  Accurate prediction of fading channel in the upcoming transmission frame is
essential to realize adaptive transmission for transmitters, and receivers with
the ability of channel prediction can also save some computations of channel
estimation. However, due to the rapid channel variation and channel estimation
error, reliable prediction is hard to realize. In this situation, an
appropriate channel model should be selected, which can cover both the
statistical model and small scale fading of channel, this reminds us the
natural languages, which also have statistical word frequency and specific
sentences. Accordingly, in this paper, we take wireless channel model as a
language model, and the time-varying channel as talking in this language, while
the realistic noisy estimated channel can be compared with mumbling.
Furthermore, in order to utilize as much as possible the information a channel
coefficient takes, we discard the conventional two features of absolute value
and phase, replacing with hundreds of features which will be learned by our
channel model, to do this, we use a vocabulary to map a complex channel
coefficient into an ID, which is represented by a vector of real numbers.
Recurrent neural networks technique is used as its good balance between
memorization and generalization, moreover, we creatively introduce
sequence-to-sequence (seq2seq) models in time series channel prediction, which
can translates past channel into future channel. The results show that
realistic channel prediction with superior performance relative to channel
estimation is attainable.
