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.
From: Brijesh Soni [view email]
[v1]
Thu, 8 Jun 2023 17:10:27 UTC (407 KB)
[v2]
Sat, 19 Aug 2023 19:15:30 UTC (407 KB)
[v3]
Tue, 11 Aug 2026 16:30:27 UTC (1,656 KB)
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