Wireless x-haul networks rely on microwave and millimeter-wave links between 4G and/or 5G
base-stations to support ultra-high data rate and ultra-low latency. A major challenge associated with
these high frequency links is their susceptibility to weather conditions. In particular, precipitation may
cause severe signal attenuation, which significantly degrades the network performance. In this paper, we
develop a Predictive Network Reconfiguration (PNR) framework that uses historical data to predict the
future condition of each link and then prepares the network ahead of time for imminent disturbances.
The PNR framework has two components: (i) an Attenuation Prediction (AP) mechanism; and (ii)
a Multi-Step Network Reconfiguration (MSNR) algorithm. The AP mechanism employs an encoderdecoder Long Short-Term Memory (LSTM) model to predict the sequence of future attenuation levels
of each link. The MSNR algorithm leverages these predictions to dynamically optimize routing and
admission control decisions aiming to maximize network utilization, while preserving max-min fairness
among the base-stations sharing the network and preventing transient congestion that may be caused
by re-routing. We train, validate, and evaluate the PNR framework using a dataset containing over 2
million measurements collected from a real-world city-scale backhaul network. The results show that
the framework: (i) predicts attenuation with high accuracy, with an RMSE of less than 0.4 dB for a
prediction horizon of 50 seconds; and (ii) can improve the instantaneous network utilization by more than
200% when compared to reactive network reconfiguration algorithms that cannot leverage information
about future disturbances
more »
« less
RAN Resource Usage Prediction for a 5G Slice Broker
Network slicing will allow 5G network operators to o�er a diverse
set of services over a shared physical infrastructure. We focus on
supporting the operation of the Radio Access Network (RAN) slice
broker, which maps slice requirements into allocation of Physical
Resource Blocks (PRBs). We �rst develop a new metric, REVA, based
on the number of PRBs available to a single Very Active bearer. REVA
is independent of channel conditions and allows easy derivation
of an individual wireless link’s throughput. In order for the slice
broker to e�ciently utilize the RAN, there is a need for reliable
and short term prediction of resource usage by a slice. To support
such prediction, we construct an LTE testbed and develop custom
additions to the scheduler. Using data collected from the testbed, we
compute REVA and develop a realistic time series prediction model
for REVA. Speci�cally, we present the X-LSTM prediction model,
based upon Long Short-Term Memory (LSTM) neural networks.
Evaluated with data collected in the testbed, X-LSTM outperforms
Autoregressive Integrated Moving Average Model (ARIMA) and
LSTM neural networks by up to 31%. X-LSTM also achieves over
91% accuracy in predicting REVA. By using X-LSTM to predict
future usage, a slice broker is more adept to provision a slice and
reduce over-provisioning and SLA violation costs by more than
10% in comparison to LSTM and ARIMA.
more »
« less
- NSF-PAR ID:
- 10093765
- Date Published:
- Journal Name:
- Mobihoc '19
- ISSN:
- 1553-121X
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
4G, 5G, and smart city networks often rely on microwave and millimeter-wave x-haul links. A major challenge associated with these high frequency links is their susceptibility to weather conditions. In particular, precipitation may cause severe signal attenuation, which significantly degrades the network performance. In this paper, we develop a Predictive Network Reconfiguration (PNR) framework that uses historical data to predict the future condition of each link and then prepares the network ahead of time for imminent disturbances. The PNR framework has two components: (i) an Attenuation Prediction (AP) mechanism; and (ii) a Multi-Step Network Reconfiguration (MSNR) algorithm. The AP mechanism employs an encoder-decoder Long Short-Term Memory (LSTM) model to predict the sequence of future attenuation levels of each link. The MSNR algorithm leverages these predictions to dynamically optimize routing and admission control decisions aiming to maximize network utilization, while preserving max-min fairness among the nodes using the network (e.g., base-stations) and preventing transient congestion that may be caused by switching routes. We train, validate, and evaluate the PNR framework using a dataset containing over 2 million measurements collected from a real-world city-scale backhaul network. The results show that the framework: (i) predicts attenuation with high accuracy, with an RMSE of less than 0.4 dB for a prediction horizon of 50 seconds; and (ii) can improve the instantaneous network utilization by more than 200% when compared to reactive network reconfiguration algorithms that cannot leverage information about future disturbances.more » « less
-
Abstract. As a genre of physics-informed machine learning, differentiable process-based hydrologic models (abbreviated as δ or delta models) with regionalized deep-network-based parameterization pipelines were recently shown to provide daily streamflow prediction performance closely approaching that of state-of-the-art long short-term memory (LSTM) deep networks. Meanwhile, δ models provide a full suite of diagnostic physical variables and guaranteed mass conservation. Here, we ran experiments to test (1) their ability to extrapolate to regions far from streamflow gauges and (2) their ability to make credible predictions of long-term (decadal-scale) change trends. We evaluated the models based on daily hydrograph metrics (Nash–Sutcliffe model efficiency coefficient, etc.) and predicted decadal streamflow trends. For prediction in ungauged basins (PUB; randomly sampled ungauged basins representing spatial interpolation), δ models either approached or surpassed the performance of LSTM in daily hydrograph metrics, depending on the meteorological forcing data used. They presented a comparable trend performance to LSTM for annual mean flow and high flow but worse trends for low flow. For prediction in ungauged regions (PUR; regional holdout test representing spatial extrapolation in a highly data-sparse scenario), δ models surpassed LSTM in daily hydrograph metrics, and their advantages in mean and high flow trends became prominent. In addition, an untrained variable, evapotranspiration, retained good seasonality even for extrapolated cases. The δ models' deep-network-based parameterization pipeline produced parameter fields that maintain remarkably stable spatial patterns even in highly data-scarce scenarios, which explains their robustness. Combined with their interpretability and ability to assimilate multi-source observations, the δ models are strong candidates for regional and global-scale hydrologic simulations and climate change impact assessment.more » « less
-
Machine and deep learning-based algorithms are the emerging approaches in addressing prediction problems in time series. These techniques have been shown to produce more accurate results than conventional regression-based modeling. It has been reported that artificial Recurrent Neural Networks (RNN) with memory, such as Long Short-Term Memory (LSTM), are superior compared to Autoregressive Integrated Moving Average (ARIMA) with a large margin. The LSTM-based models incorporate additional “gates” for the purpose of memorizing longer sequences of input data. The major question is that whether the gates incorporated in the LSTM architecture already offers a good prediction and whether additional training of data would be necessary to further improve the prediction. Bidirectional LSTMs (BiLSTMs) enable additional training by traversing the input data twice (i.e., 1) left-to-right, and 2) right-to-left). The research question of interest is then whether BiLSTM, with additional training capability, outperforms regular unidirectional LSTM. This paper reports a behavioral analysis and comparison of BiLSTM and LSTM models. The objective is to explore to what extend additional layers of training of data would be beneficial to tune the involved parameters. The results show that additional training of data and thus BiLSTM-based modeling offers better predictions than regular LSTM-based models. More specifically, it was observed that BiLSTM models provide better predictions compared to ARIMA and LSTM models. It was also observed that BiLSTM models reach the equilibrium much slower than LSTM-based models.more » « less
-
This work proposes an Adaptive Fuzzy Prediction (AFP) method for the attenuation time series in Commercial Microwave links (CMLs). Time-series forecasting models regularly rely on the assumption that the entire data set follows the same Data Generating Process (DGP). However, the signals in wireless microwave links are severely affected by the varying weather conditions in the channel. Consequently, the attenuation time series might change its characteristics significantly at different periods. We suggest an adaptive framework to better employ the training data by grouping sequences with related temporal patterns to consider the non-stationary nature of the signals. The focus in this work is two-folded. The first is to explore the integration of static data of the CMLs as exogenous variables for the attenuation time series models to adopt diverse link characteristics. This extension allows to include various attenuation datasets obtained from additional CMLs in the training process and dramatically increasing available training data. The second is to develop an adaptive framework for short-term attenuation forecasting by employing an unsupervised fuzzy clustering procedure and supervised learning models. We empirically analyzed our framework for model and data-driven approaches with Recurrent Neural Network (RNN) and Autoregressive Integrated Moving Average (ARIMA) variations. We evaluate the proposed extensions on real-world measurements collected from 4G backhaul networks, considering dataset availability and the accuracy for 60 seconds prediction. We show that our framework can significantly improve conventional models’ accuracy and that incorporating data from various CMLs is essential to the AFP framework. The proposed methods have been shown to enhance the forecasting model’s performance by 30 − 40%, depending on the specific model and the data availability.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3323679.3326521
