Wireless Transceiver Research Articles

Research on mechanical vibration monitoring based on wireless sensor network and sparse Bayes

Mechanical vibration monitoring for rotating mechanical equipment can improve the safety and reliability of the equipment. The traditional wired monitoring technology faces problems such as high-frequency signal pickup and high-precision data collection. Therefore, this paper proposes optimization techniques for mechanical vibration monitoring and signal processing based on wireless sensor networks. First, the hardware design uses high-performance STM32 as the control center and Si4463 as the wireless transceiver core. The monitoring node uses a high-precision MEMS acceleration sensor with a 16-bit resolution ADC acquisition chip to achieve high-frequency, high-precision acquisition of vibration signals. Then, the bearing vibration signal optimization method is studied, and the sparse Bayes algorithm is proposed as a compressed sensing reconstruction algorithm. Finally, the difference in reconstruction accuracy between this method and the traditional reconstruction algorithm is compared through experiments and the effect of this method on the reconstruction performance is analyzed when different parameters are selected.

Verification of the Random Line-of-Sight Measurement Setup at 1.5-3 GHz Including MIMO Throughput Measurements of a Complete Vehicle

The performance evaluation of wireless systems is crucial for the development of future systems with more connected devices. It is essential to have an easy and relevant method for ensuring the wireless communication performance of the devices. We have recently developed a new random line-of-sight (random-LOS) measurement system for evaluating the communication performance of wireless devices, e.g., transceivers and antennas installed on a vehicle. In the measurement system, a plane wave is generated in the test zone emulating the far-field wave transmitted from a radio base station. In this paper we present both numerical simulations and actual experimental results of the random-LOS over-the-air (OTA) measurement setup operating in the 1.5–3 GHz band. The measurement accuracy is determined by the field variations within the test zone, where a smaller variation gives better measurement accuracy. In this paper the achieved accuracy expressed in terms of standard deviation (STD) was evaluated to be approximately 1 dB of the power within a cylindrical test zone of height 0.4 m and diameter 2 m. The active multiple-input multiple-output (MIMO) performance of antenna systems installed on an actual vehicle was measured and evaluated using the presented setup. A comparison to a theoretical zero forcing (ZF) receiver is also presented.

A Fully Integrated Sensor-Brain–Machine Interface System for Restoring Somatosensation

Sensory feedback is critical to the performance of neural prostheses that restore movement control after neurological injury. Recent advances in direct neural control of paralyzed arms present new requirements for miniaturized, low-power sensor systems. To address this challenge, we developed a fully-integrated wireless sensor-brain-machine interface (SBMI) system for communicating key somatosensory signals, fingertip forces and limb joint angles, to the brain. The system consists of a tactile force sensor, an electrogoniometer, and a neural interface. The tactile force sensor features a novel optical waveguide on CMOS design for sensing. The electrogoniometer integrates an ultra low-power digital signal processor (DSP) for real-time joint angle measurement. The neural interface enables bidirectional neural stimulation and recording. Innovative designs of sensors and sensing interfaces, analog-to-digital converters (ADC) and ultra wide-band (UWB) wireless transceivers have been developed. The prototypes have been fabricated in 180nm standard CMOS technology and tested on the bench and in vivo. The developed system provides a novel solution for providing somatosensory feedback to next-generation neural prostheses.

Real-time traffic distribution prediction protocol (TDPP) for vehicular networks

Over downtown and highway road scenarios several applications have been proposed to enhance the quality of driving trips there. Safety, efficiency and entertainment services are provided to vehicles through several advanced technologies. Many of these applications require accurate investigation of the traffic characteristics and distributions over the area of interest in order to successfully provide the targeted services. To mention a few, path recommendation protocols, traffic light scheduling algorithms and driving assistance techniques need specific, detailed and accurate traffic distribution reports regarding the investigated area of interest. Several traffic prediction and evaluation protocols have been proposed in the literature using historical, visual and wireless connecting technologies to gather the real-time basic traffic data. Accuracy, delay, bandwidth consumption and high cost required equipments are the main challenges of the previous protocols. In this paper, we aim to propose a real-time traffic distribution prediction protocol (TDPP) using the vehicular network technology. The proposed protocol aims to produce accurate traffic evaluation and distribution of the investigated area of interest based on gathering the basic traffic data of some traveling vehicles there. From the experimental results we can infer that the TDPP protocol provides more accurate traffic evaluation in the case that only a few vehicles are equipped with wireless transceivers. Moreover, it requires less bandwidth and time to evaluate the traffic characteristics compared to traditional protocols in this field, since it only processes the basic traffic data of the small selected set of vehicles there.

A study on modeling vehicles mobility with MLC for enhancing vehicle-to-vehicle connectivity in VANET

In Vehicular ad hoc networks (VANETs), vehicle-to-vehicle (V2V) is a significant mode of communication in which vehicles communicate with other moving vehicles with the aid of wireless transceivers. Due to the rapid mobility of vehicles, network connectivity over VANETs is frequently unstable, especially in sparse highways. This paper analyzes V2V connectivity dynamics by designing the microscopic mobility and lane changing decision model using an adaptive cursive control mechanism and recurrent neural network. Extensive simulators like SUMO and NS2 analyze the validity of this proposed model. The proposed analytical model provides a framework for examining the impact of mobility dependent metrics such as vehicle velocity, acceleration/deceleration, safety gap, vehicle arrival rate, vehicle density and network metric data delivery rate for characterizing the VANET connectivity of the proposed network. The simulation results synchronized those of the proposed model, which illustrated that the developed analytical model of this work is effective.

A city-wide experimental testbed for the next generation wireless networks

To facilitate research in dynamic spectrum access, 5G, vehicular networks, underground wireless communications, and radio frequency machine learning, a city-wide experimental testbed is developed to provide realistic radio environment, standardized experimental configurations, reusable datasets, and advanced computational resources. The testbed contains 5 cognitive radio sites, and covers 1.1 square miles across two campuses of the University of Nebraska-Lincoln and a public street in the city of Lincoln, Nebraska. Each site is equipped with a 4x4 MIMO software-defined radio transceiver with 20Gbps fronthaul connectivity. Additional cognitive radio transceivers with an underground 2x2 MIMO antenna are included in a site. High speed fronthaul network based on dedicated fiber connects the 5 sites to a cloud-based central unit for data processing and storage. The testbed provides researchers rich computational resources such as arrays of CPUs and GPUs at the cloud and FPGAs at both the edge and fronthaul network. Developed via the collaboration of the university, city, and industrial partners, this testbed will facilitate education and researches in academic and industrial communities.

Free‐Space Communications Enabled by Quantum Cascade Lasers

Future generations of wireless communication systems are expected to support orders of magnitude faster data transfer with much lower latency than the currently deployed solutions. Development of wireless transceivers of higher bandwidth, low energy consumption, and small footprint becomes challenging with radio frequency (RF) electronic technologies. Photonics‐assisted technologies show many advantages in generating signals of ultrabroad bandwidth at high carrier frequencies in the millimeter‐wave, terahertz, and IR bands. Among these frequency options, the mid‐IR band has recently attracted great interest for future wireless communication due to its intrinsic merits of low propagation loss and high tolerance of atmospheric perturbations. A promising source for mid‐IR free‐space communications is the semiconductor quantum cascade laser (QCL), which can be directly modulated at a high speed and facilitates monolithic integration for compact transceivers. Herein, the research and development of QCL‐based free‐space communications are reviewed and a recent experimental study of multi‐gigabit transmission with a directly modulated mid‐IR QCL and a commercial off‐the‐shelf IR photodetector is reported on. Up to 4 Gb s−1 transmission of two advanced modulation formats, namely, four‐level pulse amplitude modulation (PAM‐4) and discrete multitone (DMT) modulation, is demonstrated.

An introduction and review on innovative silicon implementations of implantable/scalp EEG chips for data acquisition, seizure/behavior detection, and brain stimulation

Technological advances in the semiconductor industry and the increasing demand and development of wearable medical systems have enabled the development of dedicated chips for complex electroencephalogram (EEG) signal processing with smart functions and artificial intelligence‐based detections/classifications. Around 10 million transistors are integrated into a 1 mm2 silicon wafer surface in the dedicated chip, making wearable EEG systems a powerful dedicated processor instead of a wireless raw data transceiver. The reduction of amplifiers and analog‐digital converters on the silicon surface makes it possible to place the analog front‐end circuits within a tiny packaged chip; therefore, enabling high‐count EEG acquisition channels. This article introduces and reviews the state‐of‐the‐art dedicated chip designs for EEG processing, particularly for wearable systems. Furthermore, the analog circuits and digital platforms are included, and the technical details of circuit topology and logic architecture are presented in detail.

300-GHz-Band 120-Gb/s Wireless Front-End Based on InP-HEMT PAs and Mixers

We developed a 300-GHz-band 120-Gb/s wireless transceiver front-ends (TRX) using our in-house InP-based high-electron-mobility-transistor (InP-HEMT) technology for beyond-5G. The TRX is composed of the RF power amplifiers (PAs), mixers, and local oscillation (LO) PAs which are all packaged in individual waveguide (WG) modules by using a ridge coupler for low-loss WG-to-IC transition. RF PAs are designed using the low-impedance inter-stage-matching technique to reduce the inter-stage matching loss of the amplifier stages, and the back-side DC line (BDCL) technique is used to simplify the layout and to improve the gain of the PAs. The fabricated RF PAs show a high output 1-dB compression point of more than 6 dBm from 278 to 302 GHz. The mixers are used for both up- and down-conversion in the transmitter and receiver. These mixers are designed to have high conversion gain (CG) over the wideband even after packaging by enhancing the isolation between the RF and IF ports. The measured CG of mixer module is −15 dB, and the 3-dB IF-bandwidth is 32 GHz. The LO PAs are also designed using the BDCL technique so that they can supply the required LO power to the mixers. The TRX with these InP building blocks enables the data transmission of a 120 Gb/s 16QAM signal over a link distance of 9.8 m.

Passive Intermodulation in Simultaneous Transmit–Receive Systems: Modeling and Digital Cancellation Methods

This article presents novel solutions for suppressing passive intermodulation (PIM) distortion in frequency-division duplexing (FDD)-based radio transceivers, stemming from nonlinear radio frequency (RF) components and simultaneous transmission and reception, with special emphasis on modern carrier aggregation networks. With certain transmission band combinations in, e.g., long-term evolution (LTE) or the emerging 5G new radio (NR) mobile radio systems, the nonlinear distortion produced by the passive components of the transceiver RF front end can result in intermodulation (IM) distortion that falls within one of the configured reception bands. While the traditional solution to mitigate this problem is to reduce the transmit power, we take an alternative approach and seek to cancel such PIM in the transceiver digital front end by using the original transmit data as a reference. To generate as accurate cancellation signal as possible, we derive different advanced signal models for the observable IM distortion at own receiver band that incorporates also the power amplifier (PA) nonlinearities, together with the passive component nonlinearities and the frequency-selective responses of the duplex filters. The performance and the processing complexity of the devised digital cancellation and parameter estimation solutions are evaluated with real-life RF measurements, where an actual LTE-Advanced user equipment (UE)-type transceiver system is utilized. The obtained results show that the proposed cancellers are implementation feasible and can suppress the self-interference by over 20 dB, canceling the distortion nearly perfectly up to UE transmit powers of +24 dBm. The results also indicate that, in many cases, it is necessary to model the nonlinear distortion effects produced by the PAs, even if the individual component carriers are combined after the amplification stage.

Machine Learning for Solar Array Monitoring, Optimization, and Control

The efficiency of solar energy farms requires detailed analytics and information on each panel regarding voltage, current, temperature, and irradiance. Monitoring utility-scale solar arrays was shown to minimize the cost of maintenance and help optimize the performance of the photo-voltaic arrays under various conditions. We describe a project that includes development of machine learning and signal processing algorithms along with a solar array testbed for the purpose of PV monitoring and control. The 18kW PV array testbed consists of 104 panels fitted with smart monitoring devices. Each of these devices embeds sensors, wireless transceivers, and relays that enable continuous monitoring, fault detection, and real-time connection topology changes. The facility enables networked data exchanges via the use of wireless data sharing with servers, fusion and control centers, and mobile devices. We develop machine learning and neural network algorithms for fault classification. In addition, we use weather camera data for cloud movement prediction using kernel regression techniques which serves as the input that guides topology reconfiguration. Camera and satellite sensing of skyline features as well as parameter sensing at each panel provides information for fault detection and power output optimization using topology reconfiguration achieved using programmable actuators (relays) in the SMDs. More specifically, a custom neural network algorithm guides the selection among four standardized topologies. Accuracy in fault detection is demonstrate at the level of 90+% and topology optimization provides increase in power by as much as 16% under shading.

Improved adaptive impedance matching for RF front-end systems of wireless transceivers

In this paper an automatic adaptive antenna impedance tuning algorithm is presented that is based on quantum inspired genetic optimization technique. The proposed automatic quantum genetic algorithm (AQGA) is used to find the optimum solution for a low-pass passive T-impedance matching LC-network inserted between an RF transceiver and its antenna. Results of the AQGA tuning method are presented for applications across 1.4 to 5 GHz (satellite services, LTE networks, radar systems, and WiFi bands). Compared to existing genetic algorithm-based tuning techniques the proposed algorithm converges much faster to provide a solution. At 1.4, 2.3, 3.4, 4.0, and 5.0 GHz bands the proposed AQGA is on average 75%, 49.2%, 64.9%, 54.7%, and 52.5% faster than conventional genetic algorithms, respectively. The results reveal the proposed AQGA is feasible for real-time application in RF-front-end systems.

Stochastic Power Consumption Model of Wireless Transceivers.

Energy efficiency is a key aspect when designing and optimizing contemporary wireless networks and transceivers. Assessment of energy efficiency requires proper energy consumption models. The most common solutions are to measure a single device and propose a device-specific model or to propose a simplified model for many transceivers but not reflecting all phenomena visible in a given transceiver energy consumption. Therefore, it has to be selected to accurately model a single transceiver or coarsely model a wide group of transceivers. This paper proposes a new approach, where a fixed energy consumption model is used but with parameters being random variables. This reflects variability between various transceivers from various vendors. First the model parameters are adjusted separately for each of 14 measured WiFi modems. These devices are treated as samples of a wider population of devices and their parameters are used for stochastic parameters modeling, i.e., choosing the random variables’ distributions, their parameters, and the correlation among parameters. The proposed model can be used, e.g., for system-level network design where variability among transceivers power consumption can be used as a new degree of freedom. The paper presents simulation results for a simple multi-hop link whose energy consumption is characterized in much more detail thanks to the proposed stochastic power consumption model.

Radiofrequency Switches Based on Emerging Resistive Memory Technologies - A Survey

High-performance radio frequency (RF) switches play a critical role in allowing radio transceivers to provide access to shared resources such as antennas. They are important, especially in reconfigurable radios, where the connections within and between the different blocks (e.g., filters, amplifiers, and mixers) can be changed to customize and compose RF functions for distinct frequency bands. To realize this potential, new concepts for RF switch devices that can be integrated with standard transistor process, while providing better transmission performance, lower power consumption, smaller area, and lower actuation voltage than current RF switch technologies [e.g., field-effect transistors (FETs) and microelectromechanical systems (MEMSs)] are needed. Recently, RF switches based on emerging memory technologies, such as conductive-bridge RAM (CBRAM), resistive RAM (ReRAM), and phase-change memory (PCM), have been proposed. These nanoscale switches present major advantages, for example, high cutoff frequency, nonvolatility, fast and low-energy switching, small footprint, and compatibility with back-end-of-line (BEOL) of the standard CMOS process, thus placing them as possible contenders for high-performance RF switches. This article surveys high-performance RF switches based on resistive memories, comparing them with mature RF switching technologies like RF MEMS, p-i-n diodes, and FETs. We discuss the physical mechanisms, device structure, performance characteristics, and applications of these novel RF switches. Furthermore, we examine the prospects and future research directions of these technologies that could lead to their adoption at the industrial level.

A parameter extraction method of the PIN diode for physics‐based circuit simulation over a wide frequency range

The accurate physical parameters of the semiconductor devices are critical to the physics-based circuit simulation, which solves the carrier transport equations to model the semiconductor devices. However, the conventional method extracts physical parameters from low-frequency measurements such as the DC I-V curve, which cannot work at high frequencies. To overcome this problem, we propose a physical parameter extraction method of the PIN diode working well from DC to microwave frequencies. Specifically, because the transit-time effects are dependent on the working frequencies and input power levels, the operation modes of the PIN diode can be divided into three cases from DC to microwave frequencies; therefore, the proposed method extracts the parameters from three measured curves, including the DC I-V curve, a small-signal, and a large-signal voltage waveform both at a microwave frequency. Experiments of a PIN diode SMP1330 circuit show that the error of the conventional method is about 45% at frequencies above 300 MHz, but the maximum error of the proposed method is only 9.5% from DC to 2 GHz. Moreover, the conventional method is unable to characterize the conductance modulation phenomenon, which leads to unexpected signal reflections in PIN limiter circuits and the missing of information in radio transceivers.

Improved Continuous-Time Delta-Sigma Modulators With Embedded Active Filtering

Continuous-time ΔΣ modulators used in wireless transceivers need to digitize small desired signals that are accompanied by large out-of-band interferers. In such applications, it is desirable that the CTΔΣM have a low-pass signal transfer function whose bandwidth can be specified. Many architectures present themselves as potential candidates to realize such filtering-CTΔΣMs. This work investigates these threads and concludes that a CTΔΣM with an embedded Rauch filter is a compelling design choice. The theory is supported by measurement results from a filtering-CTΔΣM test chip that achieves a peak SNDR of 76.7dB in a 1MHz signal bandwidth. Measurements demonstrate improved power efficiency and out-of-band linearity when compared to prior art.

Design of Wireless Temperature and Humidity Acquisition System

Based on the Z-Stack protocol Stack, with the CC2530 radio frequency transceiver chip and DHT22 digital temperature and humidity sensor as the core, this article puts forward a new implementation designed for the wireless temperature and humidity acquisition system on the basis of the Zigbee wireless communication technology. With the method of upper machine software by GUI, changes in temperature and humidity can be shown on the display terminal intuitively. The article mainly expounds the system structure design, temperature andhumiditydataacquisitionprocessandgraphicalinterfacemonitoringimplementation. Experimentsshowthat the wireless sensor system can make the real-time data acquisition of temperature and humidity, which has not only good efficiency and scalability, but also high reliability and excellent stability.

The EcoChip 2: An Autonomous Sensor Platform for Multimodal Bio-environmental Monitoring of the Northern Habitat.

This paper presents the EcoChip 2, an autonomous multimodal bio-environmental sensor platform for the monitoring of microorganisms in the northern habitat. The EcoChip 2 prototype includes an array of 96-wells for the continuous monitoring of microbiological growth through a multichannel electrochemical impedance analyzer circuit. In addition, the platform includes luminosity, humidity, temperature sensors and monitoring. The developed electronic board uses an ultra-low-power microcontroller unit, a custom power management unit, a low-power wireless ISM-2.45 GHz transceiver, and a flash memory to accumulate and store the sensor data over extended monitoring periods. When a wireless base station is placed within the transmission range of the EcoChip 2, an embedded low-power wireless transceiver transmits the 96-wells impedance data and the other sensor data stored in the flash memory to the user interface. We present the measured performance of the prototype, along with laboratory test results of bacterial growth measurements inside the 96 wells in parallel. We show that the EcoChip 2 can successfully measure the impedances associated with bacterial growth over several hours using an excitation frequency of 2 kHz with power consumption of 114.6 mW under operating mode.

Comparison of Baseband Processors in Terms of Realization SDR-Transceivers

Software-defined Radio is a programmable transceiver with the capability of operating various wireless communication protocols without the need to change or update the hardware. Consequently, Software-defined Radio has earned a lot of attention and is of great significance to both academia, military and aerospace industry. Components of Software-defined Radio (e.g. mixers, filters, amplifiers, modulators/demodulators, detectors, etc.) implemented by means of software on a personal computer or embedded system. Operation of signal processing are handed over to the baseband processor, rather than being done in special electronic circuits. Baseband processors are implemented through employing various types of hardware platforms, such as General Purpose Processors, Graphics Processing Units, Digital Signal Processors, and Field Programmable Gate Arrays. Each of these platforms is associated with their own set of advantages and disadvantages. In this paper was proposed a comparison of the state-of-the-art hardware platforms in the context of implementation Software-defined Radio transceivers. For comparison was determined as follow criteria: computational power of hardware platform, power consumption, complexity of developing, and cost of tools and equipment. First approaches to realizing baseband processors is using a General Purpose Processor and accelerating by Graphics Processing Units. But General Purpose Processor and Graphics Processing Units execute software instructions in the sequential order. For this reason, General Purpose Processors are not convenient for high-throughput computing with real-time requirements. Also this hardware platforms have increased power consumption. This aspect does not allow use General Purpose Processor and Graphics Processing Units in small and portable Software-defined Radio transceivers. In other hand, General Purpose Processors are preferable hardware platform by researchers and beginners due to their flexibility and programmability. Therefore, General Purpose Processors and Graphics Processing Units is highly recommended for prototyping Software-defined Radio platforms. Digital Signal Processor was reviewed as alternative approach for implementing baseband processors. Digital Signal Processors is a particular type of General Purpose Processors that is optimized to process digital signals. Digital Signal Processors have similar disadvantage with insufficient computational power, but some manufacturer sell energy optimized Digital Signal Processors. Consequently, Digital Signal Processor is commonly used in small and portable Software-defined Radio transceivers. Field Programmable Gate Arrays and System-on-Chips with Field Programmable Gate Array are strongly recommended for high-performance Software-defined Radio platforms. This hardware platforms combine the flexibility of processors and efficiency of small Digital Signal Processor. Field Programmable Gate Arrays can achieve a high level of parallelism in executing digital signal processing. However, the designers must have a high degree in digital electronics and good acknowledgement of hardware description languages. After the research, was proposed own flexible architecture Software-defined Radio transceiver and methods for development.

Towards a Low-cost RSSI-based Crop Monitoring

The continuous monitoring of crop growth is crucial for site-specific and sustainable farm management in the context of precision agriculture. With the help of precise in situ information, agricultural practices, such as irrigation, fertilization, and plant protection, can be dynamically adapted to the changing needs of individual sites, thereby supporting yield increases and resource optimization. Nowadays, IoT technology with networked sensors deployed in greenhouses and farmlands already contributes to in situ information. In addition to existing soil sensors for moisture or nutrient monitoring, there are also (mainly optical) sensors to assess growth developments and vital conditions of crops. This article presents a novel and complementary approach for a low-cost crop sensing that is based on temporal variations of the signal strength of low-power IoT radio communication. To this end, the relationship between crop growth, represented by the leaf area index (LAI), and the attenuation of signal propagation of low-cost radio transceivers is investigated. Real-world experiments in wheat fields show a significant correlation between LAI and received signal strength indicator (RSSI) time series. Moreover, influencing meteorological factors are identified and their effects are analyzed. Including these factors, a multiple linear model is finally developed that enables an RSSI-based LAI estimation with great potential.