Integrating unmanned aerial vehicles (UAVs) as aerial base stations (BSs) into terrestrial cellular networks has emerged as an effective solution to provide coverage and complement communication services in a fast and cost-effective manner. The three-dimensional (3-D) trajectories of UAVs have a remarkable impact on the performance of such networks. On the other hand, UAVs are battery limited, and thus optimizing their energy consumption is of high importance. In this regard, we propose a novel trajectory design mechanism for rotary-wing UAV-BSs in 3-D space to improve the energy efficiency of the network. In this approach, UAVs aim at maximizing an objective function that captures the tradeoff between energy consumption and throughput, while satisfying their ground users’ quality-of-service requirements. Using reinforcement learning, we model our problem as a multiarmed bandit and propose an upper confidence bound-based algorithm to solve the problem. In our proposed mechanism, UAVs autonomously choose their velocities and update their locations adapted to the system conditions without requiring the prior and full knowledge of the system. Simulation results show that our proposed approach yields significant performance gains reaching up to 33.85% in terms of improving the network throughput, and up to 95% of enhancing the energy efficiency compared to a learning-based benchmark. Comparing to a nonlearning-based approach, our proposed approach improves the throughput and energy efficiency by 46.61% and 110%, respectively.

A growing demand has been established over the recent years for quick and inexpensive oil adulteration detection testing to convoy the automated and computerized processes in the industry. This research study presents a practical application of a simple low-resource microwave sensor of small size and high sensitivity to rapidly identify oil types, and monitor its quality and authenticity without having to open any bottles off-shelf. The sensor utilizes the nonreciprocal whispering-gallery-modes (WGMs) traveling on a ferrite ring resonator (FRR) when coupled to a microstrip line (MTL). The magnetic anisotropy of the ferrite is exploited to acquire four sensitive WGM resonances of nonreciprocal nature in the 22–32-GHz spectrum. A fabricated prototype is practically tested for identifying oil samples of different ingredients and brands, when loaded onto the FRR at consistent volume inside glass bottles of identical geometry. The measured scattering responses have shown a high detection sensitivity for the small contrast between the edible oils as demonstrated by the explicit frequency shifts in magnitude and phase of both S21and S12. The article also discusses a generalized concept for the complementing system layers in an Internet of Things (IoT) architecture for potential implementation in the industry.

By increasing the applicability of ground-based SAR (GBSAR) systems in geoscience and remote sensing, the development and evaluation of new systems have gained attention. This paper simulates a millimeter-wave (mm-wave) GBSAR system based on a W-band multiple-input multiple-output (MIMO) radar mounted on a mechanical rail. Combining a MIMO configuration with SAR processing in a GBSAR can result in a high resolution imaging system with optimized data acquisition time. The simulated results demonstrate a high potential of W-band MIMO GBSAR for structural monitoring with micro-meter precision for displacement estimation and strong impulse response of targets in the imaging scene.

In this paper, we present a novel solution for the remote breathing and sleep position monitoring by using a multi-input-multi-output (MIMO) radar. Our proposed system is able to monitor a number of people simultaneously, and therein we use a high-resolution direction of arrival (DOA) detection for finding closely separated targets. So, it effectively increases the number of target detection and reduces the cost by reducing the number of sensors. Furthermore, our proposed system is capable of identifying the sleep position of each monitored person by selecting appropriate target features and using a support vector machine (SVM) classifier. The breathing analysis involves designing an optimum filter for estimating both the breathing rate and the noiseless breathing waveform. In addition, we use the radar in a bedroom environment above a bed where two subjects sleep next to each other. The accuracy of the breathing monitoring subsystem is more than 97% for human subjects in the bedroom compared with a reference sensor. Also, the correct rate for sleep position detection is more than 83%.

This study reports the in-vitro characterization of blood samples based on the dielectric property measurements across the microwave spectrum 2 - 67 GHz using a newly developed commercial coaxial probe kit. This is done for different ABO-Rh types of synthetic blood samples as well as authentic specimens drawn from healthy adult donors. Measured results have indicated an appreciably dielectric contrast between blood samples of various types at certain frequency regions. This phenomenon is also demonstrated by the resonant measurements on a novel developed CSRR-based biosensor in the centimeter-band using two different setups of Vector Network Analyzer and 2.45 GHz radar board.

Monitoring glycemia levels in people with diabetes has developed rapidly over the last decade. A broad range of easy-to-use systems of reliable accuracies are now deployed in the market following the introduction of the invasive self-monitoring blood glucose meters (i.e. glucometers) that utilize the capillary blood samples from the fingertips of diabetic patients. However, the limitations and discomforts associated with these painful finger pricking devices have established a new demand for non-invasive pain-free blood glucose monitors to encourage more frequent glucose checks and thereby contribute more generously to diabetes care and prevention. In this study, a novel microwave biosensor is developed in a wearable format to enable non-invasive real-time monitoring of blood glucose level. The design comprises three cells of circular complementary split ring resonators (CSRRs) incorporated in the ground plane of an FR4 dielectric substrate. The passive sensing elements (CSRRs) are excited remotely via a coupled antenna to enable the wearable sensing in a reader/tag configuration. The CSSR-sensor is numerically modeled and analyzed for sensing the glucose concentrations relevant to diabetes condition (60-500mg/dL) by tracking the resonant amplitude variations in the frequency range 1-4GHz. The sensitivity performance of the TP-CSRR tag is practically demonstrated through in-lab measurements using a VNA setup.

Deployment of unmanned aerial vehicles (UAVs) as aerial base stations to support cellular networks can deliver a fast and flexible solution for serving high and varying traffic demand. In order to adequately leverage the benefit of UAVs deployment, their efficient placement is of utmost importance, and requires to intelligently adapt to the environment changes. In this paper, we propose novel learning-based mechanisms for the three-dimensional deployment of UAVs assisting terrestrial networks in the downlink for overloaded situations. The problem is modeled as a game among UAVs. To solve the game, we utilize tools from reinforcement learning, and develop low complexity algorithms based on the multi-armed bandit and satisfaction methods to learn UAVs' locations. Simulation results reveal that the proposed satisfaction based UAV placement algorithm can yield significant performance gains up to about 50% and 41% in terms of throughput and the number of outage users, respectively, compared to a learning based benchmark algorithm.

The use of phased array systems, previously limited to official users and the military, has entered new application areas, resulting in greater general use. K-band phased arrays in the civilian satellites help to transmit the wireless data accurately and efficiently to automotive, ships and airplanes. When installed in a ground vehicle, phased arrays offer us a new driving experience by detecting potential road hazards and dangers automatically. The phase shifter is the crucial element to a phased array. A K-band phased-array system normally demands its phase shifter to be of wideband, low insertion loss, low loss variation and high linearity. This paper proposes a reflective-type phase shifter in GLOBALFOUNDRIES 0.13-μm SiGe technology that strikes an excellent and balanced performance. We have applied novel changes onto a conventional Lange coupler and carefully designed the loading network accordingly. The measured results are as follows. The operation frequency range covers almost the entire K-band to accommodate various applications using this frequency band. The phase shifter provides a 180° phase shift range. The insertion loss is limited to the range of 10-dB ± 3-dB. The group delay is less than 70-ps in the entire K-band. The input return loss both at the input and output ports is greater than 10-dB within the entire K-band. The die area excluding the pads is 550-μm × 290-μm.

Millimeter-wave interferometric synthetic aperture radiometer (InSAR) imaging has received considerable attention owing to its pure radiation receiving scheme and high resolution. In recent years, compressive sensing (CS) has been applied to InSAR on the assumption that InSAR image could be formulated to a sparse vector. However, the CS-based InSAR ignores the 2-D sparsity of images and its performance degrades while processing large-scale images. Further using the sparsity of InSAR image, a novel InSAR imaging approach referred to as InSAR-matrix completion (MC) is proposed in this letter by adopting the MC techniques, which can recover a low-rank matrix from a small subset of its corrupted entries. Directly representing the target image as a matrix, InSAR-MC formulates the estimation of the visibility function as a 2-D linear transformation and inversely obtains the target image by convex optimization as sparsity of millimeter-wave image and interferometric operation satisfy the low-rank property. Experimental results demonstrate the proposed approach's validity and better performance of the recovered images with comparison against CS-based InSAR and other traditional approaches.

With the development of wireless communication technology, 5G will develop into a new generation of wireless mobile communication systems. MIMO (multiple-input multiple-output) technology is expected to be one of the key technologies in the field of 5G wireless communications. In this paper, 4 pairs of microstrip MIMO conformal antennas of 35 GHz have been designed. Eight-element microstrip Taylor antenna array with series-feeding not only achieves the deviation of the main lobe of the pattern but also increases the bandwidth of the antenna array and reduces sidelobe. MIMO antennas have been fabricated and measured. Measurement results match the simulation results well. The return loss of the antenna at 35 GHz is better than 20 dB, the first sidelobe level is −16 dB, and the angle between the main lobe and the plane of array is 60°.

A soft antenna selection (SAS) method is basically implemented by a Linear Network (LN) which is located in RF domain of multiple input multiple output (MIMO) systems. In this paper, we find the receive optimum SAS for MIMO spatial multiplexing and diversity transmissions when both of noise and interference exist in the system. The optimization is applied for two practical cases where the transmitter either has access to the channel state information (CSI) or does not. Moreover, we investigate the optimization for both no-constraint SAS and phase shift based SAS (PSB-SAS) modules. A two dimensional (2D) spatial presentation is used to model the interference.We evaluate the performance of the SAS technique over a Rician channel via computer simulations. The simulation results show a MIMO system with optimum SAS achieves the performance of the full-complexity MIMO for the whole range of interference-to-noise ratios.

A soft antenna selection (SAS) method is basically implemented by a linear network (LN) which is located in RF domain of multiple-input multiple-output (MIMO) systems. In this paper, we find the optimum receiver SAS structure which maximizes the signal to interference-plus-noise ratio (SINR) gain for the diversity transmission in an interference channel. The optimization is applied for two practical cases of transmitters either with or without channel state information. A two-dimensional (2D) directive model is employed to realize the interference effect. A Rician channel model is used for computer simulation. The simulation results show the optimum SAS achieving the performance of the full-complexity MIMO, i.e. a MIMO system with the same number of antenna terminals and RF chains.

In this paper, we introduce a novel beamforming algorithm for single receiver phased array antenna systems and study its performance in terms of the convergence speed and the steady state error, through simulations and experimental tests. Neither a priori knowledge of the target's direction, nor the phase-voltage characteristic of the phase shifters are required in this algorithm. Instead, the total received power, which is the only array output, is sampled to perform the beamforming and adjust the phase shifters. Insensitivity to the variation of the phase shifter characteristics, and simultaneous calibration is an important feature of this algorithm. This algorithm has been successfully applied to a 16-element Ku-band phased array.

The results of a prototype novel planar antenna providing dual-band performance for road-vehicle applications are reported. Multilayer stacked patches provide simultaneous RHCP polarization for GPS and vertically polarized low elevation coverage for 5.9 GHz DSRC operations. The results show good correlation between measurement and theory.

Multiple input multiple output (MIMO) systems equipped with Smart Passive Receive Antenna Selection (SPRAS) technique [1], [3] need the Channel State Information at Receiver side (CSIR) to maximize the mutual information through applying the optimum passive weighting matrix. In this paper, a frequency domain channel estimation method is proposed for Orthogonal Frequency-Division Multiplexing (OFDM)-based SPRAS-MIMO system in generalized condition, and a mathematical method is provided to deduce both the training sequence and beamformer structure design during channel estimation. The channel estimation method is realized in an IEEE 802.16 based MIMO system. The simulation results show that this method works well for different estimation criteria.

Multiple input multiple output (MIMO) systems equipped with smart passive receive antenna selection (SPRAS) technique need the channel state information at receiver side (CSIR) to optimally and adaptively provide the same performance as full-complexity MIMO structure. However, a perfect CSIR is not usually available due to channel estimation errors. In this paper, we investigate the effect of minimum mean square error (MMSE) channel estimation on the capacity of SPRAS-MIMO system. The lower and upper bounds on the mutual information under channel estimation error are studied and the mutual information is maximized with the optimum passive beamformer. Numerical results show how the capacity decreases by increasing the channel estimation error. The effect of the channel estimation error on the bit-error-rate (BER) performance is also studied.

This paper presents a rapid and efficient modeling method, based on the microstrip lines theory, for the coupling between a device contact and the substrate backplane. We derive simple closed-form formulas to model spiral inductors as important substrate-noise sources in mixed-signal systems. The model has been verified via IE3D simulations up to 35 GHz, and is adaptable to CAD tools. We demonstrate that the accuracy of the proposed model is better than 8%.

This paper presents two rapid and yet accurate modeling methods for substrate coupling between a device contact and a substrate backplane. We discuss effects of physical parameters and geometrical characteristics of the contact and the substrate on the proposed models. We also derive model expressions for extraction of circuit-model elements of the substrate. Both methods are efficient for speed, memory usage, and adaptable to computer-aided design (CAD) tools for optimization tasks. The accuracy of both methods, the parametric modeling method and the microstrip line approximation method, is validated by comparing with the rigorous simulation data obtained from IE3D. Using the new models, we record a much higher speedup factor and extremely lower memory requirements compared to other published methods. The modeling methods are extended to two-layer structures and the models are applied to spiral inductors for verification purposes. In our research, we have validated the models over a wide range of frequencies up to 20 GHz.