Let’s discover the answer for this question in detail. Any discussion about the design of a communication system will be incomplete without mentioning Shannon’s Theorem. Shannon’s information theory tells us the amount of information a channel can carry. In other words it specifies the capacity of the channel. The theorem can be stated in simple terms as follows  A given communication system has a maximum rate of information C known as the channel capacity  If the transmission information rate R is less than C, then the data transmission in the presence of noise can be made to happen with arbitrarily small error probabilities by using intelligent coding techniques  To get lower error probabilities, the encoder has to work on longer blocks of signal data. This entails longer delays and higher computational requirements The Shannon-Hartley theorem indicates that with sufficiently advanced coding techniques, transmission that nears the maximum channel capacity – is possible with arbitrarily small errors. One can intuitively reason that, for a given communication system, as the information rate increases, the number of errors per second will also increase. Shannon – Hartley Equation Shannon-Hartley equation relates the maximum capacity (transmission bit rate) that can be achieved over a given channel with certain noise characteristics and bandwidth. The maximum capacity is given by
C=B×log2(1+S/N) Here (C) is the maximum capacity of the channel in bits/second otherwise called Shannon’s capacity limit for the given channel, (B) is the bandwidth of the channel in Hertz, (S) is the signal power in Watts and (N) is the noise power, also in Watts. The ratio (S/N) is called Signal to Noise Ratio (SNR). It can be ascertained that the maximum rate at which we can transmit the information without any error, is limited by the bandwidth, the signal level, and the noise level. It tells how many bits can be transmitted per second without errors over a channel of bandwidth (B \; Hz), when the signal power is limited to (S \; Watts) and is exposed to Gaussian White (uncorrelated) Noise ((N \; Watts)) of additive nature. Shannon’s capacity limit is defined for the given channel. It is the fundamental maximum transmission capacity that can be achieved on a channel given any combination of any coding
scheme, transmission or decoding scheme. It is the best performance limit that we hope to achieve for that channel. The above expression for the channel capacity makes intuitive sense:  Bandwidth limits how fast the information symbols can be sent over the given channel  The SNR ratio limits how much information we can squeeze in each transmitted symbols. Increasing SNR makes the transmitted symbols more robust against noise. SNR is a function of signal quality, signal power and the characteristics of the channel. It is measured at the receiver’s front end  To increase the information rate, the signal-to-noise ratio and the allocated bandwidth have to be traded against each other  For no noise, the signal to noise ratio becomes infinite and so an infinite information rate is possible at a very small bandwidth Thus we may trade off bandwidth for SNR. However, as the bandwidth (B) tends to infinity, the channel capacity does not become infinite – since with an increase in bandwidth, the noise power also increases. The Shannon’s equation relies on two important concepts:  That, in principle, a trade-off between SNR and bandwidth is possible  That, the information capacity depends on both SNR and bandwidth