Radars with mmWave frequency modulated continuous wave (FMCW) technology accurately estimate the range and velocity of targets in their field of view (FoV). The targeted angle of arrival (AoA) estimation can be improved by increasing receiving antennas or by using multiple-input multiple-output (MIMO). However, obtaining target features such as target type remains challenging. In this paper, we present a novel target classification method based on machine learning and features extracted from a range fast Fourier transform (FFT) profile by using mmWave FMCW radars operating in the frequency range of 77–81 GHz. The measurements are carried out in a variety of realistic situations, including pedestrian, automotive, and unmanned aerial vehicle (UAV) (also known as drone). Peak, width, area, variance, and range are collected from range FFT profile peaks and fed into a machine learning model. In order to evaluate the performance, various light weight classification machine learning models such as logistic regression, Naive Bayes, support vector machine (SVM), and lightweight gradient boosting machine (GBM) are used. We demonstrate our findings by using outdoor measurements and achieve a classification accuracy of 95.6% by using LightGBM. The proposed method will be extremely useful in a wide range of applications, including cost-effective and dependable ground station traffic management and control systems for autonomous operations, and advanced driver-assistance systems (ADAS). The presented classification technique extends the potential of mmWave FMCW radar beyond the detection of range, velocity, and AoA to classification. mmWave FMCW radars will be more robust in computer vision, visual perception, and fully autonomous ground control and traffic management cyber-physical systems as a result of the added new feature.
This paper provides an overview of the most recent passive radars based on long-term evolution (LTE). To begin, this paper investigates the various characteristics and requirements of 4 G LTE signals for radar, taking performance aspects such as range, velocity, range resolution, and velocity resolution into account. An ambiguity function analysis is performed on a measured LTE signal using the synchronization and reference signal components to evaluate key performance parameters such as Doppler and range characteristics. We also discuss how LTE passive radar can be used in a variety of applications. The detailed analysis of the LTE downlink signal, its structural overview, and the effect on cross- and self-ambiguity functions are all discussed. The paper investigates related standard development proposals, with a focus on performance evaluation criteria for existing passive radar technologies. As a result, this survey paper serves as a starting point for evaluating the performance of current and future passive radar innovations, including an emerging 5 G radar.
Wi-Fi based indoor localization has gained much attention around the globe due to its widespread reach and availability. Amongst several possible approaches using Wi-Fi signals, fingerprint image-based approach has become popular due to its low hardware requirements. Further, this approach can be used alone or along with other positioning systems for indoor localization. However, a multi-building, multi-floor indoor positioning system with high localization accuracy is required. Motivated by this, we propose a Convolutional Neural Networks (CNN)-based approach. For feature extraction and classification, a multi-output multi-label sequential 2D-CNN classifier is developed and implemented. The system is able to predict the location of the user by combining the classification output from the multi-output model. This approach is verified on the publicly available UJIIndoorLoc database. The system offers an average accuracy of 97% in indoor localization.
Machine type communications (MTC) is one of the key technologies in the upcoming 5G cellular networks. Under MTC, millions of MTC devices try to access the cellular base station. The existing random access channel (RACH) mechanisms are not suitable for such large number of devices. Thus, 3GPP has proposed an extended access barring (EAB) mechanism to address this problem. However, it has been shown in the literature that the RACH success rate of EAB can be improved further. Hence, in this work, we propose a novel RACH mechanism based on successive interference cancellation (SIC) that results in higher RACH success rate. Through extensive numerical results, we show that the proposed mechanism significantly outperforms the existing RACH mechanisms for MTC in cellular networks.
Due to proliferation of mobile devices, the demand for video in cellular networks has increased exorbitantly. However, cellular networks have limited resources and the wireless medium is time-varying in nature. This necessitates the video streaming protocols to be re-designed taking into account the overall quality of experience (QoE) of the end users. In this paper, we propose a metric called enhanced-time varying subjective quality (eTVSQ) to measure the QoE of the video users. The eTVSQ accounts for time variation in QoE due to both rate adaption in HTTP streaming and playback interruption caused by rebuffering events. Based on this metric, we propose a rate adaptation strategy for HTTP video streaming in the downlink of cellular networks with α-fair resource allocation. The proposed method results in significant performance gains over the traditional throughput based rate adaptation strategy.
In this paper, we present a novel multiclass-target classification method for mmWave frequency modulated continuous wave (FMCW) radar operating in the frequency range of 77 - 81 GHz, based on custom range-angle heatmaps and machine learning tools. The elevation field of view (FoV) is increased by orienting the Radar antennas in elevation. In this orientation, the radar focuses the beam in elevation to improve the elevation FoV. The azimuth FoV is improved by mechanically rotating the Radar horizontally, which has antenna elements oriented in the elevation direction. The data from the Radar measurements obtained by mechanical rotation of the Radar in Azimuth are used to generate a range-angle heatmap. The measurements are taken in a variety of real-world scenarios with various objects such as humans, a car, and an unmanned aerial vehicle (UAV), also known as a drone. The proposed technique achieves accuracy of 97.6 % and 99.6 % for classifying the UAV and humans, respectively, and accuracy of 98.1 % for classifying the car from the range-angle FoV heatmap. Such a Radar classification technique will be extremely useful for a wide range of applications in cost-effective and dependable autonomous systems, including ground station traffic monitoring and surveillance, as well as control systems for both on-ground and aerial vehicles.
Domain Generalization (DG) techniques have emerged as a popular approach to address the challenges of domain shift in Deep Learning (DL), with the goal of generalizing well to the target domain unseen during the training. In recent years, numerous methods have been proposed to address the DG setting, among which one popular approach is the adversarial learning-based methodology. The main idea behind adversarial DG methods is to learn domain-invariant features by minimizing a discrepancy metric. However, most adversarial DG methods use 0-1 loss based $\mathcal{H}\Delta\mathcal{H}$ divergence metric. In contrast, the margin loss-based discrepancy metric has the following advantages: more informative, tighter, practical, and efficiently optimizable. To mitigate this gap, this work proposes a novel adversarial learning DG algorithm, MADG, motivated by a margin loss-based discrepancy metric. The proposed MADG model learns domain-invariant features across all source domains and uses adversarial training to generalize well to the unseen target domain. We also provide a theoretical analysis of the proposed MADG model based on the unseen target error bound. Specifically, we construct the link between the source and unseen domains in the real-valued hypothesis space and derive the generalization bound using margin loss and Rademacher complexity. We extensively experiment with the MADG model on popular real-world DG datasets, VLCS, PACS, OfficeHome, DomainNet, and TerraIncognita. We evaluate the proposed algorithm on DomainBed's benchmark and observe consistent performance across all the datasets.
In this article, we present a novel angle and height estimation technique for aerial vehicles using mmWave frequency modulated continuous wave (FMCW) Radar. In the proposed method, Radar's antennas are oriented vertically to measure the elevation angle of arrival of the aerial vehicle from ground station. Height of the aerial vehicle and horizontal distance of the aerial vehicle from Radar station on ground are estimated using the measured radial range and the elevation angle of arrival.
Base station sleeping (BSS) can result in significant reduction in energy consumption of cellular networks during low traffic conditions. We show that the coverage loss due to BSS can be compensated via coordinated multi-point (CoMP) -based transmission in a cluster of base stations. For a BSS with CoMP-based system, we propose various BSS patterns to achieve suitable trade-offs between energy savings and throughput. We formulate the CoMP resource allocation and α-Fair user scheduling as a joint optimization problem. We derive the optimal time fraction and user scheduling for this problem and use it to formulate a simplified BSS with CoMP optimization problem. A heuristic that solves this problem is presented. Through extensive simulations, we show that suitable trade-offs among energy, coverage, and rate can be achieved by appropriately selecting the BSS pattern, CoMP cluster, and rate threshold.
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