mmWave Technology Research Articles

Sub-6 GHz V2X-Assisted Synchronous Millimeter Wave Scheduler for Vehicle-to-Vehicle Communications

As millimeter wave (mmWave) technology becomes more integrated into today's communication infrastructure, its application to mobile scenarios has attracted considerable interests. In particular, mmWave technology is one of the leading candidates in Vehicle-to-Everything (V2X) community to address the ever-rising data exchange demand from vehicles. However, the inherent directionality of the technology poses challenges to peer discovery and transmission scheduling in typical V2X scenarios, causing significant loss of network capacity. To mitigate this issue, we propose a novel sub-6 GHz V2X-assisted synchronous mmWave communication scheduler in this paper. The sub-6 GHz channel serves as control channel for mmWave scheduling. The scheduler carefully considers data importance and freshness in determining communication schedule, such that network utility across mmWave links is maximized. We formulate the scheduling problem into an integer linear programming (ILP) problem. However, ILP problem is NP-hard and has exponential computational complexity for the worst cases. We then develop a scheduling algorithm, maximum weight matching based iterative algorithm (MWMIA), that operates in polynomial time with performance guarantees for the NP-hard ILP, by leveraging structural properties of link conflicts. Comparative simulation studies verify the effectiveness of our scheduler against other solutions.

Location Verification for Future Wireless Vehicular Networks: Research Directions and Challenges

Vehicle location information obtained through the global navigation satellite system (GNSS) will play a pivotal role in emerging vehicular networks. This vital information is, however, susceptible to a host of unwanted manipulations, especially if a malicious entity is involved. The most obvious example of such manipulations is the forwarding by a malicious vehicle of false GNSS locations to other members of the network. Such events can lead to poor operational outcomes for the vehicular network, and in extreme cases even lead to catastrophic safety violations. Here, we highlight research efforts pursued in the past few years that have attempted to address this weakness in vehicular networks. We also discuss the importance of location verification in the wake of emerging wireless technologies, such as those being proposed for beyond 5G (B5G) wireless vehicular networks. In particular, we detail an opportunity to conduct location reporting and verification simultaneously with the aid of mmWave technology and discuss how emerging machine learning (ML) techniques will provide for location verification solutions where reliability levels will be commensurate with that required by the vehicular network paradigm. We close by discussing the potential enhancements for location verification within a future combined B5G-ML architecture.

Precision frequency transfer with fiber frequency combs

We review methods for precision transfer of frequencies across broad optical wavelength ranges. Single-branch supercontinuum generation allows for a frequency transfer stability of < 1 × 10−17in 1 s across an octave. With supercontinuum stitching, highly coherent supercontinuum spectra spanning across more than two octaves are generated. With noise cancellation techniques a relative frequency transfer stability of ≈ 2 × 10−18in 1 s can be achieved. Highly stable frequency transfer along with a maximization of power per mode at multiple freely selectable frequency bands is further enabledviapulse shaping techniques. We also include a brief review of general fiber combs and research aimed at frequency extension of frequency combs covering the whole spectral range from the XUV to the mid IR, power scaling of frequency combs as well as low noise microwave and mmwave technology enabled with frequency combs.

Hybrid Precoder Design Using Alternating Minimization Algorithm in MultiCell Multi-User Millimeter-Wave Communications for Massive MIMO

In the field of upcoming generation in wireless communication technology there is an extreme demand for higher bandwidth which is expected to be fulfilled by the frequency spectrum ranging from 30GHz to 300GHz which in other words can be defined as millimeter wave (mmWave). But it is a fact that, the mmWave technology lacks the simplicity in the design of precoder. There is higher power loss and higher cost in the baseband precoding in the case of fully digital baseband precoder. As the supported number of analog-to-digital (ADC) converters is very few, the mmWave technology requires ‘Hybrid Precoder’ which may reduce the loss of extensive power and lessen cost. The cost-effective hybrid precoder transceiver formulation has not been standardized yet. In this paper, we proposed the design of an optimal hybrid precoder in fully connected structure which has a higher complexity but lower cost and lower power consumption in the case of multi-cell multi-user massive MIMO. We have used Alternating Minimization Algorithm for getting the optimal Reimannian manifold solution. Here the comparison between the Digital Baseband precoder performance and the Hybrid Precoder performance has been done using simulation tools.

Intelligent Offloading Decision and Resource Allocations Schemes Based on RNN/DQN for Reliability Assurance in Software-Defined Massive Machine-Type Communications

The heterogeneous novelty applications present in the 5th generation (5G) era, including machine-type communication (mMTC), enhanced mobile broadband (eMBB) communication, and ultra-reliable low latency communication (URLLC), which required mobile edge computing (MEC) for local computation and services. The next-generation radio networking (NGRN) will rely on new radio (NR) with the millimeter-wavelength (mmWave) technologies that enable ultra-dense connectivities of the deployed heterogeneous mobile terminal gateways (MTG). However, the mission-critical mMTC applications will suffer from inadequate radio resource management and orchestration (MANO), which will diminish end-to-end (E2E) communication reliability in edge areas. This paper proposed optimal MTG selections and resource allocation (RA) based on the complementary between MTG loading prediction based on recurrent neural network-based long short-term memory (RNN-LSTM) and MTG loading adjustment based on the applied deep reinforcement learning (DRL) approaches, respectively. Furthermore, the RNN-LSTM enhances offloading and handover decisions with discrete-time predictions, while the DRL plays an essential role in adjusting the determined MTG during congestion situations. The proposed method contributed to remarkable outcomes in crucial performance metrics over reference approaches regarding computation and communication quality of service (QoS).

A Low-Power High-Accuracy Urban Waterlogging Depth Sensor Based on Millimeter-Wave FMCW Radar.

The method of making precise measurements of remote water depth using mmWave technology has great potential for preventing urban waterlogging. To achieve waterlogging prevention, the mmWave system needs to measure the water depth change accurately with a short acquisition time. This paper demonstrates a new accurate mmWave water depth measurement system based on Frequency Modulated Continuous Wave (FMCW) Radar with a center frequency of 77 GHz. To improve distance resolution and lower acquisition time, the Swept Frequency-Cross Correlation (SFCC) algorithm is proposed for the first time to improve the distance computation resolution by 9× and lower time complexity from to compared to traditional FFT-based FMCW radar distance computation. A prototype system equipped with a humidity sensor, a processor module and TI’s FMCW radar module is designed for monitoring urban floods in cities. Using the prototype system with the proposed SFCC, the depth measurement error is reduced from 4.5 cm to less than 5 mm, compared to the default radar post-processing algorithm embedded in the radar module.

Green traffic backhauling in next generation wireless communication networks incorporating FSO/mmWave technologies

Free-space optics (FSO) and Millimeter wave (mmWave) technologies are envisioned to be a major part of next generation wireless backhauling solutions. The FSO technology offers a fiber-like high data rate and low deployment cost, but suffers from reliability and availability problems caused by adverse weather conditions. On the other hand, the mmWave technology offers a more reliable backhauling solution, but is limited in communication range and throughput compared to the FSO technology. While both of these technologies have been investigated in recent times, none of the existing research explored the energy efficiency aspect of the hybrid FSO/mmWave backhauling solution. This research investigates the green backhauling challenge of the hybrid FSO/mmWave backhauling system. We derive two analytical models, one for FSO links and the other for mmWave links, to measure the backhaul power consumption in 5G systems. We then formulate the green backhauling challenge as a mixed integer linear programming, which considers network traffic variation and weather condition (e.g., fog, rain, and wind) and finds the optimum backhauling path. Simulation results confirm that the proposed green backhauling solution consumes 27% less energy compared to existing solutions.

Glass for 5G applications

The present drive for higher bandwidth has moved the mobile carrier frequency range to >5 GHz and possibly to >20 GHz in the near future. This has fueled the rapid development of 5 G and mm-wave technologies. The increased bandwidths available in the mm-wave frequency range allow for applications that require high-data-rate communications, which is not possible at lower frequencies due to Shannon's Capacity Theorem. However, the mm-wave frequency also presents a challenge to device performance due to the limitation on material properties. Many incumbent materials, such as silicon, ceramic, and polymer based substrates, suffer from high dielectric loss, rough surface, or poor durability to process chemistry. Silicate glass, on the other hand, has been shown to exhibit low dielectric loss, smooth surface, and high resistance to process chemistry. In addition, modern manufacturing technology has enabled silicate glass to be made with large size and thin form factor, which provides a clear advantage to lowering the cost. While many attributes of glass may be already familiar to the general scientific community, dielectric properties in the mm-wave frequency range have not been extensively reviewed. In this report, we show that mm-wave dielectric property can be changed by glass composition and post-forming processes. We also show examples of mm-wave devices that can be made with glass.

Spatio-Temporally Modulated Metamaterials

In this talk, we discuss the opportunities and potentials of metamaterials in which their constituent elements are modulated in space and time with tailored patterns, or abruptly change in space-time, in order to overcome the constraints of passive, linear, time-invariant materials. We will discuss nonreciprocal and parametric phenomena enabled by these spatiotemporally modulated metamaterials, and emphasize the challenges in efficiently modeling their responses with analytical and numerical techniques. We will also survey several applications of this platform from radio-wave and mm-wave technology to classical and quantum photonics.

Source Coding Based Millimeter-Wave Channel Estimation With Deep Learning Based Decoding

The speed at which millimeter-Wave (mmWave) channel estimation can be carried out is critical for the adoption of mmWave technologies. This is particularly crucial because mmWave transceivers are equipped with large antenna arrays to combat severe path losses, which consequently creates large channel matrices, whose estimation may incur significant overhead. This paper focuses on the mmWave channel estimation problem. Our objective is to reduce the number of measurements required to reliably estimate the channel. Specifically, channel estimation is posed as a “source compression” problem in which measurements mimic an encoded (compressed) version of the channel. Decoding the observed measurements, a task which is traditionally computationally intensive, is performed using a deep-learning-based approach, facilitating a high-performance channel discovery. Our solution not only outperforms state-of-the-art compressed sensing methods, but it also determines the lower bound on the number of measurements required for reliable channel discovery.

Real-Time Macro Gesture Recognition Using Efficient Empirical Feature Extraction With Millimeter-Wave Technology

Human Machine Interaction based on air gestures finds an increasing number of applications in consumer electronics. The availability of mmWave technology, combined with machine learning, allows the detection and classification of gestures, avoiding high-resolution LIDAR or video sensors. Nevertheless, in most of the existing studies, the processing takes place offline, takes into account only the velocity and distance of the moving arm, and can handle only gestures that are conducted very close to the sensor device, which limits the range of possible applications. Here, we use an experimental multi-channel mmWave-based system that can detect small targets, like a moving arm, up to a few meters away from the sensor. As our pipeline can estimate and take into account the angle of arrival in azimuth and elevation, it has the ability to classify a greater variety of dynamic gestures. Furthermore, the digital signal processing chain we present here, runs in real-time, incorporating an event detector. Whenever an event is detected, a novel empirical feature extraction takes place and a Multi-Layer Perceptron is deployed to infer the type of the gesture. To evaluate our setup and signal processing pipeline, a dataset with ten subjects, performing nine gestures was recorded. Our method yielded 94.3% accuracy on the test set, indicating a successful combination of our proposed sensor and signal processing pipeline for real time applications.

Cooperative Relaying for Multi-User MIMO Wireless Backhaul Networks

With the increasing deployment of more short-range small cells using mmWave technologies, backhaul becomes a bottleneck and wireless backhaul is relatively attractive compared to wired backhaul due to lower cost and efficient operation. As such, cooperative relaying for multi-user multiple-input multiple-output (MU-MIMO) wireless backhaul networks is studied, in which the source sends independent messages to $K$ destinations with the help of $N$ relays where the relays operate in full- or half-duplex mode. Herein, we focus on a line-of-sight dominant environment in which each of the channel matrices between nodes becomes rank-deficient due to the lack of multi-paths. In such cases, the multiplexing gain from using multiple antennas is severely limited, and the capacity cannot be improved with the number of antennas. To overcome the rank deficiency, we develop a novel cooperative relaying scheme that utilizes relays as active reflectors. In the proposed scheme, the source distributes the streams over the relays as uniformly as possible, and the relays extract the streams of each destination based on their received signals and forward them to their corresponding destinations, while any inter-user and inter-relay interference is also properly nulled out. We analyze the achievable sum degrees of freedoms (DoFs) and sum rates with full-duplex and half-duplex relays, and derive an upper bound on the sum DoF, which is tight when the relays operate in full-duplex mode. The results demonstrate that our scheme offers significant improvement to system throughput in comparison with direct transmission without cooperative relaying.

MmWave technology joins the fight against viral outbreaks

Expanding the capabilities of MMW technology is helping to provide insight into the mechanics of viral transfer, offering potential treatments for COVID-19 and similar viruses, as New Electronics discovers

Development of Polycrystalline Diamond Compatible with the Latest N-Polar GaN mm-Wave Technology

Integration of diamond on GaN can ease the challenges associated with thermal management of GaN-based power amplifiers which need to base on highly scaled transistors to push toward higher frequencies at high powers for 5G networks. The integration of diamond was achieved by growing polycrystalline (PC) diamond on nitrogen-polar GaN. A standard 5 nm Si3N4 layer which forms the gate dielectric was used as an interlayer between diamond and the GaN channel for etching protection. Since diamond growth conditions involve high temperature and H2 plasma, it can easily decompose the underlying dielectric as well as the GaN channel and degrade the channel conductivity and hence the device performance. Due to the incompatibility of conventional growth recipes with thin dielectrics (<5 nm), a novel two-stage-three-step growth recipe was designed for PC diamond integration on top of nitrogen-polar GaN high-electron-mobility transistors in a H2/CH4-plasma environment. Using only H2 and CH4 in the chamber guarantees a higher-phase-purity diamond than chambers with added argon or nitrogen for lower substrate etching. This recipe maintains the performance of the two-dimensional-electron gas and provides a less columnar diamond structure with a larger grain size. Our observations were supported by scanning transmission electron microscopy and Hall mobility measurements using the van der Pauw technique before and after diamond growth. A mobility of ∼1250 cm2/V s, a sheet carrier concentration of ∼1.30 × 1013 cm–2, and a sheet resistivity of ∼380 Ω/□ were maintained after the growth of diamond. The anisotropy ratio has been decreased from 3.75 to 1.12 with this growth recipe. Using long channel devices, we measured the difference in the channel temperature, which decreased by more than 100 °C in the range of 10–24 W/mm power after the integration of the diamond on top of the device.

A comprehensive review of Millimeter wave based radio over fiber for 5G front haul transmissions

Objectives: To find out how fiber distribution system could be utilized for 5G based futuristic higher capacity and lower latency front haul transmission system. Findings: Integrating the transmission of Millimeter wave (Mm wave) signals over the Radio over Fiber (RoF) system i.e. Mm-RoF can be seen as a promising candidate that would satisfy the requirement imposed by 5G wireless system. Further, optical generation of Mm wave signals is a major concern that needs to be taken care of and some appropriate hybrid photonic generation methods should be employed for Mm wave signal distribution over the RoF system that incur lower installation cost and higher transmission performance. Applications: This will enrich the researchers with valuable content on single platform and motivate them to undertake the research work towards the advancement in the photonic generation of Mm wave signals over the RoF network for 5G applications with reduced system cost and complexity. Keywords: Radio over Fiber; Mm-wave technology; 5G networks; optical signal generation; Mm wave based RoF etc

Synthetic Radar Dataset Generator for Macro-Gesture Recognition

Recent developments in mmWave technology allow the detection and classification of dynamic arm gestures. However, achieving a high accuracy and generalization requires a lot of samples for the training of a machine learning model. Furthermore, in order to capture variability in the gesture class, the participation of many subjects and the conduct of many gestures with different arm speed are required. In case of macro-gestures, the position of the subject must also vary inside the field of view of the device. This would require a significant amount of time and effort, which needs to be repeated in case that the sensor hardware or the modulation parameters are modified. In order to reduce the required manual effort, here we developed a synthetic data generator that is capable of simulating seven arm gestures by utilizing Blender, an open-source 3D creation suite. We used it to generate 600 artificial samples with varying speed of execution and relative position of the simulated subject, and used them to train a machine learning model. We tested the model using a real dataset recorded from ten subjects, using an experimental sensor. The test set yielded 84.2% accuracy, indicating that synthetic data generation can significantly contribute in the pre-training of a model.

Implications of mmWave Radiation on Human Health: State of the Art Threshold Levels

millimeter (mmWave) frequencies are covering from 30GHz to 300GHz in the electromagnetic spectrum and their uses in various applications like next-generation wireless communication systems (massive 5G telecommunications network), medical devices, airport security and automatic collision avoidance systems are growing vastly in the near future. Therefore, it is important to study the effects of mmWave radiation (non-ionization radiation) on biological systems and biophysical mechanisms. This paper focus on thorough review of nascent literature about current understandings of biological effects and epidemiological studies due to mmWave radiation in human beings. It presents latest guidelines with quantitative electromagnetic field thresholds by considering the realistic exposure scenarios of “general public” and “occupational” who undergo through wireless communication sources in their daily life. It also gives necessary safety measures to be taken while using the emerging mmWave technologies for future generation wireless communication networks.

Comparative Analysis of V-Band and E-Band mmWaves for Green Backhaul Solutions for 5G Ultra-Dense Networks

5G Ultra-Dense Networks (UDNs) will involve massive deployment of small cells which in turn form complex backhaul network. This backhaul network must be energy efficient for the 5G UDN network to be green. V-band and E-band mmWave technologies are among the wireless backhaul solutions tipped for 5G UDN. In this paper, we have compared the performance of the two backhaul solutions to determine which is more energy efficient for 5G UDN. We first formulated the problem to minimize power, then proposed an algorithm to solve the problem. This was then simulated using Network simulator 3.The first scenario made use of V-band mmWave while thesecond was E-band mmWave. The performance metricsused were power consumption and energy efficiency againstthe normalized hourly traffic profile. The performances ofthe two solutions were compared. The results revealed thatE-band mmWave outperformed V-band mmWave inbackhauling traffic in 5G UDN. It can be concluded that E-band green backhaul solution is recommended over V-bandmmWave for 5G UDN.

Deep Learning-Based Drone Classification Using Radar Cross Section Signatures at mmWave Frequencies

This paper presents drone classification at millimeter-wave (mmWave) radars using the deep learning (DL) technique. The adoption mmWave technology in radar systems enables better resolution and aid in detecting smaller drones. Using radar cross-section (RCS) signature enables us to detect malicious drones and suitable action can be taken by respective authorities. Existing drone classification converts the RCS signature into images and then performs drone classification using a convolution neural network (CNN). Converting every signature into an image induces additional computation overhead; further CNN model is trained considering fixed learning rate. Thus, when using CNN-based drone classification under a highly dynamic environment exhibit poor classification accuracy. This paper present an im- proved long short-term memory (LSTM) by introducing a weight optimization model that can reduce computation overhead by not allowing the gradient to not flow through hidden states of the LSTM model. Further, present adaptive learning rate optimizing (ALRO) model for training the LSTM model. Experiment outcome shows LSTM-ALRO achieves much better drone detection accuracies when compared with the existing CNN-based drone classification model.

AI-empowered millimeter wave communication and networking

Thanks to the abundant and attractive spectrum resource in the millimeter-wave (mmWave) band, mmWave technologies have been widely applied to the fifth-generation (5G) mobile communication system and will continuously evolve towards the future beyond 5G systems as well. However, achieving the pervasive application of mmWave for 5G and beyond still faces many challenges, for example, the severe path-loss and vulnerability in blockage of mmWave propagation may have a great impact on mmWave communication and networking. In addition, the dense deployment of mmWave access points (APs) might be necessary to support a mmWave network with good coverage performance, which however involves very high capital and operation expenditures. Moreover, the highly directional narrow beam transmission may also complicate the mmWave link establishment and maintenance procedure, especially in the mobile scenario. To this end, efficient and cost-effective solutions are urgently called for to address the new challenges in mmWave communication and networking.