  In current wireless systems, the base-Station (eNodeB) tries to serve its
user-equipment (UE) at the highest possible rate that the UE can reliably
decode. The eNodeB obtains this rate information as a quantized feedback from
the UE at time n and uses this, for rate selection till the next feedback is
received at time n + {\delta}. The feedback received at n can become outdated
before n + {\delta}, because of a) Doppler fading, and b) Change in the set of
active interferers for a UE. Therefore rate prediction becomes essential.
Since, the rates belong to a discrete set, we propose a discrete sequence
prediction approach, wherein, frequency trees for the discrete sequences are
built using source encoding algorithms like Prediction by Partial Match (PPM).
Finding the optimal depth of the frequency tree used for prediction is cast as
a model order selection problem. The rate sequence complexity is analysed to
provide an upper bound on model order. Information-theoretic criteria are then
used to solve the model order problem. Finally, two prediction algorithms are
proposed, using the PPM with optimal model order and system level simulations
demonstrate the improvement in packet loss and throughput due to these
algorithms.
