Forecasting Aided Energy Aware Band Assignment in Multiband Networks

Abstract

The high frequency communication bands (mmWave and sub-THz) promise tremendous data rates. However, they have very high power consumption which is particularly significant for battery-powered user-equipment (UE), and are prone to blockage. In this context, we design an energy-aware band-assignment system which reduces power consumption while aiming to achieve a target sum rate of M bits/sec in T time-slots. We do this by using 1) Rate forecaster(s); 2) Channel forecaster(s) which forecast either the data rate or the channel for T subsequent time slots, utilizing either a stacked Long-short-term memory (LSTM) or transformer architecture. These forecasts are used to select frequency bands using an iterative algorithm. The proposed approach is validated on the publicly available `DeepMIMO', and `NYUSIM' datasets for both outdoor and indoor scenarios, and using a multiband empirical system. Moreover, we also propose a simple blockage segment generation algorithm such that the channel realizations inherently capture the effects of blockage. We find that the rate-forecaster-based approach outperforms the channel forecaster. Further, our approach consumes ~300 mW lower power compared to a greedy band assignment at a 1.5 Gb/s target rate for outdoor scenarios, and up to 600 mW for indoor scenarios at 2.5 Gb/s target rate, indicating that this is a promising method to reduce UE power consumption in multiband systems.