Minimum distance is a concept in encoding theory that refers to the minimum number of bits encoded differently between any two different encoding bits in a given encoding set. The minimum code distance is an important indicator to measure the encoding performance, which is closely related to the error correction ability of the encoding. The larger the minimum code distance, the stronger the anti-interference ability and error correction ability of the encoding, but the corresponding data redundancy will also increase, and the coding efficiency may be reduced.
The method for calculating the minimum yard distance is usually as follows:
1.First, all the codes in a given set of codes are converted into binary form.
2.Then, an XOR operation is performed on each pair of codes in the set to obtain the code distance between them, that is, the number of bits that are different after the two codes are compared bitwise.
3.Record the code spacing between each pair of codes.
4.Finally, find the smallest value from the code spacing of all records, which is the minimum code spacing of the encoding set.
For example, let's say we have a collection of encodings, and we first convert them to binary form:
0xa9 ->10101001
0xc7 ->11000111
0xdf ->11011011
0xbe ->10111110
We then calculate the code spacing between each pair of encodings:
0xa9 and 0xc7 have a code pitch of 5
0xa9 and 0xdf have a yard distance of 4
0xa9 and 0xbe have a code distance of 3
0xc7 and 0xdf have a code pitch of 2
0xc7 and 0xbe have a code distance of 3
0xdf and 0xbe have a code distance of 3
From the spacing above, we can see that the minimum spacing is 2, so the minimum spacing for this encoding set is 2.
In practice, the calculation of the minimum code distance can be achieved programmatically, for example, using Python to write a function to calculate the minimum code distance for a given set of codes.