Time varying channel models for 5g mobile communication systems

Researchers all over the world are looking for ways of continuing the evolution of mobile communication technology to its fifth generation (5G). Providing high data rate information transfer to highly mobile users over time varying communication channels remains a shared obstacle. In this thesis, we contribute to these global efforts by providing further fundamental understanding of time varying channels in 5G mobile communication systems and overcome the obstacle. First, we reopen the door of research in the field of time varying communication channels. The door has almost been closed before by a well-accepted conclusion related to the types of channels. It was ‘proven’ that mutual information rate of the uniformly symmetric variable noise finite state Markov channel (USVNFSMC) was maximized by input signals of maximum information entropy. The result means time varying channels and time invariable channels are identical, regarding information rate maximization over input signal probability distribution. We provide evidence that assumptions for the results are not valid for time varying channels and replace them with more practical ones. We confirm, via input signals of non-uniform independent distribution and first order Markov chain, that the mutual information rate of the USVN-FSMC is maximized by input signals with information redundancy. Second, we provide a solution which dramatically reduces the waste of communication resources in estimating channel state information of time varying mobile communication channels. The orthodox method in dealing with time varying channels is that, the channel is “cut” to pieces in time domain to look like a sequence of time invariable channels for the purpose of state estimation. By doing this the capacity loss is staggering for n-times higher carrier frequency channels and n-dimensional multiple input and multiple output channels, eliminating almost entirely the capacity gain of these two most promising capacity-increasing techniques for 5G. We define the simplest finite state Markov model for time varying channels to explain the essential difference between information processing of time varying channels and time invariable channels. We prove that the full information capacity of the model can be achieved by the differential type encoding/decoding scheme without employing any conventional channel state estimator.