Abstract—Millimeter-wave (mmWave) and sub-Terahertz
(THz) frequencies are expected to play a vital role in 6G wireless
systems and beyond due to the vast available bandwidth of many
tens of GHz. This paper presents an indoor 3-D spatial statistical
channel model for mmWave and sub-THz frequencies based on
extensive radio propagation measurements at 28 and 140 GHz
conducted in an indoor office environment from 2014 to 2020.
Omnidirectional and directional path loss models and channel
statistics such as the number of time clusters, cluster delays,
and cluster powers were derived from over 15,000 measured
power delay profiles. The resulting channel statistics show that
the number of time clusters follows a Poisson distribution and
the number of subpaths within each cluster follows a composite
exponential distribution for both LOS and NLOS environments
at 28 and 140 GHz. This paper proposes a unified indoor statistical
channel model for mmWave and sub-Terahertz frequencies
following the mathematical framework of the previous outdoor
NYUSIM channel models. A corresponding indoor channel simulator
is developed, which can recreate 3-D omnidirectional,
directional, and multiple input multiple output (MIMO) channels
for arbitrary mmWave and sub-THz carrier frequency up to
150 GHz, signal bandwidth, and antenna beamwidth. The presented
statistical channel model and simulator will guide future
air-interface, beamforming, and transceiver designs for 6G and
beyond.
Index Terms—Millimeter-wave, terahertz, radio propagation,
indoor office scenario, channel measurement, channel modeling,
channel simulation, NYUSIM, 28 GHz, 140 GHz, 142 GHz,
5G, 6G.
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Robust Deep Learning-based Indoor mmWave Channel Prediction Under Concept Drift
The mmWave WiGig frequency band can support high throughput and low latency emerging applications. In this context, accurate prediction of channel gain enables seamless connectivity with user mobility via proactive handover and beamforming. Machine learning techniques have been widely adopted in literature for mmWave channel prediction. However, the existing techniques assume that the indoor mmWave channel follows a stationary stochastic process. This paper demonstrates that indoor WiGig mmWave channels are non-stationary where the channel’s cumulative distribution function (CDF) changes with the user’s spatio-temporal mobility. Specifically, we show significant differences in the empirical CDF of the channel gain based on the user’s mobility stage, namely, room entering, wandering, and exiting. Thus, the dynamic WiGig mmWave indoor channel suffers from concept drift that impedes the generalization ability of deep learning-based channel prediction models. Our results demonstrate that a state-of-the-art deep learning channel prediction model based on a hybrid convolutional neural network (CNN) long-short-term memory (LSTM) recurrent neural network suffers from a deterioration in the prediction accuracy by 11–68% depending on the user’s mobility stage and the model’s training. To mitigate the negative effect of concept drift and improve the generalization ability of the channel prediction model, we develop a robust deep learning model based on an ensemble strategy. Our results show that the weight average ensemble-based model maintains a stable prediction that keeps the performance deterioration below 4%.
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- PAR ID:
- 10516300
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-2928-5
- Page Range / eLocation ID:
- 1 to 5
- Subject(s) / Keyword(s):
- Degradation Deep learning Training Vehicular and wireless technologies Predictive models Throughput Convolutional neural networks Channel prediction WiGig mmWave concept drift deep learning generalization domain adaptation
- Format(s):
- Medium: X
- Location:
- Hong Kong, Hong Kong
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/VTC2023-Fall60731.2023.10333513
