Cell-type continuous electromagnetic radiation system generating millimeter waves for active denial system applications

The cell-type continuous electromagnetic radiation system is a demonstration device capable of generating high-power millimeter electromagnetic waves of a specific wavelength and observing their effects on living organisms.It irradiates a biological sample placed in a 30×30×50 cm^(3)cell with electromagnetic waves in the 3.15-mm-wavelength region(with an output of≥1 W)and analyzes the temperature change of the sample.A vacuum electronic device-based coupled-cavity backward-wave oscillator converts the electron energy of the electron beam into radiofrequency(RF)energy and radiates it to the target through an antenna,increasing the temperature through the absorption of RF energy in the skin.The system causes pain and ultimately reduces combat power.A cell-type continuous electromagnetic radiation system consisting of four parts—an electromagnetic-wave generator,a highvoltage power supply,a test cell,and a system controller—generates an RF signal of≥1 W in a continuous waveform at a 95-GHz center frequency,as well as a chemical solution with a dielectric constant similar to that of the skin of a living organism.An increase of 5°C lasting approximately 10 s was confirmed through an experiment.

Plasma Simulation of Dielectric-lined Multiwave Cerenkov Generators Producing High Power Millimeter Waves

This paper presents the concept of a Dielectric-lined Multiwave Cerenkov Generator producing high power millimeter waves, which has been investigated with a two and onehalf dimensional electromagnetic relativistic Particle-in-Cell (PIC) plasma simulation code. Themodified device can operate in a lower diode-voltage regime with much higher radiation efficiencyand slight downshift of operation frequency. There exist the optima for the permittivity of thedielectric liner and for the magnitude of the guiding magnetic field. The required intensity of theguiding field is reduced by the introduction of the liner. The enhanced propagation of the electronbeam is studied in the presence of the liner.

Millimeter waves in medical applications:status and prospects

Millimeter waves are electromagnetic waves with wavelengths of 1–10 mm,which have characteristics of high frequency and short wavelength.They have gradually and widely been used in engineering and medical fields.We have identified studies related to millimeter waves in the biomedical field and summarized the biological effects of millimeter waves and their current status in medical applications.Finally,the shortcomings of existing studies and future developments were analyzed and discussed,with the aim of providing a reference for further research and development of millimeter waves in the medical field.

Modeling and Performance Analysis of UAV-Aided Millimeter Wave Cellular Networks with Stochastic Geometry

UAV-aided cellular networks,millimeter wave(mm-wave) communications and multi-antenna techniques are viewed as promising components of the solution for beyond-5G(B5G) and even 6G communications.By leveraging the power of stochastic geometry,this paper aims at providing an effective framework for modeling and analyzing a UAV-aided heterogeneous cellular network,where the terrestrial base stations(TBSs) and the UAV base stations(UBSs) coexist,and the UBSs are provided with mm-wave and multi-antenna techniques.By modeling the TBSs as a PPP and the UBSs as a Matern hard-core point process of type Ⅱ(MPH-Ⅱ),approximated but accurate analytical results for the average rate of the typical user of both tiers are derived through an approximation method based on the mean interference-to-signal ratio(MISR) gain.The influence of some relevant parameters is discussed in detail,and some insights into the network deployment and optimization are revealed.Numerical results show that some trade-offs are worthy of being considered,such as the antenna array size,the altitude of the UAVs and the power control factor of the UBSs.

Off-Grid Compressed Channel Estimation with Parallel Interference Cancellation for Millimeter Wave Massive MIMO

Millimeter wave(mmWave)massive multiple-input multiple-output(MIMO)plays an important role in the fifth-generation(5G)mobile communications and beyond wireless communication systems owing to its potential of high capacity.However,channel estimation has become very challenging due to the use of massive MIMO antenna array.Fortunately,the mmWave channel has strong sparsity in the spatial angle domain,and the compressed sensing technology can be used to convert the original channel matrix into the sparse matrix of discrete angle grid.Thus the high-dimensional channel matrix estimation is transformed into a sparse recovery problem with greatly reduced computational complexity.However,the path angle in the actual scene appears randomly and is unlikely to be completely located on the quantization angle grid,thus leading to the problem of power leakage.Moreover,multiple paths with the random distribution of angles will bring about serious interpath interference and further deteriorate the performance of channel estimation.To address these off-grid issues,we propose a parallel interference cancellation assisted multi-grid matching pursuit(PIC-MGMP)algorithm in this paper.The proposed algorithm consists of three stages,including coarse estimation,refined estimation,and inter-path cyclic iterative inter-ference cancellation.More specifically,the angular resolution can be improved by locally refining the grid to reduce power leakage,while the inter-path interference is eliminated by parallel interference cancellation(PIC),and the two together improve the estimation accuracy.Simulation results show that compared with the traditional orthogonal matching pursuit(OMP)algorithm,the normalized mean square error(NMSE)of the proposed algorithm decreases by over 14dB in the case of 2 paths.

Optimization-based mode selection scheme for OAM millimeter wave system in the off-axis misalignment case

Millimeter-wave transmission combined with Orbital Angular Momentum(OAM)has the advantage of reducing the loss of beam power and increasing the system capacity.However,to fulfill this advantage,the antennas at the transmitter and receiver must be parallel and coaxial;otherwise,the accuracy of mode detection at the receiver can be seriously influenced.In this paper,we design an OAM millimeter-wave communication system for overcoming the above limitation.Specifically,the first contribution is that the power distribution between different OAM modes and the capacity of the system with different mode sets are analytically derived for performance analysis.The second contribution lies in that a novel mode selection scheme is proposed to reduce the total interference between different modes.Numerical results show that system performance is less affected by the offset when the mode set with smaller modes or larger intervals is selected.

An ISGW Filtering Antenna with Spurious Modes and Surface Wave Suppression for Millimeter Wave Communications

In this paper,an integrated substrate gap waveguide(ISGW)filtering antenna is proposed at millimeter wave band,whose surface wave and spurious modes are simultaneously suppressed.A secondorder filtering response is obtained through a coupling feeding scheme using one uniform impedance resonator(UIR)and two stepped-impedance resonators(SIRs).To increase the stopband width of the antenna,the spurious modes are suppressed by selecting the appropriate sizes of the ISGW unit cell.Furthermore,the ISGW is implemented to improve the radiation performance of the antenna by alleviating the propagation of surface wave.And an equivalent circuit is investigated to reveal the working principle of ISGW.To demonstrate this methodology,an ISGW filtering antenna operating at a center frequency of 25 GHz is designed,fabricated,and measured.The results show that the antenna achieves a stopband width of 1.6f0(center frequency),an out-of-band suppression level of 21 dB,and a peak realized gain of 8.5 dBi.

Cubature Kalman Filter Based Millimeter Wave Beam Tracking for OTFS Systems

In this paper,a Millimeter wave(mmWave)beam tracking problem is studied in orthogonal time frequency space(OTFS)systems.Considering the nonlinearity of beamforming and the constraints of existing Kalman-filtering based beam tracking schemes,we propose a novel Cubature Kalman Filter(CKF)framework tracking the channel state information(CSI)to manage the challenge of highspeed channel variation in single-user moving scene for OTFS systems.Aiming for low complexity for mobile settings,this paper trains only one beam pair to track a path to maintain the reliable communication link in the analog beamforming architecture.Simulation results show that our proposed method has better tracking performance to improve the accuracy of the estimated beam angle compared with prior work.

High-Accuracy NLOS Identification Based on Random Forest and High-Precision Positioning on 60 GHz Millimeter Wave

60 GHz millimeter wave(mmWave)system provides extremely high time resolution and multipath components(MPC)separation and has great potential to achieve high precision in the indoor positioning.However,the ranging data is often contaminated by non-line-of-sight(NLOS)transmission.First,six features of 60GHz mm Wave signal under LOS and NLOS conditions are evaluated.Next,a classifier constructed by random forest(RF)algorithm is used to identify line-of-sight(LOS)or NLOS channel.The identification mechanism has excellent generalization performance and the classification accuracy is over 97%.Finally,based on the identification results,a residual weighted least squares positioning method is proposed.All ranging information including that under NLOS channels is fully utilized,positioning failure caused by insufficient LOS links can be avoided.Compared with the conventional least squares approach,the positioning error of the proposed algorithm is reduced by 49%.

Compressive near-field millimeter wave imaging algorithm based on Gini index and total variation mixed regularization

A compressive near-field millimeter wave(MMW)imaging algorithm is proposed.From the compressed sensing(CS)theory,the compressive near-field MMW imaging process can be considered to reconstruct an image from the under-sampled sparse data.The Gini index(GI)has been founded that it is the only sparsity measure that has all sparsity attributes that are called Robin Hood,Scaling,Rising Tide,Cloning,Bill Gates,and Babies.By combining the total variation(TV)operator,the GI-TV mixed regularization introduced compressive near-field MMW imaging model is proposed.In addition,the corresponding algorithm based on a primal-dual framework is also proposed.Experimental results demonstrate that the proposed GI-TV mixed regularization algorithm has superior convergence and stability performance compared with the widely used l1-TV mixed regularization algorithm.

Non-intrusive soil carbon content quantification methods using machine learning algorithms:A comparison of microwave and millimeter wave radar sensors

Agricultural and forestry biomass can be converted to biochar through pyrolysis gasification,making it a significant carbon source for soil.Applying biochar to soil is a carbon-negative process that helps combat climate change,sustain soil biodiversity,and regulate water cycling.However,quantifying soil carbon content conventionally is time-consuming,labor-intensive,imprecise,and expensive,making it difficult to accurately measure in-field soil carbon’s effect on storage water and nutrients.To address this challenge,this paper for the first time,reports on extensive lab tests demonstrating non-intrusive methods for sensing soil carbon and related smart biochar applications,such as differentiating between biochar types from various biomass feedstock species,monitoring soil moisture,and biochar water retention capacity using portable microwave and millimeter wave sensors,and machine learning.These methods can be scaled up by deploying the sensor in-field on a mobility platform,either ground or aerial.The paper provides details on the materials,methods,machine learning workflow,and results of our investigations.The significance of this work lays the foundation for assessing carbon-negative technology applications,such as soil carbon content accounting.We validated our quantification method using supervised machine learning algorithms by collecting real soil mixed with known biochar contents in the field.The results show that the millimeter wave sensor achieves high sensing accuracy(up to 100%)with proper classifiers selected and outperforms the microwave sensor by approximately 10%–15%accuracy in sensing soil carbon content.

Artificial Intelligence Based Multi-Scenario mmWave Channel Modeling for Intelligent High-Speed Train Communications

A large amount of mobile data from growing high-speed train(HST)users makes intelligent HST communications enter the era of big data.The corresponding artificial intelligence(AI)based HST channel modeling becomes a trend.This paper provides AI based channel characteristic prediction and scenario classification model for millimeter wave(mmWave)HST communications.Firstly,the ray tracing method verified by measurement data is applied to reconstruct four representative HST scenarios.By setting the positions of transmitter(Tx),receiver(Rx),and other parameters,the multi-scenarios wireless channel big data is acquired.Then,based on the obtained channel database,radial basis function neural network(RBF-NN)and back propagation neural network(BP-NN)are trained for channel characteristic prediction and scenario classification.Finally,the channel characteristic prediction and scenario classification capabilities of the network are evaluated by calculating the root mean square error(RMSE).The results show that RBF-NN can generally achieve better performance than BP-NN,and is more applicable to prediction of HST scenarios.

Resource Allocation for IRS Assistedmm Wave Wireless Powered Sensor Networks with User Cooperation

In this paper,we investigate IRS-aided user cooperation(UC)scheme in millimeter wave(mmWave)wirelesspowered sensor networks(WPSN),where two single-antenna users are wireless powered in the wireless energy transfer(WET)phase first and then cooperatively transmit information to a hybrid access point(AP)in the wireless information transmission(WIT)phase,following which the IRS is deployed to enhance the system performance of theWET andWIT.We maximized the weighted sum-rate problem by jointly optimizing the transmit time slots,power allocations,and the phase shifts of the IRS.Due to the non-convexity of the original problem,a semidefinite programming relaxation-based approach is proposed to convert the formulated problem to a convex optimization framework,which can obtain the optimal global solution.Simulation results demonstrate that the weighted sum throughput of the proposed UC scheme outperforms the non-UC scheme whether equipped with IRS or not.

Intelligent Reflecting Surface Assisted Transmission Optimization Strategies in Wireless Networks

Wireless Power Transfer(WPT)technology can provide real-time power for many terminal devices in Internet of Things(IoT)through millimeterWave(mmWave)to support applications with large capacity and low latency.Although the intelligent reflecting surface(IRS)can be adopted to create effective virtual links to address the mmWave blockage problem,the conventional solutions only adopt IRS in the downlink from the Base Station(BS)to the users to enhance the received signal strength.In practice,the reflection of IRS is also applicable to the uplink to improve the spectral efficiency.It is a challenging to jointly optimize IRS beamforming and system resource allocation for wireless energy acquisition and information transmission.In this paper,we first design a Low-Energy Adaptive Clustering Hierarchy(LEACH)clustering protocol for clustering and data collection.Then,the problem of maximizing the minimum system spectral efficiency is constructed by jointly optimizing the transmit power of sensor devices,the uplink and downlink transmission times,the active beamforming at the BS,and the IRS dynamic beamforming.To solve this non-convex optimization problem,we propose an alternating optimization(AO)-based joint solution algorithm.Simulation results show that the use of IRS dynamic beamforming can significantly improve the spectral efficiency of the system,and ensure the reliability of equipment communication and the sustainability of energy supply under NLOS link.

Study on the Technique of Passive Millimeter Wave Detector

In order to initiate the flight immediately when it reaches the top of the pedrail vehicle, technical parameters of radiometer have been designed and speedy effective signal processing method has been adopted. After analyzing the difference of signal characteristic between the main jam and the target, a method of identifying target in time domain is given. The target distinguishing rules are set up by extracting the magnitude, the slope and the width of the signal, combining with distinguishing the dimension of the target. The result of the theoretic analysis shows that the detecting scheme adopted can ensure the detector to identify and orientate the pedrail vehi cle＇s top armour, as well as control the detonation precisely.

Modeling Human Blockers in Millimeter Wave Radio Links

In this paper, we investigate the loss caused by multiple humans blocking millimeter wave frequencies. We model human blockers as absorbing screens of infinite height with two knife-edges, We take a physical optics approach to computing the diffraction around the absorbing screens, This approach differs to the geometric optics approach described in much of the literature. The blocking model is validated by measuring the gain from multiple-human blocking configurations on an indoor link. The blocking gains predicted using Piazzi ＇ s numerical integration method （a physical optics method） agree well with measurements taken from approximately 2.7 dB to -50 dB. Thereofre, this model is suitable for real human blockers, The mean prediction error for the method is approximately -1.2 dB, and the standard deviation is approximately 5 dB.

Super-resolution processing of passive millimeter-wave images based on adaptive projected Landweber algorithm

Passive millimeter wave （PMMW） images inherently have the problem of poor resolution owing to limited aperture dimension. Thus, efficient post-processing is necessary to achieve resolution improvement. An adaptive projected Landweber （APL） super-resolution algorithm using a spectral correction procedure, which attempts to combine the strong points of all of the projected Landweber （PL） iteration and the adaptive relaxation parameter adjustment and the spectral correction method, is proposed. In the algorithm, the PL iterations are implemented as the main image restoration scheme and a spectral correction method is included in which the calculated spectrum within the passband is replaced by the known low frequency component. Then, the algorithm updates the relaxation parameter adaptively at each iteration. A qualitative evaluation of this algorithm is performed with simulated data as well as actual radiometer image captured by 91.5 GHz mechanically scanned radiometer. From experiments, it is found that the super-resolution algorithm obtains better results and enhances the resolution and has lower mean square error （MSE）. These constraints and adaptive character and spectral correction procedures speed up the convergence of the Landweber algorithm and reduce the ringing effects that are caused by regularizing the image restoration problem.

Super-resolution processing of passive millimeter-wave images based on conjugate-gradient algorithm

This paper designs a 3 mm radiometer and validate with experiments based on the principle of passive millimeter wave （PMMW） imaging. The poor spatial resolution, which is limited by antenna size, should be improved by post data processing. A conjugate-gradient （CG） algorithm is adopted to circumvent this drawback. Simulation and real data collected in laboratory environment are given, and the results show that the CG algorithm improves the spatial resolution and convergent rate. Further, it can reduce the ringing effects which are caused by regularizing the image restoration. Thus, the CG algorithm is easily implemented for PMMW imaging.

Ultra-Wideband Annular Ring Fed Rectangular Dielectric Resonator Antenna for Millimeter Wave 5G Applications

In this article an ultra-wideband rectangular Dielectric Resonator Antenna is designed for millimeter wave 5G frequency band applications.Indoor 5G communications require antenna system with wide bandwidth and high efficiency to enhance the throughput in the channel.To fulfill such requirements a Dielectric Resonator Antenna(DRA)is designed here which has achieved an ultra-wide bandwidth of 20.15%(22.32–27.56 GHz)which is 5.24 GHz of bandwidth centered at 26 GHz as resonating frequency.This covers the complete band 30(24.3–27.5 GHz)of 5G spectrum.26 and 28 GHz are considered as most popular frequencies in millimeter wave 5G communications.The aperture fed DRA designed here has also achieved an efficiency of 96 percentage with maximum radiation in the broadside direction(Phi=0,Theta=0).The measured gain of the DRA is 6.3 dB.The DRA designed here has dimensions of 0.25λ0×0.22λ0×0.12λ0.under the characteristic’s mode.The DRA is placed over a substrate with dimensions 0.5λ0×0.5λ0×0.02λ0.A cross slot aperture has been made on the ground plane which is placed above to the substrate.Here a full ground plane is used to resonate the antenna and is of similar dimension to the substrate.A microstrip line with two concentric rings makes an annular feed structure is used to excite the DRA and is placed below the substrate.The DRA is excited here in characteristics mode TE1Y1 and is the only mode of excitation.The DRA is linearly polarized,and the characteristic mode of excitation is maintained with 50 Ohm input impedance of the antenna.The DRA also gives here a good difference between the co-pol and cross pol approximately 15 to 20 dB.This antenna is more suitable for 5G indoor applications in millimeter wave frequency band centered at 26 GHz.

Millimeter-Wave Concurrent Beamforming:A Multi-Player Multi-Armed Bandit Approach

The communication in the Millimeter-wave(mmWave)band,i.e.,30~300 GHz,is characterized by short-range transmissions and the use of antenna beamforming(BF).Thus,multiple mmWave access points(APs)should be installed to fully cover a target environment with gigabits per second(Gbps)connectivity.However,inter-beam interference prevents maximizing the sum rates of the established concurrent links.In this paper,a reinforcement learning(RL)approach is proposed for enabling mmWave concurrent transmissions by finding out beam directions that maximize the long-term average sum rates of the concurrent links.Specifically,the problem is formulated as a multiplayer multiarmed bandit(MAB),where mmWave APs act as the players aiming to maximize their achievable rewards,i.e.,data rates,and the arms to play are the available beam directions.In this setup,a selfish concurrent multiplayer MAB strategy is advocated.Four different MAB algorithms,namely,ϵ-greedy,upper confidence bound(UCB),Thompson sampling(TS),and exponential weight algorithm for exploration and exploitation(EXP3)are examined by employing them in each AP to selfishly enhance its beam selection based only on its previous observations.After a few rounds of interactions,mmWave APs learn how to select concurrent beams that enhance the overall system performance.The proposed MAB based mmWave concurrent BF shows comparable performance to the optimal solution.