Hamming(7,4)
In coding theory, Hamming(7,4) is a linear errorcorrecting code that encodes four bits of data into seven bits by adding three parity bits. It is a member of a larger family of Hamming codes, but the term Hamming code often refers to this specific code that introduced in 1950. At the time, Hamming worked at Bell Telephone Laboratories and was frustrated with the errorprone reader, which is why he started working on errorcorrecting codes.
The 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, or detect all singlebit and twobit errors. In other words, the minimal Hamming distance between any two correct codewords is 3, and received words can be correctly decoded if they are at a distance of at most one from the codeword that was transmitted by the sender. This means that for transmission medium situations where burst errors do not occur, Hamming’s (7,4) code is effective (as the medium would have to be extremely noisy for two out of seven bits to be flipped).
Contents
Goal
The goal of the Hamming codes is to create a set of parity bits that overlap such that a singlebit error (the bit is logically flipped in value) in a data bit or a parity bit can be detected and corrected. While multiple overlaps can be created, the general method is presented in Hamming codes.

Bit # 1 2 3 4 5 6 7 Transmitted bit
This table describes which parity bits cover which transmitted bits in the encoded word. For example, p_{2} provides an even parity for bits 2, 3, 6, and 7. It also details which transmitted bit is covered by which parity bit by reading the column. For example, d_{1} is covered by p_{1} and p_{2} but not p_{3} This table will have a striking resemblance to the paritycheck matrix (H) in the next section.
Furthermore, if the parity columns in the above table were removed
then resemblance to rows 1, 2, and 4 of the code generator matrix (G) below will also be evident.
So, by picking the parity bit coverage correctly, all errors with a Hamming distance of 1 can be detected and corrected, which is the point of using a Hamming code.
Hamming matrices
Hamming codes can be computed in terms through because Hamming codes are . For the purposes of Hamming codes, two Hamming matrices can be defined: the code G and the H:
As mentioned above, rows 1, 2, and 4 of G should look familiar as they map the data bits to their parity bits:
 p_{1} covers d_{1}, d_{2}, d_{4}
 p_{2} covers d_{1}, d_{3}, d_{4}
 p_{3} covers d_{2}, d_{3}, d_{4}
The remaining rows (3, 5, 6, 7) map the data to their position in encoded form and there is only 1 in that row so it is an identical copy. In fact, these four rows are and form the (by design, not coincidence).
Also as mentioned above, the three rows of H should be familiar. These rows are used to compute the syndrome vector at the receiving end and if the syndrome vector is the (all zeros) then the received word is errorfree; if nonzero then the value indicates which bit has been flipped.
The four data bits — assembled as a vector p — is premultiplied by G (i.e., Gp) and taken 2 to yield the encoded value that is transmitted. The original 4 data bits are converted to seven bits (hence the name “Hamming(7,4)”) with three parity bits added to ensure even parity using the above data bit coverages. The first table above shows the mapping between each data and parity bit into its final bit position (1 through 7) but this can also be presented in a . The first diagram in this article shows three circles (one for each parity bit) and encloses data bits that each parity bit covers. The second diagram (shown to the right) is identical but, instead, the bit positions are marked.
For the remainder of this section, the following 4 bits (shown as a column vector) will be used as a running example:
Channel coding
Suppose we want to transmit this data (1011
) over a . Specifically, a meaning that error corruption does not favor either zero or one (it is symmetric in causing errors). Furthermore, all source vectors are assumed to be equiprobable. We take the product of G and p, with entries modulo 2, to determine the transmitted codeword x:
This means that 0110011
would be transmitted instead of transmitting 1011
.
Programmers concerned about multiplication should observe that each row of the result is the least significant bit of the of set bits resulting from the row and column being together rather than multiplied.
In the adjacent diagram, the seven bits of the encoded word are inserted into their respective locations; from inspection it is clear that the parity of the red, green, and blue circles are even:
 red circle has two 1’s
 green circle has two 1’s
 blue circle has four 1’s
What will be shown shortly is that if, during transmission, a bit is flipped then the parity of two or all three circles will be incorrect and the errored bit can be determined (even if one of the parity bits) by knowing that the parity of all three of these circles should be even.
Parity check
If no error occurs during transmission, then the received codeword r is identical to the transmitted codeword x:
The receiver multiplies H and r to obtain the syndrome vector z, which indicates whether an error has occurred, and if so, for which codeword bit. Performing this multiplication (again, entries modulo 2):
Since the syndrome z is the , the receiver can conclude that no error has occurred. This conclusion is based on the observation that when the data vector is multiplied by G, a change of basis occurs into a vector subspace that is the of H. As long as nothing happens during transmission, r will remain in the kernel of H and the multiplication will yield the null vector.
Error correction
Otherwise, suppose a single bit error has occurred. Mathematically, we can write
modulo 2, where e_{i} is the , that is, a zero vector with a 1 in the , counting from 1.
Thus the above expression signifies a single bit error in the place.
Now, if we multiply this vector by H:
Since x is the transmitted data, it is without error, and as a result, the product of H and x is zero. Thus
Now, the product of H with the standard basis vector picks out that column of H, we know the error occurs in the place where this column of H occurs.
For example, suppose we have introduced a bit error on bit #5
The diagram to the right shows the bit error (shown in blue text) and the bad parity created (shown in red text) in the red and green circles. The bit error can be detected by computing the parity of the red, green, and blue circles. If a bad parity is detected then the data bit that overlaps only the bad parity circles is the bit with the error. In the above example, the red and green circles have bad parity so the bit corresponding to the intersection of red and green but not blue indicates the errored bit.
Now,
which corresponds to the fifth column of H. Furthermore, the general algorithm used (see Hamming code#General algorithm) was intentional in its construction so that the syndrome of 101 corresponds to the binary value of 5, which indicates the fifth bit was corrupted. Thus, an error has been detected in bit 5, and can be corrected (simply flip or negate its value):
This corrected received value indeed, now, matches the transmitted value x from above.
Decoding
Once the received vector has been determined to be errorfree or corrected if an error occurred (assuming only zero or one bit errors are possible) then the received data needs to be decoded back into the original four bits.
First, define a matrix R:
Then the received value, p_{r}, is equal to Rr. Using the running example from above
Multiple bit errors
It is not difficult to show that only single bit errors can be corrected using this scheme. Alternatively, Hamming codes can be used to detect single and double bit errors, by merely noting that the product of H is nonzero whenever errors have occurred. In the adjacent diagram, bits 4 and 5 were flipped. This yields only one circle (green) with an invalid parity but the errors are not recoverable.
However, the Hamming (7,4) and similar Hamming codes cannot distinguish between singlebit errors and twobit errors. That is, twobit errors appear the same as onebit errors. If error correction is performed on a twobit error the result will be incorrect.
Similarly, Hamming codes cannot detect or recover from an arbitrary threebit error; Consider the diagram: if the bit in the green circle (colored red) were 1, the parity checking would return the null vector, indicating that there is no error in the codeword.
All codewords
Since the source is only 4 bits then there are only 16 possible transmitted words. Included is the eightbit value if an extra parity bit is used (see Hamming(7,4) code with an additional parity bit). (The data bits are shown in blue; the parity bits are shown in red; and the extra parity bit shown in green.)
Data  Hamming(7,4)  Hamming(7,4) with extra parity bit (Hamming(8,4))  

Transmitted  Diagram  Transmitted  Diagram  
0000  0000000  00000000  
1000  1110000  11100001  
0100  1001100  10011001  
1100  0111100  01111000  
0010  0101010  01010101  
1010  1011010  10110100  
0110  1100110  11001100  
1110  0010110  00101101  
0001  1101001  11010010  
1001  0011001  00110011  
0101  0100101  01001011  
1101  1010101  10101010  
0011  1000011  10000111  
1011  0110011  01100110  
0111  0001111  00011110  
1111  1111111  11111111 
Source
In coding theory, Hamming(7,4) is a that encodes four bits of data into seven bits by adding three parity bits. It is a member of a larger family of Hamming codes, but the term Hamming code often refers to this specific code that introduced in 1950. At the time, Hamming worked at and was frustrated with the errorprone reader, which is why he started working on errorcorrecting codes.
The 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, or detect all singlebit and twobit errors. In other words, the minimal between any two correct codewords is 3, and received words can be correctly decoded if they are at a distance of at most one from the codeword that was transmitted by the sender. This means that for transmission medium situations where do not occur, Hamming’s (7,4) code is effective (as the medium would have to be extremely noisy for two out of seven bits to be flipped).
Goal
The goal of the Hamming codes is to create a set of parity bits that overlap such that a singlebit error (the bit is logically flipped in value) in a data bit or a parity bit can be detected and corrected. While multiple overlaps can be created, the general method is presented in Hamming codes.

Bit # 1 2 3 4 5 6 7 Transmitted bit
This table describes which parity bits cover which transmitted bits in the encoded word. For example, p_{2} provides an even parity for bits 2, 3, 6, and 7. It also details which transmitted bit is covered by which parity bit by reading the column. For example, d_{1} is covered by p_{1} and p_{2} but not p_{3} This table will have a striking resemblance to the paritycheck matrix (H) in the next section.
Furthermore, if the parity columns in the above table were removed
then resemblance to rows 1, 2, and 4 of the code generator matrix (G) below will also be evident.
So, by picking the parity bit coverage correctly, all errors with a Hamming distance of 1 can be detected and corrected, which is the point of using a Hamming code.
Hamming matrices
Hamming codes can be computed in terms through because Hamming codes are . For the purposes of Hamming codes, two Hamming matrices can be defined: the code G and the H:
As mentioned above, rows 1, 2, and 4 of G should look familiar as they map the data bits to their parity bits:
 p_{1} covers d_{1}, d_{2}, d_{4}
 p_{2} covers d_{1}, d_{3}, d_{4}
 p_{3} covers d_{2}, d_{3}, d_{4}
The remaining rows (3, 5, 6, 7) map the data to their position in encoded form and there is only 1 in that row so it is an identical copy. In fact, these four rows are and form the (by design, not coincidence).
Also as mentioned above, the three rows of H should be familiar. These rows are used to compute the syndrome vector at the receiving end and if the syndrome vector is the (all zeros) then the received word is errorfree; if nonzero then the value indicates which bit has been flipped.
The four data bits — assembled as a vector p — is premultiplied by G (i.e., Gp) and taken 2 to yield the encoded value that is transmitted. The original 4 data bits are converted to seven bits (hence the name “Hamming(7,4)”) with three parity bits added to ensure even parity using the above data bit coverages. The first table above shows the mapping between each data and parity bit into its final bit position (1 through 7) but this can also be presented in a . The first diagram in this article shows three circles (one for each parity bit) and encloses data bits that each parity bit covers. The second diagram (shown to the right) is identical but, instead, the bit positions are marked.
For the remainder of this section, the following 4 bits (shown as a column vector) will be used as a running example:
Channel coding
Suppose we want to transmit this data (1011
) over a . Specifically, a meaning that error corruption does not favor either zero or one (it is symmetric in causing errors). Furthermore, all source vectors are assumed to be equiprobable. We take the product of G and p, with entries modulo 2, to determine the transmitted codeword x:
This means that 0110011
would be transmitted instead of transmitting 1011
.
Programmers concerned about multiplication should observe that each row of the result is the least significant bit of the of set bits resulting from the row and column being together rather than multiplied.
In the adjacent diagram, the seven bits of the encoded word are inserted into their respective locations; from inspection it is clear that the parity of the red, green, and blue circles are even:
 red circle has two 1’s
 green circle has two 1’s
 blue circle has four 1’s
What will be shown shortly is that if, during transmission, a bit is flipped then the parity of two or all three circles will be incorrect and the errored bit can be determined (even if one of the parity bits) by knowing that the parity of all three of these circles should be even.
Parity check
If no error occurs during transmission, then the received codeword r is identical to the transmitted codeword x:
The receiver multiplies H and r to obtain the syndrome vector z, which indicates whether an error has occurred, and if so, for which codeword bit. Performing this multiplication (again, entries modulo 2):
Since the syndrome z is the , the receiver can conclude that no error has occurred. This conclusion is based on the observation that when the data vector is multiplied by G, a change of basis occurs into a vector subspace that is the of H. As long as nothing happens during transmission, r will remain in the kernel of H and the multiplication will yield the null vector.
Error correction
Otherwise, suppose a single bit error has occurred. Mathematically, we can write
modulo 2, where e_{i} is the , that is, a zero vector with a 1 in the , counting from 1.
Thus the above expression signifies a single bit error in the place.
Now, if we multiply this vector by H:
Since x is the transmitted data, it is without error, and as a result, the product of H and x is zero. Thus
Now, the product of H with the standard basis vector picks out that column of H, we know the error occurs in the place where this column of H occurs.
For example, suppose we have introduced a bit error on bit #5
The diagram to the right shows the bit error (shown in blue text) and the bad parity created (shown in red text) in the red and green circles. The bit error can be detected by computing the parity of the red, green, and blue circles. If a bad parity is detected then the data bit that overlaps only the bad parity circles is the bit with the error. In the above example, the red and green circles have bad parity so the bit corresponding to the intersection of red and green but not blue indicates the errored bit.
Now,
which corresponds to the fifth column of H. Furthermore, the general algorithm used (see Hamming code#General algorithm) was intentional in its construction so that the syndrome of 101 corresponds to the binary value of 5, which indicates the fifth bit was corrupted. Thus, an error has been detected in bit 5, and can be corrected (simply flip or negate its value):
This corrected received value indeed, now, matches the transmitted value x from above.
Decoding
Once the received vector has been determined to be errorfree or corrected if an error occurred (assuming only zero or one bit errors are possible) then the received data needs to be decoded back into the original four bits.
First, define a matrix R:
Then the received value, p_{r}, is equal to Rr. Using the running example from above
Multiple bit errors
It is not difficult to show that only single bit errors can be corrected using this scheme. Alternatively, Hamming codes can be used to detect single and double bit errors, by merely noting that the product of H is nonzero whenever errors have occurred. In the adjacent diagram, bits 4 and 5 were flipped. This yields only one circle (green) with an invalid parity but the errors are not recoverable.
However, the Hamming (7,4) and similar Hamming codes cannot distinguish between singlebit errors and twobit errors. That is, twobit errors appear the same as onebit errors. If error correction is performed on a twobit error the result will be incorrect.
Similarly, Hamming codes cannot detect or recover from an arbitrary threebit error; Consider the diagram: if the bit in the green circle (colored red) were 1, the parity checking would return the null vector, indicating that there is no error in the codeword.
All codewords
Since the source is only 4 bits then there are only 16 possible transmitted words. Included is the eightbit value if an extra parity bit is used (see Hamming(7,4) code with an additional parity bit). (The data bits are shown in blue; the parity bits are shown in red; and the extra parity bit shown in green.)
Data  Hamming(7,4)  Hamming(7,4) with extra parity bit (Hamming(8,4))  

Transmitted  Diagram  Transmitted  Diagram  
0000  0000000  00000000  
1000  1110000  11100001  
0100  1001100  10011001  
1100  0111100  01111000  
0010  0101010  01010101  
1010  1011010  10110100  
0110  1100110  11001100  
1110  0010110  00101101  
0001  1101001  11010010  
1001  0011001  00110011  
0101  0100101  01001011  
1101  1010101  10101010  
0011  1000011  10000111  
1011  0110011  01100110  
0111  0001111  00011110  
1111  1111111  11111111 