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Monitoring human activities, such as walking, falling, and jumping, provides valuable information for personalized health assistants. Existing solutions require the user to carry/wear certain smart devices to capture motion/audio data, use a high-definition camera to record video data, or deploy dedicated devices to collect wireless data. However, none of these solutions are widely adopted for reasons such as discomfort, privacy, and overheads. Therefore, an effective solution to provide non-intrusive, secure, and low-cost human activity monitoring is needed. In this study, we developed a contactless human activity monitoring system that utilizes channel state information (CSI) of the existing ubiquitous WiFi signals. Specifically, we deployed a low-cost commercial off-the-shelf (COTS) router as a transmitter and reused a desktop equipped with an Intel WiFi Link 5300 NIC as a receiver, allowing us to obtain CSI data that recorded human activities. To remove the outliers and ambient noise existing in raw CSI signals, an integrated filter consisting of Hampel, wavelet, and moving average filters was designed. Then, a new metric based on kurtosis and standard deviation was designed to obtain an optimal set of subcarriers that is sensitive to all target activities from the candidate 30 subcarriers. Finally, we selected a group of features, including time- and frequency-domain features, and trained a classification model to recognize different indoor human activities. Our experimental results demonstrate that the proposed system can achieve a mean accuracy of above 93%, even in the face of a long sensing distance. [ABSTRACT FROM AUTHOR]

Transfer Learning (TL) has emerged as a powerful approach for improving the performance of Deep Learning systems in various domains by leveraging pre-trained models. It was proven that features learned by deep learning can smoothly be reused across similar domains. Deep transfer learning schemes compensate for limited training data via transfer learning of a rich data environment. This paper investigates the effectiveness of applying TL schemes in indoor localization. It proposes four deep TL models where the knowledge is transferred from the rich-measurement data source domain to multiple target domains with limited data measurements. The architecture of the source domain is based on Convolutional Neural Network (CNN), where the four deep TL models for the target domain are: standalone feature extractor, integrated feature extractor, selective fine-tuning, and weight initialization. We employed a dataset of RF fingerprinting measurement signals representing common interior conditions, including extremely crowded, medium cluttered, low cluttered, and open environments, to test the effectiveness of the proposed TL models. We measured the accuracy and computation time of target-domain models trained, with varied percentages of restricted data sizes: 40%, 30%, 20%, 15%, 10%, 5%, and 2.5%. The experimental results show that all TL models are effective in achieving significant improvement in accuracy when compared to non-transferred models, even with minimal training data size. However, the proper determination of the TL model and the amount of training data profoundly influence the performance results. [ABSTRACT FROM AUTHOR]

The data used during training in any given application space are directly tied to the performance of the system once deployed. While there are many other factors that are attributed to producing high-performance models based on the Neural Scaling Law within Machine Learning, there is no doubt that the data used to train a system provide the foundation from which to build. One of the underlying heuristics used within the Machine Learning space is that having more data leads to better models, but there is no easy answer to the question, "How much data is needed to achieve the desired level of performance?" This work examines a modulation classification problem in the Radio Frequency domain space, attempting to answer the question of how many training data are required to achieve a desired level of performance, but the procedure readily applies to classification problems across modalities. The ultimate goal is to determine an approach that requires the lowest amount of data collection to better inform a more thorough collection effort to achieve the desired performance metric. By focusing on forecasting the performance of the model rather than the loss value, this approach allows for a greater intuitive understanding of data volume requirements. While this approach will require an initial dataset, the goal is to allow for the initial data collection to be orders of magnitude smaller than what is required for delivering a system that achieves the desired performance. An additional benefit of the techniques presented here is that the quality of different datasets can be numerically evaluated and tied together with the quantity of data, and ultimately, the performance of the architecture in the problem domain. [ABSTRACT FROM AUTHOR]

In the automotive industry, products must be tested and verified before being launched; currently, products such as automotive radio are usually tested with expensive metrology equipment, such as the Rohde & Schwarz SFE100, which produces radio frequency signals. This study assessed the feasibility of replacing the SFE100 with two inexpensive devices, Adalm-Pluto and LimeSDR usb, which are software-defined radios. For this evaluation, a study was carried out to determine specific metrological characteristics and then to compare with those of metrological equipment. Based on the results and tests of the study, we conclude that SDRs can generate test signals (FM, DAB, DVB, ISDB and SXM) with uncertainties of 0.5dBm and a spectral density ratio comparable to SFE100, thereby enabling their replacement. Furthermore, the evaluation datasets of all equipment have been publicly available [1] , enabling the scientific community to extract the metrological uncertainties for all signal carriers generated by the device. The result of this study proposes a methodology for testing auto radios that is innovative and can be applied to a production line based on the principles of Industry 4.0. [ABSTRACT FROM AUTHOR]

Achieving accurate indoor localization is of paramount importance for numerous applications, including asset tracking, navigation, and context-aware services. In this research, we propose a design and an implementation of a deep Convolutional Neural Network (CNN) classification model for indoor localization. The model is trained and tested using a rich labeled dataset encompassing four different indoor environments sharing a common characteristic of being located on the same floor within the same building. Each environment is characterized by varying levels of clutter: highly cluttered, medium cluttered, and low cluttered open spaces. The experimental results demonstrate a remarkable increase in localization accuracy across all environments. The average accuracy achieved by the deep CNN classification model exceeds 99%. This impressive performance highlights the model's ability to effectively distinguish and classify objects in indoor environments that exhibit varying degrees of clutter. The proposed model holds great promise for applications that rely on precise indoor localization, showcasing its potential to meet the demands of real-world scenarios. [ABSTRACT FROM AUTHOR]

Due to low cost and low complexity, passive ultrahigh-frequency radio-frequency identification (UHF RFID) is gaining popularity in indoor localization. According to current RFID channel models, multipath propagation and noise are two significant factors that affect the accuracy of measured RF signals. To minimize the impacts of measurement errors on localization, scene analysis algorithms are proposed and got lots of attention in recent years. These algorithms make use of reference tags as similarity markers in neighboring locations to improve localization accuracy. However, densely deployed tags induce a new challenge, antenna interference, leading to the poor signal similarity between nearby tags. Nonetheless, there are no refined channel models for explaining the effect of spacing distances between tags on RF signal fluctuations, thus the choice of spacing distances becomes an outstanding issue for scene analysis localization algorithms. In this article, we propose Timing, the Tag Interference ModelING, for analyzing the relationship between signal fluctuations and spacing distances. Unlike inductive coupling in the near field, as the tags are all located at the far field of interacted antenna, we introduce microwave transmission line theory for channel model analysis. The experimental results verify that Timing is well suited to authentic situations. Additionally, we examine the performance of the ${k}$ -nearest neighbor (${k}$ -NN) algorithm under various spacing distances. The findings demonstrate that the spacing distance identified by Timing as a stable point achieves better accuracy with scene analysis algorithms. Therefore, Timing could be used as a critical foundation for the choice of spacing distances in future tag deployment applications. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) classification and identification have many applications in a variety of fields, including UAV tracking systems, antidrone systems, intrusion detection systems, military, space research, product delivery, agriculture, search and rescue, and internet carrier. It is challenging to identify a specific drone and/or type in critical scenarios, such as intrusion. In this article, a UAV classification method that utilizes fixed boundary empirical wavelet sub-bands of radio frequency (RF) fingerprints and a deep convolutional neural network (CNN) is proposed. In the proposed method, RF fingerprints collected from UAV receivers are decomposed into 16 fixed boundary empirical wavelet sub-band signals. Then, these sub-band signals are then fed into a lightweight deep CNN model to classify various types of UAVs. Using the proposed method, we classify a total of 15 different commercially available UAVs with an average testing accuracy of 97.25%. The proposed model is also tested with various sampling points in the signal. Furthermore, the proposed method is compared with recently reported works for classifying UAVs utilizing remote controller RF signals. [ABSTRACT FROM AUTHOR]

Recently, super-resolution ultrasound imaging with ultrasound localization microscopy (ULM) has received much attention. However, ULM relies on low concentrations of microbubbles in the blood vessels, ultimately resulting in long acquisition times. Here, we present an alternative super-resolution approach, based on direct deconvolution of single-channel ultrasound radio-frequency (RF) signals with a one-dimensional dilated convolutional neural network (CNN). This work focuses on low-frequency ultrasound (1.7 MHz) for deep imaging (10 cm) of a dense cloud of monodisperse microbubbles (up to 1000 microbubbles in the measurement volume, corresponding to an average echo overlap of 94%). Data are generated with a simulator that uses a large range of acoustic pressures (5-250 kPa) and captures the full, nonlinear response of resonant, lipid-coated microbubbles. The network is trained with a novel dual-loss function, which features elements of both a classification loss and a regression loss and improves the detection-localization characteristics of the output. Whereas imposing a localization tolerance of 0 yields poor detection metrics, imposing a localization tolerance corresponding to 4% of the wavelength yields a precision and recall of both 0.90. Furthermore, the detection improves with increasing acoustic pressure and deteriorates with increasing microbubble density. The potential of the presented approach to super-resolution ultrasound imaging is demonstrated with a delay-and-sum reconstruction with deconvolved element data. The resulting image shows an order-of-magnitude gain in axial resolution compared to a delay-and-sum reconstruction with unprocessed element data. [ABSTRACT FROM AUTHOR]

This paper studies intelligent reflecting surface (IRS) aided wireless power transfer (WPT) to batteryless Internet of Everything (IoE) devices. A practical energy receiver (ER) with multiple antennas is investigated. Multiple RF energy flows gleaned by all the receive antennas are input multiple energy harvesters, which are further rectified to direct-current (DC) energy. The resultant multiple DC energy flows are then combined in the DC domain for energy storage. Three classic waveforms, namely deterministic waveform, M-QAM waveform, and Gaussian waveform, are considered for WPT. We maximize the output DC power by jointly designing the active transmit beamformer of the transmitter and the passive reflecting beamformer of the IRS with the above-mentioned waveforms, respectively, subject to the transmit power constraint at the transmitter and to the limited resolution constraints on the phase-shifters of the IRS. A low complexity alternating optimization (AO) algorithm is proposed, which converges to a Karush-Kuhn-Tucker (KKT) point and thus results in a locally optimal solution. The numerical results demonstrate that the Gaussian waveform has the best energy performance with a low input RF power to the energy harvesters. By contrast, the deterministic waveform becomes superior with a high input RF power to the energy harvesters. [ABSTRACT FROM AUTHOR]

Nonlinear dynamic features are crucial in specific emitter identification (SEI), which works based on the nonlinearity of the emitter. Nevertheless, its underlying principles and stability have not been systematically studied, so these methods are close to a black box, making the recognition performance of the system unstable in applications. In this study, we provide an in-depth analysis of its mechanism and theoretical model, extracting, for the first time, the intrinsic low-dimensional nonlinear manifold structure (IMS) of radio frequency signals based on manifold learning and analyzing its robustness and distinctiveness for SEI. Hardware type and intentional modulation are found to determine the overall shape of IMS, and the device-specific information for SEI is subtle and distributed locally on IMS. Notably, sampling-related random disturbances cause the distortion, zooming, or rotation of IMS, which lowers the identification accuracy by a range of 1.74%–52%, depending on the feature, device type, signal type, and other factors. [ABSTRACT FROM AUTHOR]

Wireless power transfer provides a most convenient solution to charge devices remotely and without contacts. R&D has advanced the capabilities, variety, and maturity of solutions greatly in recent years. This survey provides a comprehensive overview of the state of the art on different technological concepts, including electromagnetic coupled and uncoupled systems and acoustic technologies. Solutions to transfer mW to MW of power, over distances ranging from millimeters to kilometers, and exploiting wave concepts from kHz to THz, are covered. It is an attractive charging option for many existing applications and moreover opens new opportunities. Various technologies are proposed to provide wireless power to these devices. The main challenges reside in the efficiency and range of the transfer. We highlight innovation in beamforming and UV-assisted approaches. Of particular interest for designers is the discussion of implementation and operational aspects, standards, and safety relating to regulations. A high-level catalog of potential applications maps these to adequate technological options for wireless power transfer. [ABSTRACT FROM AUTHOR]

The stable radio frequency (RF) transmission scheme involving single wavelength has received increasing attention due to the feature of accurate phase information extraction. However in single laser system, the single side band (SSB) phase noise of output signal will be deteriorated by Rayleigh scattering during long-distance optical transmission. In this work, we propose an incoherent Rayleigh scattering noise depression method in the passive phase compensation stable RF transmission scheme with single laser. The carrier of scattering light is suppressed with narrow-band optical filter based on the Stimulated Brillouin scattering (SBS) effect, so that the noise generated by the beat of scattering itself will be significantly reduced. As a result, a 5-GHz RF signal stability transmission over 50-km single mode fiber is demonstrated experimentally. The SSB phase noise at 10 KHz offset is 10.9 dB lower than the scenarios without incoherent Rayleigh scattering noise depression. Meanwhile, the phase jitter mean square error is 1.12 ps during $1\times 10^{4}$ seconds. [ABSTRACT FROM AUTHOR]

Gesture recognition has become increasingly important in human-computer interaction and can support different applications such as smart home, VR, and gaming. Traditional approaches usually rely on dedicated sensors that are worn by the user or cameras that require line of sight. In this paper, we present a fine-grained finger gesture recognition system by using commodity WiFi without requiring user to wear any sensors. Our system takes advantages of the fine-grained Channel State Information available from commodity WiFi devices and the prevalence of WiFi network infrastructures. It senses and identifies subtle movements of finger gestures by examining the unique patterns exhibited in the detailed CSI. We devise environmental noise removal mechanism to mitigate the effect of signal dynamic due to the environment changes. Moreover, we propose to capture the intrinsic gesture behavior to deal with individual diversity and gesture inconsistency. Lastly, we utilize multiple WiFi links and larger bandwidth at 5GHz to achieve finger gesture recognition under multi-user scenario. Our experimental evaluation in different environments demonstrates that our system can achieve over 90 percent recognition accuracy and is robust to both environment changes and individual diversity. Results also show that our system can provide accurate gesture recognition under different scenarios. [ABSTRACT FROM AUTHOR]

Ultrafast measurement ofbroadband radio frequency (RF) spectrum is of great importance in dispersion compensation, signal impairment monitoring and coherence analysis. As an all-optical RF spectrum measuring system, the frequency-domain light intensity spectrum analyzer (f-LISA) maps the RF information into the time domain and realizes the single-shot RF spectrum acquisition. In order to further improve the performance of f-LISA, we investigate theoretically the relationship of resolution and bandwidth with the factors including the spectral width of the probe, the front and output 3rd-order dispersion (TOD). Under the theoretical guidance, 1 nm spectral width of the probe is filtered out to reduce the impact of the front TOD, and the optical phase conjugation is adopted to decrease the output TOD which has a greater impact on f-LISA. The optimized f-LISA achieves the frame rate of 20.6 MHz, the resolution of 1 GHz and the bandwidth of 2.2 THz which is 3 times wider than the original f-LISA, demonstrating better measurement stability and accuracy. Furthermore, the high-speed and broadband RF spectrum analyzer is promising to provide a solution for ultrafast dynamical RF spectrum scenario like terahertz measurement. [ABSTRACT FROM AUTHOR]

A disambiguation method for dual-comb sampling frequency measurement is proposed and experimentally demonstrated. For dual-comb sampling frequency measurement, multiple solutions may exist if only the frequency information of the output signal is employed for disambiguation. By further exploiting the phase relationship between the intermediate frequencies and the first harmonics of the two sampling rates, most of the ambiguous frequencies can be distinguished in our experiment. The experiment results also indicate that nonideal phase response of the devices will reduce the performance of the method especially in high frequencies. To solve the problem, an improved method, in which the variations of the phases are observed and used to solve the ambiguity, is given so that the method is free from the calibration of the nonideal phase response. [ABSTRACT FROM AUTHOR]

Backscatter communication (BC) enables ultra-low-power communications and allows devices to harvest energy simultaneously. But its practical deployment faces severe security threats caused by its nature of openness and broadcast. Authenticating backscatter devices (BDs) is treated as the first line of defense. However, complex cryptographic approaches are not desirable due to the limited computation capability of BDs. Existing physical layer authentication schemes cannot effectively support BD mobility, multiple attacker identification and attacker location tracing in an integrated way. To tackle these problems, this paper proposes BCAuth, a multi-stage authentication and attack tracing scheme based on the physical spatial information of BDs to realize enhanced BD authentication security for both static and mobile BDs. After initial authentication based on BD identity with its position information registration, preemptive authentication and re-authentication are performed according to spatial correlation of backscattered signal source locations associated with the BD. By exploiting clustering-based analysis on spacial information, BCAuth is capable of determining the number of attackers and localizing their positions. In addition, we propose a reciprocal channel-based method for BD re-authentication with better authentication performance than the clustering-based method for mobile BDs when the BDs is able to measure received signal strength (RSS), which also enables mutual authentication. We theoretically analyze BCAuth security and conduct extensive numerical simulations with various settings to show its desirable performance. [ABSTRACT FROM AUTHOR]

In this letter, the uplink (UL) wireless information transmission (WIT) and downlink (DL) wireless power transfer (WPT) in cell-free massive multiple-input multiple-output (CF-mMIMO) networks are investigated. Specifically, the mobile terminals (MTs) collect the radio-frequency energy emitted from the access points (APs) in the DL WPT phase and then use the harvested energy (HE) to transmit pilot and data symbols in the UL WIT phase. Considering the performance of CF-mMIMO networks with a large number of MTs is severely bottlenecked by pilot contamination, the superimposed pilot transmission (SPT) is advocated to alleviate its impact and improve transmission efficiency. Based on the SPT, closed-form UL achievable spectral efficiency (SE) and DL average HE expressions are derived. Simulation results verify the accuracies of the derived expressions and indicate that the SPT significantly outperforms the regular pilot transmission in terms of both SE and HE. [ABSTRACT FROM AUTHOR]

Power-handling capability of bulk CMOS-based single-pole double-throw (SPDT) switch operating in millimeter-wave (mm-wave) and subterahertz region is significantly limited by the reduced threshold voltage of deeply scaled transistors. A unique design technique based on impedance transformation network (ITN) is presented in this work, which improves 1-dB compression point, namely P1dB, without deteriorating other performance. To prove the presented solution is valid, a 70–100-GHz switch is designed and implemented in a 55-nm bulk CMOS technology. At 90 GHz, it achieves a measured P1dB of 15 dBm, an insertion loss (IL) of 3.5 dB, and an isolation (ISO) of 18 dB. The total area of the chip is only 0.14 mm2. [ABSTRACT FROM AUTHOR]

This study considers a two-way amplify-and-forward relay system wherein two users exchange information with the assistance of an energy-harvesting MIMO relay node. With two-time-slot transmission, the relay receives the transmitted signal from both users in the first time-slot and splits it into two parts for information forwarding and energy harvesting, respectively, in accordance with a particular power splitting ratios. The phases of the RF signals used for energy harvesting at each relay antenna are shifted before being combined to achieve the highest power. Meanwhile, the RF signals used for information forwarding are passed through a linear precoder and then broadcast to the users in the second time-slot. We aim to optimize the power splitting ratios, precoding matrix, and RF phase shifting factors such that the sum-rate is maximized. Since joint optimization is intractable, the parameters are partitioned into three sub-optimization problems. For each parameter, a sub-optimal solution is derived in a (semi-)closed form. It shows numerically that the proposed sub-optimal solutions for the optimal phase shift and power split ratio are close to the global optimal solutions. Moreover, the proposed solution for the precoding matrix outperforms the so-called ’General Power Iterative’ algorithm proposed in the literature. [ABSTRACT FROM AUTHOR]

Using a license-free industrial, scientific, and medical (ISM) frequency band as 2.45 GHz, a miniaturized incident angle-insensitive radio frequency (RF) energy harvester design for the Internet-of-Things (IoT) applications is proposed. For incident angle insensitivity of ambient RF energy and a low-profile compact design, the proposed harvester is used to a two-layer printed circuit board (PCB) substrate, which consists of orthogonally deployed antennas with an $LC$ balun, impedance matching, and Dickson charge pump circuits at each layer. The dual-polarized antenna for miniaturized design uses a bowtie-shaped meander dipole antenna having orthogonal arrangement. By measuring the efficiency of each diode stage of the rectifier circuit, a two-stage Dickson charge pump circuit with optimal efficiency was selected, and the dc outputs of the two rectifier circuits are connected in parallel. This rectifier circuit has a conversion efficiency of up to 71.4% and harvests approximately 1.6 times more power than a single-polarized circuit from any incident ambient RF signal. Due to its compact size and improved harvesting power at various incident angles, the proposed energy harvester can act as a power source, such as many kinds of ultralow power consumption systems, such as IoT sensors and medical applications. [ABSTRACT FROM AUTHOR]

In this letter, we present a reconfigurable intelligent surface (RIS)-assisted space shift keying (SSK) modulation and reflection phase modulation (RPM) scheme, where RIS embeds its own information in the reflection phase shift of the reflected radio frequency (RF) signal. More specifically, the RIS simultaneously performs two tasks: i) reflecting the impinging RF signal with a discrete phase shift and ii) embedding the information bits in the phase shift. We perform joint detection for the RPM and SSK symbols using a maximum likelihood detector, and a unified analytical framework is presented for theoretical analysis of the average bit error rate (ABER) and ergodic capacity of the proposed scheme. All numerical results are thoroughly verified through Monte Carlo simulation. Numerical results reveal that the proposed scheme outperforms the conventional SSK scheme. [ABSTRACT FROM AUTHOR]

A new techniqueto detect the phase difference of two high-frequency RF signals is presented. It is based on downconverting the two high-frequency input RF signals into two low-frequency signals via Serrodyne optical frequency translation and optical frequency downconversion. The phase difference of the two low-frequency downconverted signals is the same as the two high-frequency input RF signal phase difference. Hence a low-cost low-frequency electronic phase detector can be used after photodetection to generate a DC voltage with a value depending on the two input RF signal phase difference. The technique is simple, novel, and innovative. It utilises the advantage of ultra-wide operating frequency range in microwave photonics and the input RF power independent phase detection characteristic in electronic phase detectors. It overcomes the limitations of the input RF signal power and frequency need to be known prior to phase detection in the reported microwave photonic based phase detectors. Experimental results demonstrate 0°-180° RF signal phase difference detection with 1° resolution and less than ±2.6° errors for different RF signal frequencies (10 GHz, 14 GHz and 18 GHz) and different RF modulation indexes (0.2, 0.4 and 0.7). Results also demonstrate the proposed phase detector has excellentrepeatability. [ABSTRACT FROM AUTHOR]

A photonic system based on a two-channel structure is proposed to realize the simultaneous measurement of angle-of-arrival (AOA) and Doppler-frequency-shift (DFS) of microwave signal without direction ambiguity. The unambiguous AOA measurement can be acquired by recording the power of the output intermediate frequency (IF) signal and output radio frequency (RF) signal from two channels. The DFS can be determined from the frequency of the IF signal. Estimated experimental results show that a −66.44° to 66.44° AOA measurement with errors of less than ±2.2° and an unambiguous DFS measurement with errors of ±0.6 Hz are implemented. [ABSTRACT FROM AUTHOR]

The front-end circuit used in the pulsar acquisition of the radio telescope determines the availability of the entire pulsar-based timing framework. However, it would be expensive and resource consuming to use a real radio telescope in the circuit design process. To address this issue, in this article, the hardware-in-the-loop simulation solution is proposed to generate, sample, and extract pulse signals in real time. The artificial pulse generation method is first proposed to generate the pulses that are close to real pulsar signals. Then, three analog front-end circuit architectures (AFCAs) are proposed to generate the analog pulses, including the superposition, attenuator, and attenuator-based superposition structures. The generated pulses are close to the real pulsar signal in shape, amplitude, and noise level. Next, a pulsar-based timing synchronization framework that can be used to improve the timing accuracy of the wide-area monitoring systems is developed to process the sampled data. Multiple experiments are conducted to verify the effectiveness of the analog front-end and the pulsar-based timing synchronization framework using the simulated pulse and real pulsar signals. The results demonstrated that the designed AFCAs can generate effective analog pulses, and is convenient and flexible. [ABSTRACT FROM AUTHOR]

This article proposes and presents an innovative approach to realizing the concurrent dual-band transmission with only a single front-end and local oscillator at radio frequency (RF). Fundamentally, the proposed concurrent dual-band front-end is based on the conventional multiport interferometric architecture for single-band operation. Nevertheless, the conventional variable loads are replaced by diode networks with IF input signals to produce the RF signals at their designated center frequencies. Furthermore, due to the linear characteristics of a multiport network, the signals with different data contents will be mixed into both RF channels. To this end, an inverse matrix operation is applied to reversely convert the original data streams before the modulations of IF input signals. As a result, the two original data streams can be effectively modulated into specific RF channels (for transmission) with minimal interchannel interferences based on this configuration. In addition to the theoretical analyses, this innovative concurrent dual-band transmitting front-end has been investigated and verified with $M$ -quadrature amplitude modulation (QAM) signals in simulations and experiments. All outcomes demonstrate excellent performances in concurrent dual-band transmission with significant reductions in both size and biasing power. [ABSTRACT FROM AUTHOR]

We propose and experimentally demonstrate an ultraefficient broadband photonic channelizer. By polarization-division multiplexing the received signal and using dual-output Hartley image-reject receiving topology, $4N$ channels can be generated with only ($N +1$)-line signal optical frequency comb (OFC) and $N$ -line local oscillator (LO) OFC, and the frequency interval of the OFC is reduced to half of the typical value. With the use of the integrated dual-polarization coherent detection receiver (DP-CDR), the required quadrature optical hybrid (QOH) and the balanced photodetectors (BPDs) are reduced to half. The system size is reduced and stability is improved. Besides, assisted with a digital signal processor (DSP) algorithm, the IQ imbalance can be compensated, and approximately, ideal image rejection can be realized. A proof-of-concept experiment is carried out. A 2–14-GHz wideband signal is successfully divided into 12 channels with 1-GHz bandwidth by applying four-line signal OFC and three-line LO OFC. The image rejection ratio (IRR), channel isolation (CI), and spurious-free dynamic range (SFDR) are also measured and discussed. [ABSTRACT FROM AUTHOR]

In this work, we study the covert communications in ambient backscatter systems (ABS) with uncontrollable RF source on AWGN channels. In contrast to the prior works impractically assuming the existence of a controllable RF excitation source, our work arms the receiver of covert information with the full-duplex ability. The covert receiver can emit artificial noise (AN) with variable power to cover up the modulation action of a tag on the excitation signals while receiving the backscattered information. Specifically, we first derive the warden’s optimum power-detection threshold that minimizes the detection error probability. To against the optimal warden, we design the covert backscatter communication policy that determines the feasible region of the AN power at the covert receiver depending on the transmission power of the RF source and the reflection coefficient of the tag and guarantees the covertness constraint. We analyze the performance of the policy and provide the closed-form maximum covert rate and maximum detection error probability at the warden, revealing their tradeoff. The numerical analysis shows the increasing transmission power of the RF source and tag’s reflection coefficient would degrade the covertness of ABS when the covert receiver has to increase the AN power. [ABSTRACT FROM AUTHOR]

Detecting human body motion is a promising application to monitor the health status of the elderly or patient. With the rapid advancement of the fifth generation of cellular network technology (5G), an increasing number of radio frequency (RF) devices are deployed on the human body. These devices have great potential to continuously and remotely detect human motions. In this paper, we propose a thorough study for developing effective on-body RF motion detection systems from three perspectives, including effects of signal frequencies, effects of on-body sensor locations, and effects of classification algorithms. First, based upon handcrafted on-body RF characteristics, the detection accuracies are studied at 0.4GHz, 2.4GHz, and 5GHz, when the RF transceiver is mounted on the human hand, foot, and head. A minimal redundancy maximal relevance (mRMR) based stepwise detection is implemented to cross-validate the study. It also proves that a subset of candidate features can provide an acceptable detection accuracy. Moreover, to improve the detection performance, an attention-based bidirectional long-short term memory (BiLSTM) is proposed for on-body RF motion detection. We distinguish the typical human body motions, including standing, walking, and falling, with an average accuracy of 99.04%. The performance of the proposed attention-based BiLSTM has been analyzed with visualized training histories and attention weights. [ABSTRACT FROM AUTHOR]

With ever more stringent requirements of cellular standards it is an ongoing challenge to find the optimal RF transmitter architecture. In recent years, the multiphase transmitter (MP TX) architecture has been introduced as a promising candidate. It features high power efficiency and avoids phase modulation which is critical for upconversion of ultra-wideband signals. In this brief, the problem of unwanted spectral emission, inherent in MP TX architectures, is compensated by a proper data delay. Such an approach is considered for the first time in the multiphase architecture and achieves a spectral improvement of up to 38 dB in adjacent channels and 24 dB at the receiver (RX) duplex distance for a new radio (NR) signal with 20 MHz bandwidth. With the proposed solution the MP TX architecture becomes a highly competitive candidate for wideband signals such as LTE, LTE-CA or NR signals. In addition, a detailed and efficient implementation of the digital front-end (DFE) for the MP TX architecture including the proposed data delay is presented. [ABSTRACT FROM AUTHOR]

In this letter, we present a novel intelligent reflecting surface (IRS)-assisted unmanned aerial vehicle (UAV) communication system, where both the IRS and ground users (GUs) can achieve self-sustainable operations by exploiting the energy harvesting (EH) technique. We consider a practical scenario that the direct links between the UAV and GUs always suffer from blockages, typically in complex urban scene. To mitigate this adverse effect, the IRS can be properly deployed to create a clear propagation environment. We maximize the sum-rate for the proposed system by jointly optimizing the IRS’s phase-shift, transmit power and time allocation of GUs, and trajectory of the UAV. To handle the non-convexity caused by coupled variables, the original problem is decomposed into three subproblems and solved by the block coordinate descent (BCD) method. Through numerical results, the proposed algorithm can achieve significant performance gains over the benchmarks. [ABSTRACT FROM AUTHOR]

Quantitative ultrasound (QUS), which attempts to extract quantitative features from the US radiofrequency (RF) or envelope data for tissue characterization, is becoming a promising technique for noninvasive assessments of liver fibrosis. However, the number of feature variables examined and finally used in the existing QUS methods is typically small, limiting the diagnostic performance. Therefore, this paper devises a new multiparametric QUS (MP-QUS) method which enables the extraction of a large number of feature variables from US RF signals and allows for the use of feature-engineering and machine-learning based algorithms for liver fibrosis assessment. In the MP-QUS, eighty-four feature variables were extracted from multiple QUS parametric maps derived from the RF signals and the envelope data. Afterwards, feature reduction and selection were performed in turn to remove the feature redundancy and identify the best combination of features in the reduced feature set. Finally, a variety of machine-learning algorithms were tested for fibrosis classification with the selected features, based on the results of which the optimal classifier was established. The performance of the proposed MP-QUS method for staging liver fibrosis was evaluated on an animal model, with histologic examination as the reference standard. The mean accuracy, sensitivity, specificity and area under the receiver-operating-characteristic curve achieved by MP-QUS are respectively 83.38%, 86.04%, 80.82%, and 0.891 for recognizing significant liver fibrosis, and 85.50%, 88.92%, 85.24%, and 0.924 for diagnosing liver cirrhosis. The proposed MP-QUS method paves a way for its future extension to assess liver fibrosis in human subjects. [ABSTRACT FROM AUTHOR]

Myocardial infarction (MI) is the myocardial necrosis caused by persistent ischemia and hypoxia of coronary arteries. People do not realize that they are suffering from MI until they have a heart attack. Early MI detection plays a vital role in symptom relief and improvement in the performance of daily activities. However, conventional MI detection methods require expensive and inconvenient medical tests, e.g., intrusive blood tests or wear electrocardiogram (ECG) sensors, which can only be performed in medical institutions. In this paper, we introduce a contactless and non-intrusive MI detection method based on wireless sensing that monitors abnormalities in heartbeats. Specifically, we present Health-Radio, a radar-based system towards early MI detection. Health-Radio extracts heart rate variability (HRV) from the RF signals reflected from users. In particular, with our carefully designed signal processing algorithms, Health-Radio is able to not only obtain heartbeat signals when the user is stationary, but also tolerate interference when the user is performing certain activities, e.g., eating, reading and browsing the Internet. We have recruited 30 MI patients from the Central Hospital of Wuhan, China, and 30 healthy university students to conduct comprehensive evaluations of the performance of Health-Radio. The experiment results show that Health-Radio can achieve a median MI detection accuracy of 81.2 percent when the users are stationary, which is comparable to ECG-based MI detection. Even when the users are not stationary, Health-Radio can still achieve a median detection accuracy of 66.5 percent. Health-Radio is promising in providing a new paradigm for smart-home healthcare in the future. [ABSTRACT FROM AUTHOR]

A radio frequency memory (RFM) based on microwave photonic mixing and digital RFM is proposed and demonstrated. By introducing the microwave photonic mixing, the operational frequency range is largely extended. Besides, the mixing spurs are effectively suppressed, which is important to guarantee the system efficiency and avoid generating significant feature for the jamming recognition. Meanwhile, by applying the digital RFM, flexible modulation and reconstruction are achieved. A proof-of-concept experiment is carried out. The wide working frequency range from C- to Ka- bands is achieved. The mixing spurs over the 0–40 GHz frequency range are experimentally suppressed. Radar imaging is successfully deceived. A radar waveform with a bandwidth of 500 MHz in the K band is successfully received and reconstructed. Two false targets are added. To the best of our knowledge, it is the first time that a photonics-assisted RFM is reported to have the functions of storage, flexible modulation, and reconstruction with wideband RF signals at a high frequency. The proposed scheme shows the great potential to be applied in electronic warfare, electronic environment simulation and testing, and so on. [ABSTRACT FROM AUTHOR]

Monitoring of electromagnetic field (EMF) near power distribution facilities, both in the low- and the high-frequency ranges, has become highly demanded in recent years. The reason lies in the accumulated concerns on public health, which is mostly caused by the evolution of electric power and communication infrastructure, in and around the power substations, as well as their closeness to residential areas. In this paper, the initial comparative analysis of the EMF monitoring results of three one-day campaigns, in 2015, 2018 and 2020, performed in the vicinity of the Serbian highpower distribution substation "Novi Sad 7", is presented. The overall EMF levels in all campaigns comply with the reference levels prescribed by the Serbian legislation, including some new EMF sources which were detected in 2018 and 2020. Likewise, the used continuous monitoring has demonstrated suitability to systematically address EMF fluctuation on daily basis, as well as corresponding concerns on EMF exposure. [ABSTRACT FROM AUTHOR]

This article presents spur reduction and efficiency enhancement techniques for pulse-modulated polar transmitters (PMPTs). The technique allows low-power out-of-band odd spurs in PMPTs to be suppressed deeply before going into power amplifiers (PAs). By configuring the PAs as a balanced Doherty amplifier for efficiency improvement with quad-phase aliasing-free digital pulsewidth modulation (PWM), the several even spurs can be separated directly into the isolation port of the output quadrature coupler. The residual even spurs can be easily suppressed using direct connection of an absorptive bandpass filter (BPF) behind each PA. Using the proposed architecture, the prototype transmitter achieved 49.4% drain efficiency at an output channel power of 28 dBm using 20-MHz bandwidth long-term evolution (LTE) 16-quadrature amplitude modulation (QAM) test signals at 836.5 MHz. After output signal restoration by the proposed absorptive filters, an output power level of 27.1 dBm and 40% drain efficiency were achieved while keeping the spur level close to −60 dBc without using any surface acoustic wave (SAW) filter. [ABSTRACT FROM AUTHOR]

Multipactor prediction methods are of high relevance for the space industry in order to prevent its appearance during the design phase of RF high-power components. Up to the present time, prediction for multicarrier signals has been covered by an empirical rule, the 20-gap-crossing rule (20GCR), proposed in the 2003 version of the multipactor standard published by the European Cooperation for Space Standardization (ECSS). The 20GCR has been widely used by the space industry, although some studies have demonstrated that it might be inaccurate in some situations. The latest version of the ECSS multipactor standard, published in 2020, presents two novel methods for multipactor prediction with multicarrier signals: the pulsed method and the envelope sweep (ES) method, both simple, accurate enough, and suitable for industry standards. While the pulsed model is a simple and fast method based on a 1-D analytical theory, the ES method is more accurate and able to deal with real 3-D microwave structures. This article details both multipactor prediction methods, as well as their practical validation with a large dataset from previous analytical studies and experimental activities. [ABSTRACT FROM AUTHOR]

In this paper, the physical-layer security (PLS) for an underlay cognitive radio network (CRN) with multiple non-colluding eavesdroppers is studied. We assume that a secondary user (SU) transmitter communicates with an SU receiver over a cascaded $\kappa$ - $\mu$ fading channel and under the threat of eavesdropping. A cooperative jammer exists to improve the SUs’ security by harvesting energy from the SU transmitter using power splitting technique. This harvested energy is utilized to generate jamming signals to deteriorate the eavesdroppers’ reception quality. Moreover, the eavesdroppers are assumed to be randomly distributed according to a homogeneous Poisson point process (HPPP), in which the $k^{th}$ closest eavesdropper to the SU transmitter will be selected in our analysis. Additionally, the legitimate receiver is assumed to harvest energy from the SU transmitter using the power splitting technique. In this context, we propose an optimum power splitting factor at the legitimate receiver that achieves the best privacy while constraining the amount of the harvested energy. Furthermore, on enhancing system security, we investigate the effectiveness of the jamming technique. The impact of distances over security is also examined. The results reveal an improvement in privacy as the legitimate receiver utilizes the adaptable power splitting factor. PLS is evaluated in terms of the probability of non-zero secrecy capacity and the intercept probability. [ABSTRACT FROM AUTHOR]

Over the last decade, simultaneous wireless information and power transfer (SWIPT) has become a practical and promising solution for connecting and recharging battery-limited devices due to significant advances in low-power electronics technology and wireless communications techniques. To realize the promised potentials, advanced resource allocation design plays a decisive role in revealing, understanding, and exploiting the intrinsic rate–energy tradeoff capitalizing on the dual use of radio frequency (RF) signals for wireless charging and communication. In this article, we provide a comprehensive tutorial overview of SWIPT from the perspective of resource allocation design. The fundamental concepts, system architectures, and RF energy harvesting (EH) models are introduced. In particular, three commonly adopted EH models, namely, the linear EH model, the nonlinear saturation EH model, and the nonlinear circuit-based EH model, are characterized and discussed. Then, for a typical wireless system setup, we establish a generalized resource allocation design framework that subsumes conventional resource allocation design problems as special cases. Subsequently, we elaborate on relevant tools from optimization theory and exploit them for solving representative resource allocation design problems for SWIPT systems with and without perfect channel state information (CSI) available at the transmitter, respectively. The associated technical challenges and insights are also highlighted. Furthermore, we discuss several promising and exciting future research directions for resource allocation design for SWIPT systems intertwined with cutting-edge communication technologies, such as intelligent reflecting surfaces, unmanned aerial vehicles, mobile edge computing, federated learning, and machine learning. [ABSTRACT FROM AUTHOR]

There has been a boost in optoelectronic device technology that can leverage strengths of both optical and electronic worlds to support high-voltage and high-speed operation. It is critical to characterize the RF performance from the measured signals of these devices in order to evaluate their performance, optimize their designs and also aid in better understanding of the device physics. Conventional curve-fitting models either fail to fit measured signals with high accuracy or provide limited, if any, information about the device physics. Here, we propose a Prony-based curve-fitting method to characterize RF pulse measurements from such optoelectronic devices. The performance of the overall algorithm on measurement data shows high accuracy, with the capability to extract key pulse parameters such as full width at half maximum and rise time. Additionally, the capability of the method to extract time constants associated with semiconductor traps can help in better understanding of optoelectronic device physics. [ABSTRACT FROM AUTHOR]

Ambient backscatter communication (AmBC) is a new technology that has emerged as a potential option for Internet of Things (IoT) applications wherein the sensors and other miniature devices require low power for communication. We proposed a selection combining (SC) diversity scheme and analyze the outage performance of the AmBC system consisting of a moving reader and $M$ multiple tags. Particularly, we carried out the analysis under time-selective links along with imperfect channel estimation and the outage probability expression is derived. Our results indicate that as the number of tags increases, the probability of an outage lowers drastically and it gets worse with an increase in the speed of the reader as well as estimation error. Furthermore, an asymptotic equation is obtained for the high signal to noise ratio (SNR) case. The derived analytical expressions are validated using Monte Carlo (MC) simulations. [ABSTRACT FROM AUTHOR]

Significant progress has been made in recent years demonstrating an all-optical high sensitivity Radio Frequency (RF) sensor exploiting the Electromagnetically Induced Transparency (EIT) effect in an atomic vapor. Rydberg RF sensors have previously been shown to exhibit excellent lab-based performance in measuring electric field properties. As a commercial network operator we wish to exploit these capabilities to produce a rugged high-sensitivity receiver for 5G signals, so we seek simple planning rules applicable to readily available low cost components. We present a simple analytic model for the operation of a sensitive Rydberg RF receiver to predict the optical modulation in response to an RF signal. We focus on maximizing the contrast in the optical response to a modulated RF signal rather than to a constant RF field. We show that the optical modulation depth can be maximized using an optimum coupling intensity. The optical modulation depth drops off rapidly as the RF source is detuned away from resonance. By detuning the coupling frequency to compensate for the RF detuning we demonstrate that the useful RF bandwidth of the receiver can be significantly extended. We measure this variation in optical response using a Rubidium vapor at a 15 GHz RF carrier frequency. Using readily available optical components we obtain good agreement with our model. This convenient analytic approach will simplify the design and operation of Rydberg RF receivers as sensitive components in future 5G mobile communications, particularly for the power-limited uplink from mobile device to base-station. [ABSTRACT FROM AUTHOR]

A Radio over Fiber system based on optical frequency multiplication and multiplexing compatible with 3G/4G/5G systems for full spectrum access and handover with multiple scenarios is proposed and analyzed theoretically as well as simulated numerically. By combining a dual-drive Mach-Zehnder Modulator in Push-Pull modulation-mode with optical frequency multiplication and multiplexing technology, the simultaneous transmission of multiple radio frequency signals in optical carrier is realized. It can be also switchable for full spectrum access of different scenarios and user terminals with Multi-Input Multi-Output wireless capability, based on frequency diversity technology. The BER and eye diagram for the transmission performances have been presented by the simulation experiment. Meanwhile, the feasibility of the scheme has been demonstrated by lots of numerical simulations and accurate theoretical analyses. The method could meet the requirements for communication capacity and transmission rate in both continuous-wide-area coverage and hot-spot high-capacity transmission of 5G system, and for full spectrum access and handover. [ABSTRACT FROM AUTHOR]