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. We use a 5-bit programmable feedback circuit to correct detection inaccuracies. The RSSI circuit is implemented in a 65-nm CMOS process and consumes 15nW power. It has a linear dynamic range of 26dB and exhibits an error of (+/-)0.5dB with a wide bandwidth of 500MHz. We also present a detailed analysis of the proposed technique and verify it 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.
A (+/-)0.5dB, 15nW RSSI Circuit with RF-to-Digital Conversion Technique for Ultra-low Power IoT Radio Applications
A (+/-)0.5dB, 15nW RSSI Circuit with RF-to-Digital Conversion Technique for Ultra-low Power IoT Radio Applications
A (+/-)0.5dB, 15nW RSSI Circuit with RF-to-Digital Conversion Technique for Ultra-low Power IoT Radio Applications
Research Paper / Jun 2022
Related Content
White Paper /May 2025
Media over Wireless: Networks for Ubiquitous Video
Research Paper /Mar 2025
To realize the objectives of Integrated Sensing and
Communication (ISAC) in 6G, there is a need to introduce
new functionalities in 6G core (6GC) architecture that are
dynamic and resource-efficient. In ISAC, sensing signals are used by a Sensing Receiver (SRx) to measure and report Sensing Data Points (SDPs) to the network. However, a direct approach involving …
Research Paper /Mar 2025
This paper proposes a method that enhances the compression performance of the current model under development for the upcoming MPEG standard on Feature Compression for Machines (FCM).
This standard aims at providing inter-operable compressed bitstreams of features in the context of split computing, i.e., when the inference of a large computer vision Neural-Netwo…
Webinar /Jun 2024
Blog Post /Jun 2025
Blog Post /Jun 2025