viterbimain 7 91 117 100111./viterbimain -reversepolynomials 3 3 5 00101011./viterbimain -encode 3 7 5 10001./viterbimain -encode 3 6 5 1001101./viterbimain -encode -reversepolynomials 3 3 5 1001101 Error Handling All inputs are validated, and proper error messages will be output. The constraint should be greater than 0. A generator polynomial should be greater than 0 and less than 1 where N is an integer.
Otherwise some bits must be missing during transmission. We will fill in appropriate number of trailing zeros.
Running Unit Test Run unit test. Make viterbitest &&./viterbitest If the test passes, it will output PASSED and the end of the output; otherwise, the test program aborts with assertion error. There are a few sample cases in the unit test, some are with manually injected bit errors. It also generates random messages of length 8, 16 and 32 for several different convolutional codes and test if they can be properly encoded and decoded. Dependencies The code is self-contained, meaning it depends on nothing but the C standard library.
The purpose is to make it easy to be integrated in any project.
Rather than reinvent the wheel, I wonder if anyone could refer me to a 1D linear code snippet in ANSI C? I did a search on google and in stack overflow, but couldn't find anything in C I could use. For example, for Arrays A, B, and C, all double-precision, where A and B are inputs and C is output, having lengths lenA, lenB, and lenC = lenA + lenB - 1, respectively. My array sizes are small and so any speed increase in implementing fast convolution by FFT is not needed. Looking for straightforward computation.
./viterbimain 3 7 5 1011 More Features. It supports reading input from stdin or from commandline arguments. When reading from stdin, it ignores blank lines or comment lines (which start with #). It supports any number of generator polynomials. It can perform convolutional encoding by providing -encode commandline flag.
It supports a different notation of generator polynomials by providing -reversepolynomials commandline flag. Here are more options to run the program. Show help message./viterbimain 3 7 5 1011./viterbimain 3 6 5 00101011./viterbimain 7 91 11./viterbimain 7 91 117 100111./viterbimain -reversepolynomials 3 3 5 00101011./viterbimain -encode 3 7 5 10001./viterbimain -encode 3 6 5 1001101./viterbimain -encode -reversepolynomials 3 3 5 1001101 Error Handling All inputs are validated, and proper error messages will be output. The constraint should be greater than 0. A generator polynomial should be greater than 0 and less than 1 where N is an integer.
Otherwise some bits must be missing during transmission. We will fill in appropriate number of trailing zeros. Running Unit Test Run unit test. Make viterbitest &&./viterbitest If the test passes, it will output PASSED and the end of the output; otherwise, the test program aborts with assertion error. There are a few sample cases in the unit test, some are with manually injected bit errors. It also generates random messages of length 8, 16 and 32 for several different convolutional codes and test if they can be properly encoded and decoded. Dependencies The code is self-contained, meaning it depends on nothing but the C standard library.
The purpose is to make it easy to be integrated in any project.
Sorry for the delay. Can anybody give me an small example?
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Using the convolution of matrices (i'm not asking for the answer) so maybe that can be more helpfulYou have two arrays of values which you are convolving together. For the sake of example, assume they are one-dimensional, though higher dimensions work the same way.
One of the arrays is typically much larger than the other. The one which is larger is called the 'signal', by convention. The smaller one is called the 'kernel'. To convolve them, you take the kernel and slap it down on top of the signal somewhere.
You take the dot product of the two, this produces a result. This is one data point of the convolution. Now you slide the kernel to the right (or left, whatever) by one sample, and do it again. That produces the next data point. And so on, until the kernel 'slides off' the end of the signal. There are details that I won't go into, such as the kernel is the time-reversed impulse response of the filter, the length of the result is N + M - 1 where N is the signal length and M is the kernel length, etc.
Since this is a programming assignment, I assume that those particular details are not really the point of the assignment. The biggest hint that I can give you is that you are going to need one loop nested inside another loop. gi = 4. fi; So what did you store in the f array between declaring it in main and using it here in this function?
Simulation Source Code Examples-Tutorial on Convolutional Coding with Viterbi Decoding-Simulation Source Code Examples Read about how the family of Chinese prime minister Wen JiaBao enriched themselves by as much as US$2.7 Billion during his tenure as leader on the New York Times website: If you're behind the so-called 'Great Firewall,' try the BBC News website: or the Washington Post website: You can also try to read the pdf version in Chinese: Simulation Source Code Examples The simulation source code comprises a test driver routine and several functions, which will be described below. This code simulates a link through an AWGN channel from data source to Viterbi decoder output. The first dynamically allocates several arrays to store the source data, the convolutionally encoded source data, the output of the AWGN channel, and the data output by the Viterbi decoder.
It calls the data generator, convolutional encoder, channel simulation, and Viterbi decoder functions in turn. It then compares the source data output by the data generator to the data output by the Viterbi decoder and counts the number of errors. Once 100 errors (sufficient for +/- 20% measurement error with 95% confidence) are accumulated, the test driver displays the BER for the given Es/No. The test parameters are controlled by definitions in.
The test driver includes a compile-time option to also measure the BER for an uncoded channel, i.e. A channel without forward error correction. I used this option to validate my Gaussian noise generator, by comparing the simulated uncoded BER to the theoretical uncoded BER given by, where E b/N 0 is expressed as a ratio, not in dB.
I am happy to say that the results agree quite closely. When running the simulations, it is important to remember the relationship between E s/N 0 and E b/N 0. As stated earlier, for the uncoded channel, E s/N 0 = E b/N 0, since there is one channel symbol per bit. However, for the coded channel, E s/N 0 = E b /N 0 + 10log 10(k/n).
For example, for rate 1/2 coding, E s/N 0 = E b/N 0 + 10log 10(1/2) = E b/N 0 - 3.01 dB. For rate 1/8 coding, E s/N 0 = E b/N 0 + 10log 10(1/8) = E b/N 0 - 9.03 dB. The function simulates the data source. It accepts as arguments a pointer to an input array and the number of bits to generate, and fills the array with randomly-chosen zeroes and ones.
The function accepts as arguments the pointers to the input and output arrays and the number of bits in the input array. It then performs the specified convolutional encoding and fills the output array with one/zero channel symbols.
C Program For Armstrong Number
The convolutional code parameters are in the header file. The function accepts as arguments the desired E s/N 0, the number of channel symbols in the input array, and pointers to the input and output arrays. It performs the binary (one and zero) to baseband signal level (+/- 1) mapping on the convolutional encoder channel symbol outputs.
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It then adds Gaussian random variables to the mapped symbols, and fills the output array. The output data are floating point numbers. The arguments to the function are the expected E s/N 0, the number of channel symbols in the input array, and pointers to its input and output arrays. First, the decoder function sets up its data structures, the arrays described in the algorithm description section. Then, it performs three-bit soft quantization on the floating point received channel symbols, using the expected E s/N 0, producing integers. (Optionally, a fixed quantizer designed for a 4 dB E s/N 0 can be chosen.) This completes the preliminary processing. The next step is to start decoding the soft-decision channel symbols.
C Program For Factorial
The decoder builds up a trellis of depth K x 5, and then traces back to the beginning of the trellis and outputs one bit. The decoder then shifts the trellis left one time instant, discarding the oldest data, following which it computes the accumulated error metrics for the next time instant, traces back, and outputs a bit. The decoder continues in this way until it reaches the flushing bits. The flushing bits cause the encoder to converge back to state 0, and the decoder exploits this fact. Once the decoder builds the trellis for the last bit, it flushes the trellis, decoding and outputting all the bits in the trellis up to but not including the first flushing bit. I have compiled and tested the simulation source code described above under Borland C Builder Version 3-please do not request help in modifying the code to compile under a different environment. Simulation results are presented Click on one of the links below to go to the beginning of that section: Copyright 1999-2003, Spectrum Applications.