ABSTRACT
Wireless communications technology is widely used in many applications throughout the world. Wireless networks of different types serve target customers with different requirements from general-purpose consumer electronics to telemedicine supporting a diverse range of healthcare applications including critical life-saving missions, there are different reliability requirements for different situations. The main objective of any communication system is to deliver as much information as possible from a source to the intended recipients in a timely manner. The information received should be an exact copy of what has been sent out from the source. An ideal communication system should be available any time and it should
be able to deliver information in real time without any delay or loss of information. In reality, no such system exists because practical systems can fail due to a number of reasons. There is a long list of possible causes of failure. Apart from the usual suspects of hardware problems and human errors, there are certain inherent problems such as additive noise and interference. In information theory, where ‘how good’ information is sent from its source to destination, is quantitatively measured in terms of the ‘likelihood’ that it arrives to the intended recipient intact. We can build a statistical model that describes the behavior of the communication system so that how the information in transit is expected to go through can be predicted based on past experience. So, we can describe statistical attributes like how much noise will be added to the signal during transmission, how long a delay is anticipated for the data to reach the recipient, how much interference is along the path, how likely is a hardware to fail and for how long. We can then use such knowledge to improve system reliability according to different situations.
