Rate Forecaster based Energy Aware Band Assignment in Multiband Networks

The high frequency communication bands (mm Wave 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. We find that the rate forecaster based approach performs better than the channel forecaster. Furthermore, LSTM based predictions outperforms well celebrated Transformer predictions in terms of normalized root mean square error (NRMSE) and normalized mean absolute error (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.