Revisiting Unlicensed Channel Access Scheme of 5G New Radio

BACKGROUND: As the second phase of 5G standardization efforts encapsulated in Release 16 comes to its freeze and completion date in June 2020, aspects of some promised features and services started to crystallize. Among of which, New Radio (NR)-based access to unlicensed spectrum, commonly known as 5G NR-U. Current technical reports have identified Listen-Before-Talk (LBT) as a working assumption in the process of standardizing NR-U channel access scheme. LBT was originally developed for Licensed-Assisted Access (LAA) in release 13 of the 3GPP specifications, which was based on ETSI regulations. This research examines how next-generation wireless systems using LBT perform under vastly presumed 5G NR dense deployments, and how the coexistence landscape manifests in the homogeneous prospect rather than the widely investigated heterogeneous counterpart, e.g. with Wi-Fi. METHODS: In this work, a simulator was developed in C++ to help analyze different intra-network NR-U co-channel scenarios under saturated traffic. The simulator was validated with Markov Chain analytical model to confirm the procedures and algorithms conform to the standard delineated by the 3GPP specifications. RESULTS: Simulation results indicated inefficiency in channel utilization of homogeneous dense deployments with high priority traffic classes. For instance, the effective channel utilization drops to less than 10% when only 20 devices share the channel with traffic tagged as priority 4, e.g., voice calls. Moreover, mean delay between successful packet transmissions in aforesaid scenario turned out to be around 1 second and exponentially increasing with the number of devices sharing the channel. We demonstrated through simulations how LBT devices can be unfair when sharing the channel with others exhibiting different traffic priority classes. A video streaming device – i.e. class 3 – for example, takes away 42% of the channel when sharing it with other 7 devices browsing the internet – i.e. class 2 – leaving them with 34% of useful channel time to split. The remaining 24% of the time packets collide with each other, rendering the channel futile and reducing the overall throughput. CONCLUSION: Literature is inundated with research on cross-technology coexistence analysis. This work aims to study same-technology wireless coexistence performance and underlines the importance of improving channel access mechanisms in next-generation wireless communication.N

Examining the Ability of an FSO Receiver to Simultaneously Communicate with Multiple Transmitters

FSO (Free-Space Optical)-based communication systems experience difficulty with receiving and separating signals arising from multiple transmitters, a capability that would facilitate implementation of MIMO (Multiple-In, Multiple-Out) systems. Current implementations require multiple, distinct optical antennas, each tracking a single user, which proves bulky and costly, especially if the transmitters are moving and must be tracked. A fiber-bundle receiver has the potential to use multiple pathways, corresponding to the individual fibers within the receiver, to capture different combinations of the incoming optical signals. If the bundle provides linear combining of the optical signals from both the individual fibers in the bundle and amongst the incoming optical signals, signal processing could extract the individual signals from the combinations. In this paper, we experimentally investigate whether the fiber-bundle receiver possesses sufficient linearity of operation to allow the separation of two signals by simple processing algorithms, for both turbulent and non-turbulent conditions. Data from two distinct sources enters a single-bundle, single field of view receiver, and a basis signal from one transmitter provides the reference for performing simple subtraction-based extraction of the unknown signal from the other transmitter. The experimental results show that the receiver does operate linearly, and that the linear operation remains sufficiently intact in the presence of turbulence to extract a recognizable copy of one signal from the other. The ability of the fiber bundle receiver to mitigate turbulence effects appears to assist in maintaining this sufficient level of linearity

The Study of Vehicle Classification Equipment with Solutions to Improve Accuracy in Oklahoma

The accuracy of vehicle counting and classification data is vital for appropriate future highway and road design, including determining pavement characteristics, eliminating traffic jams, and improving safety. Organizations relying on vehicle classifiers for data collection should be aware that systems can be affected by hardware and sensor malfunction, as well as the equipmentÍs implementation of classification scheme (i.e., algorithm). This report presents outcomes from an extensive statewide examination of vehicle misclassification at Oklahoma Department of Transportation (ODOT) AVC stations employing the PEEK Traffic 'FHWA-USA' classification algorithm. A ground truth system utilizing continuous video recordings was developed and utilized. Results from the rigorous investigation are reported herein. Also detailed in this report is a novel method for an improved classification algorithm designed to reduce the number of classification errors. Thirteen Gaussian distributions were employed to model axle spacing for each of the 13 FHWA vehicle types. Classifications obtained from video recordings and PEEK Traffic axle spacing measurements for a sample of 20,000 vehicles were recorded and analyzed to obtain 13 good-fit Gaussian distributions that correspond with each vehicle class. An optimization algorithm was then implemented to develop axle spacing thresholds for vehicles currently traveling Oklahoma's highways and to minimize vehicle misclassification. The new scheme was then implemented in the PEEK Traffic automatic data record equipment and experimentally evaluated for accuracy. Results demonstrated its effectiveness in improving vehicle classifications and reducing persistent overall system errors characteristic of the 'FHWA-USA' Scheme. Analysis methodology detailed in this report will benefit organizations interested in improving vehicle classification and overall system accuracy.Final report, October 2013-October 2014N

National Performance Management Research Dataset (NPMRDS) - Speed Validation for Traffic Performance Measures (FHWA-OK-17-02)

This report presents research detailing the use of the first version of the National Performance Management Research Data Set (NPMRDS v.1) comprised of highway vehicle travel times used for computing performance measurements in the state of Oklahoma. Data extraction, preprocessing, and statistical analysis were performed on the dataset and acomprehensive study of dataset characteristics, influencing variables, outliersand anomalies was carried out. In addition, a study on filtering and removing speed data outliers across multiple road segments is developed, and a comparative analysis of raw baseline speed data and cleansed data is performed. A method for improved congestion detection is investigated and developed. Identification and a computational comparison analysis of travel time reliability performance metrics for both raw and cleansed datasets is shown. An outlier removal framework is formulated, and a cleansed and complete version of NPMRDS v.1 is generated. Finally, a validation analysis on the cleansed dataset is presented. In the end, research affirmsthat understanding domain specific characteristics is vital for filtering data outliers and anomalies of this dataset,which in turn is key for calculating accurate performance measurements. Thus, careful consideration for outlierremoval must be taken into account when computing travel time reliability metrics using the NPMRDS.October 2015-October 2017N

Integration of Simultaneous Resting-State EEG, fMRI, and Eye Tracker Methods to Determine and Verify EEG Vigilance Measure

Resting-state functional magnetic resonance imaging (rsfMRI) has been widely used for studying the (presumably) awake and alert human brain. Although rsfMRI scans are typically collected while individuals are instructed to focus their eyes on a fixation cross, objective and verified experimental measures to quantify degree of alertness (e.g., vigilance) are not readily available. Concurrent electroencephalography and fMRI (EEG-fMRI) measurements are also widely used to study human brain with high spatial/temporal resolution. EEG is the modality extensively used for estimating vigilance during eyes-closed resting state. On the other hand, pupil size measured using an eye-tracker device could provide an indirect index of vigilance. In this study, we investigated whether simultaneous multimodal EEG-fMRI combined with eye-tracker measurements can be used to determine EEG signal feature associated with pupil size changes (e.g., vigilance measure) in healthy human subjects (n=10) during brain rest with eyes open. We found that EEG frontal and occipital beta power (FOBP) correlates with pupil size changes, an indirect index for locus coeruleus activity implicated in vigilance regulation (r=0.306, p<0.001). Moreover, FOBP also correlated with heart rate (r=0.255, p<0.001), as well as several brain regions in the anti-correlated network, including the bilateral insula and inferior parietal lobule. These results support the conclusion that FOBP is an objective measure of vigilance in healthy human subjects

Predicting Age From Brain EEG Signals—A Machine Learning Approach

Objective: The brain age gap estimate (BrainAGE) is the difference between the estimated age and the individual chronological age. BrainAGE was studied primarily using MRI techniques. EEG signals in combination with machine learning (ML) approaches were not commonly used for the human age prediction, and BrainAGE. We investigated whether age-related changes are affecting brain EEG signals, and whether we can predict the chronological age and obtain BrainAGE estimates using a rigorous ML framework with a novel and extensive EEG features extraction.Methods: EEG data were obtained from 468 healthy, mood/anxiety, eating and substance use disorder participants (297 females) from the Tulsa-1000, a naturalistic longitudinal study based on Research Domain Criteria framework. Five sets of preprocessed EEG features across channels and frequency bands were used with different ML methods to predict age. Using a nested-cross-validation (NCV) approach and stack-ensemble learning from EEG features, the predicted age was estimated. The important features and their spatial distributions were deduced.Results: The stack-ensemble age prediction model achieved R2 = 0.37 (0.06), Mean Absolute Error (MAE) = 6.87(0.69) and RMSE = 8.46(0.59) in years. The age and predicted age correlation was r = 0.6. The feature importance revealed that age predictors are spread out across different feature types. The NCV approach produced a reliable age estimation, with features consistent behavior across different folds.Conclusion: Our rigorous ML framework and extensive EEG signal features allow a reliable estimation of chronological age, and BrainAGE. This general framework can be extended to test EEG association with and to predict/study other physiological relevant responses

An Experimental Investigation of Applying Mica2 Motes in Pavement Condition Monitoring

Pavement maintenance is vital for travel safety, thus detecting dangerous road conditions in a real-time fashion is desirable. Using an off-the-shelf wireless sensor network to detect such conditions at a low cost poses many challenges. In order to meet these challenges, a Mica2 Mote sensor network is adopted in this study to process and transmit data collected from three external analog sensors. Consequentially, several hardware and software interfaces are developed to complete a pavement monitoring system that uses temperature and moisture presence to detect hazardous road conditions. Surge Time Synchronization is explored in this specific application to enable the wireless sensor network to operate in a low power consumption mode. A fairly simplistic pattern classification algorithm is embedded into the motes to create the smart wireless sensing application. A series of outdoor tests are conducted in this study paying special attention to the survivability of fragile analog sensors in harsh roadway conditions. In this regard, a novel solution called the ``Sensor-Road Button''(SRB) is developed and validated experimentally. This is one of several exercises made in this study to enable the application of sensor technologies in intelligent transportation systems (ITS). The size of the wireless sensor network in this study is relatively small, utilizing a total of five motes in order to fully exploit the transmitting range of each mote. Long testing periods (i.e., uninterrupted 12-hour time frames for each period of data collection) add an additional advantage, allowing for the evaluation of the selected wireless sensor network for long-term monitoring using the low power consumption mode under Surge Time Synchronization. Many performance metrics of the adopted small-size, large-interval Mica2 Motes wireless sensor network are revealed in this study through a series of data processing efforts. Results are presented to examine (i) inter-node connectivity and transmitting range, (ii) battery life, (iii) the length of the initial network connection time as affected by methods of setting up tests under practical conditions, (iv) error rate and analysis of different error types (showing the importance of the subsequent data cleansing step), and (v) other network routing properties including the parent time histories for each mote. The results and analysis form a database for future efforts to better understand, appreciate, and improve the performance of Mica2 Motes. This study will thus benefit robust real-world implementation of off-the-shelf sensor network products such as Mica2 Motes in terms of hardware development and data processing.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

ODOT Radar System for Real-Time Traffic Flow Monitoring

ODOT SPR Item Number 2314This report presents research results and experiments that were designed to compare four traffic systems: Radar, AVC, HERE, and INRIX in terms of speed, volume, and travel time. The first three chapters introduce the systems, related studies, data collection procedures, and preprocessing techniques. The second three chapters detail the comparison results. Machine learning models that utilize radar speed data for travel time estimation are introduced in chapter 7. Chapter 8 presents the geospatial and temporal analysis experiment results