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.

In this extended study, we consider both outdoor and indoor scenarios, and blockage effects of mmWave and Thz. We further validate the proposed framework using DeepMIMO and NYUSIM datasets for both outdoor and indoor environments. We developed an empirical testbed to collect multiband indoor measurement data and evaluate the proposed approach