Binary symmetric channel explained
A binary symmetric channel (or BSCp) is a common communications channel model used in coding theory and information theory. In this model, a transmitter wishes to send a bit (a zero or a one), and the receiver will receive a bit. The bit will be "flipped" with a "crossover probability" of p, and otherwise is received correctly. This model can be applied to varied communication channels such as telephone lines or disk drive storage.
The noisy-channel coding theorem applies to BSCp, saying that information can be transmitted at any rate up to the channel capacity with arbitrarily low error. The channel capacity is
bits, where
is the
binary entropy function. Codes including Forney's code have been designed to transmit information efficiently across the channel.
Definition
A binary symmetric channel with crossover probability
, denoted by BSC
p, is a channel with binary input and binary output and probability of error
. That is, if
is the transmitted
random variable and
the received variable, then the channel is characterized by the
conditional probabilities:
\begin{align}
\operatorname{Pr}[Y=0|X=0]&=1-p\\
\operatorname{Pr}[Y=0|X=1]&=p\\
\operatorname{Pr}[Y=1|X=0]&=p\\
\operatorname{Pr}[Y=1|X=1]&=1-p
\end{align}
It is assumed that
. If
, then the receiver can swap the output (interpret 1 when it sees 0, and vice versa) and obtain an equivalent channel with crossover probability
.
Capacity
The channel capacity of the binary symmetric channel, in bits, is:
CBSC=1-\operatornameHb(p),
where
is the
binary entropy function, defined by:
Noisy-channel coding theorem
Shannon's noisy-channel coding theorem gives a result about the rate of information that can be transmitted through a communication channel with arbitrarily low error. We study the particular case of
.
The noise
that characterizes
is a
random variable consisting of n independent random bits (n is defined below) where each random bit is a
with probability
and a
with probability
. We indicate this by writing "
".
What this theorem actually implies is, a message when picked from
, encoded with a random encoding function
, and sent across a noisy
, there is a very high probability of recovering the original message by decoding, if
or in effect the rate of the channel is bounded by the quantity stated in the theorem. The decoding error probability is exponentially small.
Proof
The theorem can be proved directly with a probabilistic method. Consider an encoding function
that is selected at random. This means that for each message
, the value
is selected at random (with equal probabilities). For a given encoding function
, the decoding function
is specified as follows: given any received codeword
, we find the message
such that the
Hamming distance
is as small as possible (with ties broken arbitrarily). (
is called a maximum likelihood decoding function.)
The proof continues by showing that at least one such choice
satisfies the conclusion of theorem, by integration over the probabilities. Suppose
and
are fixed. First we show that, for a fixed
and
chosen randomly, the probability of failure over
noise is exponentially small in
n. At this point, the proof works for a fixed message
. Next we extend this result to work for all messages
. We achieve this by eliminating half of the codewords from the code with the argument that the proof for the decoding error probability holds for at least half of the codewords. The latter method is called expurgation. This gives the total process the name
random coding with expurgation.
Converse of Shannon's capacity theorem
The converse of the capacity theorem essentially states that
is the best rate one can achieve over a binary symmetric channel. Formally the theorem states:
The intuition behind the proof is however showing the number of errors to grow rapidly as the rate grows beyond the channel capacity. The idea is the sender generates messages of dimension
, while the channel
introduces transmission errors. When the capacity of the channel is
, the number of errors is typically
for a code of block length
. The maximum number of messages is
. The output of the channel on the other hand has
possible values. If there is any confusion between any two messages, it is likely that
. Hence we would have
k\geq\lceil(1-H(p+\epsilon)n)\rceil
, a case we would like to avoid to keep the decoding error probability exponentially small.
Codes
Very recently, a lot of work has been done and is also being done to design explicit error-correcting codes to achieve the capacities of several standard communication channels. The motivation behind designing such codes is to relate the rate of the code with the fraction of errors which it can correct.
The approach behind the design of codes which meet the channel capacities of
or the
binary erasure channel
have been to correct a lesser number of errors with a high probability, and to achieve the highest possible rate. Shannon's theorem gives us the best rate which could be achieved over a
, but it does not give us an idea of any explicit codes which achieve that rate. In fact such codes are typically constructed to correct only a small fraction of errors with a high probability, but achieve a very good rate. The first such code was due to George D. Forney in 1966. The code is a concatenated code by concatenating two different kinds of codes.
Forney's code
to achieve the capacity of the noisy-channel coding theorem for
. In his code,
is a code of block length
and rate
over the field
, and
. Additionally, we have a
decoding algorithm
for
which can correct up to
fraction of worst case errors and runs in
time.
is a code of block length
, dimension
, and a rate of
. Additionally, we have a decoding algorithm
for
with a
decoding error probability of at most
over
and runs in
time. For the outer code
, a Reed-Solomon code would have been the first code to have come in mind. However, we would see that the construction of such a code cannot be done in polynomial time. This is why a
binary linear code is used for
.
For the inner code
we find a
linear code by exhaustively searching from the
linear code of block length
and dimension
, whose rate meets the capacity of
, by the noisy-channel coding theorem.
The rate
R(C*)=R(Cin) x R(Cout)=(1-
)(1-H(p)-
)\geq1-H(p)-\epsilon
which almost meets the
capacity. We further note that the encoding and decoding of
can be done in polynomial time with respect to
. As a matter of fact, encoding
takes time
. Further, the decoding algorithm described takes time
as long as
; and
.
Decoding error probability
A natural decoding algorithm for
is to:
on
Note that each block of code for
is considered a symbol for
. Now since the probability of error at any index
for
is at most
and the errors in
are independent, the expected number of errors for
is at most
by linearity of expectation. Now applying
Chernoff bound, we have bound error probability of more than
errors occurring to be
. Since the outer code
can correct at most
errors, this is the
decoding error probability of
. This when expressed in asymptotic terms, gives us an error probability of
. Thus the achieved decoding error probability of
is exponentially small as the noisy-channel coding theorem.
We have given a general technique to construct
. For more detailed descriptions on
and
please read the following references. Recently a few other codes have also been constructed for achieving the capacities.
LDPC codes have been considered for this purpose for their faster decoding time.
[1] Applications
The binary symmetric channel can model a disk drive used for memory storage: the channel input represents a bit being written to the disk and the output corresponds to the bit later being read. Error could arise from the magnetization flipping, background noise or the writing head making an error. Other objects which the binary symmetric channel can model include a telephone or radio communication line or cell division, from which the daughter cells contain DNA information from their parent cell.
This channel is often used by theorists because it is one of the simplest noisy channels to analyze. Many problems in communication theory can be reduced to a BSC. Conversely, being able to transmit effectively over the BSC can give rise to solutions for more complicated channels.
See also
References
- Book: Thomas M. . Cover . Joy A. . Thomas . Elements of Information Theory . Wiley . Hoboken, New Jersey . 978-0-471-24195-9. 1991 .
- G. David Forney. Concatenated Codes. MIT Press, Cambridge, MA, 1966.
- Venkat Guruswamy's course on https://archive.today/20121215055356/http://www.cs.washington.edu/education/courses/533/06au/ Error-Correcting Codes: Constructions and Algorithms], Autumn 2006.
- Book: MacKay, David J.C. . David J. C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press. 2003. 0-521-64298-1.
- Atri Rudra's course on Error Correcting Codes: Combinatorics, Algorithms, and Applications (Fall 2007), Lectures 9, 10, 29, and 30.
- Madhu Sudan's course on Algorithmic Introduction to Coding Theory (Fall 2001), Lecture 1 and 2.
- A mathematical theory of communication C. E Shannon, ACM SIGMOBILE Mobile Computing and Communications Review.
- Modern Coding Theory by Tom Richardson and Rudiger Urbanke., Cambridge University Press
Notes and References
- Richardson and Urbanke