Predictive Pre-allocation for Low-latency Uplink Access in Industrial Wireless Networks

Abstract

Driven by mission-critical applications in modern industrial systems, the 5th generation (5G) communication system is expected to provide ultra-reliable low-latency communications (URLLC) services to meet the quality of service (QoS) demands of industrial applications. However, these stringent requirements cannot be guaranteed by its conventional dynamic access scheme due to the complex signaling procedure. A promising solution to reduce the access delay is the pre-allocation scheme based on the semi-persistent scheduling (SPS) technique, which however may lead to low spectrum utilization if the allocated resource blocks (RBs) are not used. In this paper, we aim to address this issue by developing DPre, a predictive pre-allocation framework for uplink access scheduling of delay-sensitive applications in industrial process automation. The basic idea of DPre is to explore and exploit the correlation of data acquisition and access behavior between nodes through static and dynamic learning mechanisms in order to make judicious resource perallocation decisions. We evaluate the effectiveness of DPre based on several monitoring applications in a steel rolling production process. Simulation results demonstrate that DPre achieves better performance in terms of the prediction accuracy, which can effectively increase the rewards of those reserved resources.

An End-to-End Trainable Feature Selection-Forecasting Architecture Targeted at the Internet of Things

“…1, we aim to compare the forecasting performance of our FSF architecture against the performance obtained by a selected subset of the following (feature selection, forecaster) pairs: Autocorrelation Function (ACF), Analysis of Variance (ANOVA), Recurrent Feature Elimination (RFE) and Ridge Regression for feature selection, and Linear Regression, Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM), 1 Dimensional Convolutional Neural Network (1D CNN), Autoregressive Integrated Moving Average (ARIMA), and Logistic Regression for forecasting. 7 To this end, in this section, we first present the collection and processing methodology for IoT traffic data sets on which we have obtained our results. Second, we present the methodology for competing feature selection methods and for tuning the hyperparameters of competing forecasting models.…”

“…We calculate the number of parameters excluding those in the Forecasting module because the number of parameters in the Forecasting module depends on the particular forecasting scheme 7. In order to compare the performance of the FSF architecture against that of representative (feature selection, forecaster) pairs, we have selected ACF-based and ANOVA-based feature selection as representatives of filterbased, RFE as a representative of wrapper-based, and Ridge Regression as a representative of embedded feature selection methods.6VOLUME 4, 2016 …”

High-Reliability and Low-Latency Wireless Communication for Internet of Things: Challenges, Fundamentals, and Enabling Technologies

“…Based on above approaches, reference [81] applied the delay-sensitive area spectral efficiency (DASE) as the objective function, which sought to minimize the total bandwidth consumed by the devices and simultaneously ensure the strict QoS constraint on reliability such that the DASE was maximized. Moreover, to achieve the different requirements associated with different application scenarios, the HRLL communications schemes with various application scenarios such as industrial process automation [82], factory settings [83], and typical indoor environments [84] were investigated. Additionally, from the view of spectrum effectiveness, the HRLL communications related to utilizing the unlicensed spectrum were surveyed in [85].…”

“…To highlight the importance of spectrum-efficient and power-efficient in HRLL IoT communications, we will give an example on the dynamic uplink transmission for an OFDM access (OFDMA) cellular network with D2D communications [103]. Access delay [82] Pre-allocation scheme based on the semi-persistent scheduling technique…”

Wireless Network Design for Emerging IIoT Applications: Reference Framework and Use Cases

“…5G NR defines more dedicated radio resources, such as the sub-millisecond time resolution in the transmission time interval (TTI) and OFDM symbol durations. Meanwhile, it also introduces extra transmission redundancy to improve the link reliability and employs quicker allocation schemes, e.g., smaller HARQ reply messages, to better support industrial applications in the URLLC scenario [8], [101], [110].…”