Hamming code
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In telecommunication, a Hamming code is an errorcorrecting code named after its inventor, Richard Hamming. Hamming codes can detect single and doublebit errors, and correct singlebit errors as well. In contrast, the simple parity code cannot detect errors where two bits are transposed, nor can it help correct the errors it can find.
Contents 
History
Hamming worked at Bell Labs in the 1940s on the Bell Model V computer, an electromechanical relaybased monster with cycle times in seconds. Input was fed in on punch cards, which would invariably have read errors. During weekdays, special code would find errors and flash lights so the operators could correct the problem. During afterhours periods and on weekends, when there were no operators, the machine simply moved on to the next job.
Hamming worked on weekends, and grew increasingly frustrated with having to restart his programs from scratch due to the unreliability of the card reader. Over the next few years he worked on the problem of errorcorrection, developing an increasingly powerful array of algorithms. In 1950 he published what is now known as Hamming Code, which remains in use in some applications today.
Codes predating Hamming
A number of simple errordetecting codes were used before Hamming codes, but none were nearly as effective as Hamming codes in the same overhead of space. We describe some here.
Parity
Parity adds a single bit that indicates whether the number of 1 bits in the preceding data was even or odd. If a single bit is changed in transmission, the message will change parity and the error can be detected at this point. (Note that the bit that changed may have been the parity bit itself!) The most common convention is that a parity value of 1 indicates that there is an odd number of ones in the data, and a parity value of 0 indicates that there is an even number of ones in the data.
Parity checking is not very robust, since if the number of bits changed is even, the check bit will be valid and the error will not be detected. Moreover, parity does not indicate which bit contained the error, even when it can detect it. The data must be discarded entirely, and retransmitted from scratch. On a noisy transmission medium a successful transmission could take a long time, or even never occur. Parity does have the advantage, however, that it's about the best possible code that uses only a single bit of space.
Twooffive
In the 1940s Bell used a slightly more sophisticated code known as twooffive. This code ensured that every block of five bits (known as a 5block) had exactly two 1s. The computer could tell if there was an error if in its input there were not exactly two 1s in each block. Twooffive was still only able to detect single bits; if one bit flipped to a 1 and another to a 0 in the same block, the twooffive rule remained true and the error would go undiscovered.
Repetition
Another code in use at the time repeated every data bit several times in order to ensure that it got through. For instance, if the data bit to be sent was a 1, an n=3 repetition code would send "111". If the three bits received were not identical, an error occurred. If the channel is clean enough, most of the time only one bit will change in each triple. Therefore, 001, 010, and 100 each correspond to a 0 bit, while 110, 101, and 011 correspond to a 1 bit, as though the bits counted as "votes" towards what the original bit was. A code with this ability to reconstruct the original message in the presence of errors is known as an errorcorrecting code.
Such codes cannot correctly repair all errors, however. In our example, if the channel flipped two bits and the receiver got "001", the system would detect the error, but conclude that the original bit was 0, which is incorrect. If we increase the number of times we duplicate each bit to four, we can detect all twobit errors but can't correct them (the votes "tie"); at five, we can correct all twobit errors, but not detect all threebit errors.
Moreover, the repetition code is extremely inefficient, reducing throughput by three times in our original case, and the efficiency drops drastically as we increase the number of times each bit is duplicated in order to detect and correct more errors.**
Hamming codes
If more errorcorrecting bits are included with a message, and if those bits can be arranged such that different incorrect bits produce different error results, then bad bits could be identified. In a 7bit message, there are seven possible single bit errors, so three error control bits could potentially specify not only that an error occurred but also which bit caused the error.
Hamming studied the existing coding schemes, including twooffive, and generalized their concepts. To start with he developed a nomenclature to describe the system, including the number of data bits and errorcorrection bits in a block. For instance, parity includes a single bit for any data word, so assuming ASCII words with 7bits, Hamming described this as an (8,7) code, with eight bits in total, of which 7 are data. The repetition example would be (3,1), following the same logic. The information rate is the second number divided by the first, for our repetition example, 1/3.
Hamming also noticed the problems with flipping two or more bits, and described this as the "distance" (it is now called the Hamming distance, after him.) Parity has a distance of 2, as any two bit flips will be invisible. The (3,1) repetition has a distance of 3, as three bits need to be flipped in the same triple to obtain another code word. A (4,1) repetition (each bit is repeated four times) has a distance of 4, so flipping two bits in the same group will no longer go undiscovered.
Hamming was interested in two problems at once; increasing the distance as much as possible, while at the same time increasing the information rate as much as possible. During the 1940s he developed several encoding schemes that were dramatic improvements on existing codes. Key to all of his systems was to have the parity bits overlap, such that they managed to check each other as well as the data.
Hamming (7,4)
Today, Hamming Code really refers to a specific (7,4) code Hamming introduced in 1950. Hamming Code adds three additional check bits to every four data bits of the message. Hamming's (7,4) algorithm can correct any singlebit error, and detect all twobit errors. Since the medium would have to be uselessly noisy for 2 out of 7 bits to be lost, Hamming's (7,4) is generally lossless.
The algorithm is simple:
 All bit positions that are powers of two are used as parity bits. (positions 1, 2, 4, 8, 16, 32, 64, etc.)
 All other bit positions are for the data to be encoded. (positions 3, 5, 6, 7, 9, 10, 11, 12, 13, 14, 15, 17, etc.)
 Each parity bit calculates the parity for some of the bits in the code word. The position of the parity bit determines the sequence of bits that it alternately checks and skips.
 Position 1: skip 1 bit, check 1 bit, skip 1 bit, check 1 bit, etc.
 Position 2: check 1 bit, skip 2 bits, check 2 bits, skip 2 bits, check 2 bits, etc.
 Position 4: check 3 bits, skip 4 bits, check 4 bits, skip 4 bits, check 4 bits, etc.
 Position 8: check 7 bits, skip 8 bits, check 8 bits, skip 8 bits, check 8 bits, etc.
 Position 16: check 15 bits, skip 16 bits, check 16 bits, skip 16 bits, check 16 bits, etc.
 Position 32: check 31 bits, skip 32 bits, check 32 bits, skip 32 bits, check 32 bits, etc.
 And so on.
1==Example==
Consider the 7bit data word "0110101". To demonstrate how Hamming codes are calculated and used to detect an error, see the tables below. They use d to signify data bits and p to signify parity bits.
Firstly the data bits are inserted into their appropriate positions and the parity bits calculated in each case using even parity.
Calculation of Hamming code parity bits p_{1} p_{2} d_{1} p_{3} d_{2} d_{3} d_{4} p_{4} d_{5} d_{6} d_{7} Data word (without parity): 0 1 1 0 1 0 1 p_{1} 1 0 1 0 1 1 p_{2} 0 0 1 0 0 1 p_{3} 0 1 1 0 p_{4} 0 1 0 1 Data word (with parity): 1 0 0 0 1 1 0 0 1 0 1
The new data word (with parity bits) is now "10001100101". We now assume the final bit gets corrupted and turned from 1 to 0. Our new data word is "10001100100"; and this time when we analyse how the Hamming codes were created we flag each parity bit as 1 when the even parity check fails.
Checking of parity bits (switched bit highlighted) p_{1} p_{2} d_{1} p_{3} d_{2} d_{3} d_{4} p_{4} d_{5} d_{6} d_{7} Parity check Parity bit Received data word: 1 0 0 0 1 1 0 0 1 0 0 1 p_{1} 1 0 1 0 1 0 Fail 1 p_{2} 0 0 1 0 0 0 Fail 1 p_{3} 0 1 1 0 Pass 0 p_{4} 0 1 0 0 Fail 1
The final step is to evaluate the value of the parity bits (remembering the lowest value bit goes furthest to the right). The integer value of the parity bits is 11, signifying that the 11th bit in the data word (including parity bits) is wrong and needs to be flipped.
p_{4} p_{3} p_{2} p_{1} Binary 1 0 1 1 Decimal 8 2 1 Σ = 11
Flipping the 11th bit gives changes 10001100100 back into 10001100101. Removing the Hamming codes gives the original data word of 0110101.
Note that as parity bits do not check each other. If a single parity bit check fails and all others succeed, then it is the parity bit in question that is wrong and not any bit it checks.
Finally, suppose two bits change, at positions x and y. If x and y have the same bit at the 2^{k} position in their binary representations, then the parity bit corresponding to that position checks them both, and so will remain the same. However, some parity bit must be altered, because x ≠ y, and so some two corresponding bits differ in x and y. Thus, the Hamming code detects all two bit errors — however, it cannot distinguish them from 1bit errors.
See also
External links
 CGI script for calculating Hamming distances (from R. Tervo, UNB, Canada) (http://www.ee.unb.ca/cgibin/tervo/hamming.pl)de:HammingCode