**INFORMATION CAPACITY**

The
maximum rate at which data can be transferred through a channel without error
is known as the information capacity. Bits per second are used to calculate it.

The
maximum rate at which reliable communication may occur through the channel is
an important factor in evaluating the performance of a digital communication
system.

**SHANNON’S LIMIT FOR INFORMATION CAPACITY**

**Shannon-Hartley capacity theorem:**

The
fundamental limit on the rate of error-free transmission for a power-limited,
band-limited Gaussian channel is defined by Shannon's channel capacity theorem.
The average received signal power S, the average noise power N, and the
bandwidth B determine the information capacity C of a channel affected by
additive white Gaussian Noise (AWGN). The Shannon-Hartley theorem describes the
information capacity connection as follows:

C
= B log_{2}( 1+ ๐/๐), bits/s
------------------------ (1)

The
noise power may be rewritten as N=N_{o}B, where No denotes the noise
power spectral density. As a result, the theorem may be expressed as

C
= B log_{2}( 1+ ๐/๐_{๐}๐ต), bits/s
------------------------- (2)

The
following are the significance of channel capacity:

I
If the source's information rate R is less than or equal to the channel
capacity C (R ≤ C), then proper coding can be used to achieve reliable
(error-free) transmission via the channel.

(ii)
It is impossible to create a code that can provide reliable (error-free)
transmission via the channel if the information rate R from the source is
larger than the channel capacity C (R > C).

As a result, Shannon established fundamental restrictions on information communication, resulting in the emergence of a new subject known as Information Theory.