  The high frequency communication bands (mmWave and sub-THz) promise
tremendous data rates, however, they also have very high power consumption
which is particularly significant for battery-power-limited user-equipment
(UE). In this context, we design an energy aware band assignment system which
reduces the power consumption while also achieving a target sum rate of M in T
time-slots. We do this by using 1) Rate forecaster(s); 2) Channel forecaster(s)
which forecasts T direct multistep ahead using a stacked (long short term
memory) LSTM architecture. We propose an iterative rate updating algorithm
which updates the target rate based on current rate and future predicted rates
in a frame. The proposed approach is validated on the publicly available
`DeepMIMO' dataset. Research findings shows that the rate forecaster based
approach performs better than the channel forecaster. Furthermore, LSTM based
predictions outperforms well celebrated Transformer predictions in terms of
NRMSE and NMAE. Research findings reveals that the power consumption with this
approach is ~ 300 mW lower compared to a greedy band assignment at a 1.5Gb/s
target rate.
