This paper presents a new technique of radio frequency (RF) signal strength detection with a received signal strength indicator (RSSI) circuit which can be deployed in an internet-of-things (IoT) network. The proposed RSSI circuit is based on a direct conversion of RF to digital code indicating the signal strength. The direct conversion is achieved by the repeated switching of a rectifier's output voltage using an ultra-low power comparator. A 5-bit programmable feedback circuit is used to correct detection inaccuracies. The RSSI circuit is implemented in a 65-nm CMOS process and consumes 6nW power. It has a linear dynamic range of 26dB and exhibits an error of ±0.5dB with a wide bandwidth of 750MHz. A detailed analysis of the RSSI circuit is presented and verified with simulation and measurement results. The high detection accuracy with ultra-low power consumption of our RSSI circuit is favourable for IoT applications including localization, beamforming, hardware security and other low-power applications.
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http://dx.doi.org/10.1109/tcsi.2022.3181543 | DOI Listing |
Sensors (Basel)
February 2024
Laboratory for Electrical Instrumentation and Embedded Systems, Faculty of Engineering, University of Freiburg, 79110 Freiburg, Germany.
Resonators are passive time-invariant components that do not produce a frequency shift. However, they respond to an excitation signal close to resonance with an oscillation at their natural frequencies with exponentially decreasing amplitudes. If resonators are connected to antennas, they form purely passive sensors that can be read remotely.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
July 2023
In this work, a methodology for assessing the impact of implantation surgery on laboratory mice on behavior was created. The study included the design of several implants fabricated on various printed circuit board (PCB) technologies with overall diameters between 26-28mm and weights between 4.5-6.
View Article and Find Full Text PDFEntropy (Basel)
December 2022
Zhejiang Integrated Circuits and Intelligent Hardware Collaborative Innovation Center, Hangzhou Dianzi University, Hangzhou 310018, China.
Aiming at the path planning problem of unmanned aerial vehicle (UAV) base stations when performing search tasks, this paper proposes a Double DQN-state splitting Q network (DDQN-SSQN) algorithm that combines state splitting and optimal state to complete the optimal path planning of UAV based on the Deep Reinforcement Learning DDQN algorithm. The method stores multidimensional state information in categories and uses targeted training to obtain optimal path information. The method also references the received signal strength indicator (RSSI) to influence the reward received by the agent, and in this way reduces the decision difficulty of the UAV.
View Article and Find Full Text PDFIEEE Trans Biomed Circuits Syst
October 2022
The wearable localization system for wireless capsule endoscopy (WCE) is a potential technology to realize rapid diagnosis and treatment of the gastrointestinal (GI). However, the electromagnetic localization accuracy of WCE still needs to be improved. In this paper, based on RSSI electromagnetic fading model, the accurate fitting parameter values are obtained by Kalman filter and the least square method.
View Article and Find Full Text PDFIEEE Trans Circuits Syst I Regul Pap
September 2022
Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, 02115 USA.
This paper presents a new technique of radio frequency (RF) signal strength detection with a received signal strength indicator (RSSI) circuit which can be deployed in an internet-of-things (IoT) network. The proposed RSSI circuit is based on a direct conversion of RF to digital code indicating the signal strength. The direct conversion is achieved by the repeated switching of a rectifier's output voltage using an ultra-low power comparator.
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