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Since the source is only 4 bits then there are only 16 possible transmitted words. Included is the eight-bit value if an extra parity bit is used (see Hamming(7,4) code with an additional parity bit).
The main idea is to choose the error-correcting bits such that the index-XOR (the XOR of all the bit positions containing a 1) is 0. We use positions 1, 10, 100, etc. (in binary) as the error-correcting bits, which guarantees it is possible to set the error-correcting bits so that the index-XOR of the whole message is 0.
Some important terminologies & constraints :- Implicit Numbering : It simply means that nodes are numbered in increasing order by level and from left to right. i.e. nodes at bottom level will have low implicit number as compared to upper level nodes and nodes on same level are numbered in increasing order from left to right.
• Length. The length is the number of evaluation points. Because the sets are disjoint for {, …,}, the length of the code is | | = (+). • Dimension. The dimension of the code is (+), for ≤ , as each has degree at most (()), covering a vector space of dimension (()) =, and by the construction of , there are + distinct .
Cyclic codes are not only simple to implement but have the benefit of being particularly well suited for the detection of burst errors: contiguous sequences of erroneous data symbols in messages. This is important because burst errors are common transmission errors in many communication channels, including magnetic and optical storage devices.
See Hamming code for an example of an error-correcting code. Parity bit checking is used occasionally for transmitting ASCII characters, which have 7 bits, leaving the 8th bit as a parity bit. For example, the parity bit can be computed as follows. Assume Alice and Bob are communicating and Alice wants to send Bob the simple 4-bit message 1001.
In this example, the first bit cannot yet be recovered, because all of the constraints connected to it have more than one unknown bit. In order to proceed with decoding the message, constraints connecting to only one of the erased bits must be identified. In this example, only the second constraint suffices.
If the minority is larger than the maximum number of errors possible, the decoding step fails knowing there are too many errors in the input code. Once a coefficient is computed, if it's 1, update the code to remove the monomial μ {\textstyle \mu } from the input code and continue to next monomial, in reverse order of their degree.