Communication

Section III

 

Technical Aspects of Communication


Communications is gradually converging into a single fiber optic and wireless worldwide network for all types of communication. To understand the problems in creating this network we need to understand the concept of information from the perspective of communication engineers. This concept enables communication engineers to design communication networks with enough capacity to carry the intended messages. It also provides a theory to obtain the maximum compression of digital messages.

Information



Shannon in 1949 developed a theory of communication based on a measure of information which enables communication engineers to determine the communication capacity required to communicate the messages. In order to discuss Shannon's measure of the information content in a message, consider the following simple diagram of a communication system:

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At the information source the input message is digitized and at the destination the message is converted back into the input form, that is, text, voice, etc. Noise will cause the output to differ from the input. To build a communication system, we need to know the volume of the message. Intuitively, consider a fresh water system in a city. To build this system you must know the size of pipe to transmit the desired amount of water to each household. Since we have converted the message into binary code, Shannon's contribution was to define the message volume in bits.

To gain some intuitive understanding of his concept we need to consider some examples. If you had to transmit an infinite string of 1's with no zero's, you can use the string's pattern to greatly compress the number of bits you must transmit. In this case, once you remove the pattern there is nothing left to transmit. Now suppose 0's were placed at random intervals in the string of 1's. You would have to transmit the pattern of all ones and also transmit the locations of the zero's. The greater difficulty in transmitting the second message over the first is directly related to the number of randomly spaced 0's. Now consider a picture composed of a green field. To transmit the message all you must transmit is the pattern. The video message becomes progressively more difficult to transmit depending on the number of blue dots randomly placed in the picture. The Shannon information message is a measure of the randomness, or entropy, of the message. Shannon's measure says nothing about the meaning of the message. The communication engineer does not care whether the message is nonsense syllables or national secrets. Entropy, or randomness, refers to number of bits required to transmit the random elements. Shannon is very important because he provided communication engineers with a theory for designing appropriately sized communication channels for transmitting the information content of the messages. If the channel capacity is greater than the information measure (entropy) of the message, it is theoretically possible to transmit the message without error in a noisy channel. On the crest and trough of every wave you can place a bit or no bit. Thus to transmit a message the frequency of channel must be greater than one half the number of bits which must be transmitted per second.

Message Compression



Digital communications presents a fundamental problem in that the raw digitization of analog messages greatly increases their size. One approach is to use a bigger communication channel to transmit the digitized message. A better approach to deal with raw digitized messages is to compress the message using compression algorithms, which in the case of teleconferencing are called codices. For example, a telephone conversation is digitized into 64,000 bits/sec. The entropy measure of a voice message may be less than 1000 bits/sec. Communication engineers have developed techniques to compress messages so that the amount of bits is closer to the entropy measure. For example, telephone conversations can be transmitted by voice recorder at 2400 baud, a term meaning bits per second. In order to market inexpensive teleconferencing there are powerful incentives to compress video messages. The current status is:

Type

Baud (transmission) Rate

 

Slides

1.2K - 56K

 

Poor to medium quality motion

56K - 1.2k

Quality motion

1.2M - 140M


You should anticipate rapid advances in message compression which will reduce the baud rate required to transmit various types of images. Transmitting a detailed picture with motion is bound to be 1000 times as costly as transmitting text and about 100 times as expensive as transmitting voice. We do not use video for communication today simply because it is far too expensive.

 

Compression:

Check out various types of compression technology: