<P> Statistically a communication channel is usually modelled as a triple consisting of an input alphabet, an output alphabet, and for each pair (i, o) of input and output elements a transition probability p (i, o). Semantically, the transition probability is the probability that the symbol o is received given that i was transmitted over the channel . </P> <P> Statistical and physical modelling can be combined . For example, in wireless communications the channel is often modelled by a random attenuation (known as fading) of the transmitted signal, followed by additive noise . The attenuation term is a simplification of the underlying physical processes and captures the change in signal power over the course of the transmission . The noise in the model captures external interference and / or electronic noise in the receiver . If the attenuation term is complex it also describes the relative time a signal takes to get through the channel . The statistics of the random attenuation are decided by previous measurements or physical simulations . </P> <P> Channel models may be continuous channel models in that there is no limit to how precisely their values may be defined . </P> <P> Communication channels are also studied in a discrete - alphabet setting . This corresponds to abstracting a real world communication system in which the analog → digital and digital → analog blocks are out of the control of the designer . The mathematical model consists of a transition probability that specifies an output distribution for each possible sequence of channel inputs . In information theory, it is common to start with memoryless channels in which the output probability distribution only depends on the current channel input . </P>

Why do we need protocols for broadcast channels