n theory'. One equation calculates entropy as the average amount of information that a source generates. Source entropy is useful in that it can be used to determine the minimum required transmission rate of a link for communicating the information. Source entropy is also important in designing efficient loss-less source coding. Business implications of source entropy are in the design of audio and video source coders used in mobile phones for example and in data compression applications (such as 'winzip'). Shannon's other equation defines the maximum amount of information that may be trans- mitted over a band-limited channel. This is called channel capacity and was initially used by Claude Shannon: www.flickr.com/photos/tekniskamuseet/6832884236. Broadband Telecommunications Technologies and Management Information Transmitter Receiver Destination source Message Message Noise source Figure 4.1 Communications system block diagram from Shannon's landmark paper AT&T to determine the maximum possible rate of transmission of telegraph messages over their network in the presence of noise. At the time it was well understood that channel capacity was directly related to the available bandwidth: that is the larger the size of the pipe, the more information it can carry. However, the effect of noise was not known. Figure 4.1 shows a block diagram of a telecommunications network, including signal power and additive noise as drawn in Shannon's paper [2]. Shannon's equation calculates channel capacity in the presence of such random noise. This equation is fundamental to the design of all digital telecommunications networks, from space probes now travelling, and communicating with earth at the edge of the Solar System, as well as digital mobile systems we use every day. The equation is known as Shannon's theorem, and is widely used to design, dimension and compare communication systems and technologies. Its business implications are evident in the design of all digital communications systems we use today. These contributions h