Wireless Technology Recognition Based on RSSI Distribution at Sub-Nyquist Sampling Rate for Constrained Devices (original) (raw)

Wireless Technology Identification Employing Dynamic Mode Decomposition Modeling

2023

Significant growth in broadband wireless services, as well as ever-increasing demand on the spectrum caused by the Internet of Things (IoT) have overstretched limited available spectrum space for wireless services. Heterogeneous wireless networks (HetNets)-wherein multiple wireless technologies (e.g., Wi-Fi, Bluetooth, Zigbee, LTE, and GSM) coexist and share spectrum-are a promising solution for enhancing spectrum sharing. An essential element in developing coexistence protocols is correctly identifying wireless technologies anticipated to share spectrum and to shift users between available wireless technologies in an effort to optimize spectrum usage and minimize interference. For the coexistence research reported in this paper, we analyzed the performance of our developed novel algorithm based on dynamic mode decomposition (DMD) mathematical modeling to identify and differentiate among various wireless technologies. More specifically, our technique identified GSM and LTE signals in the cellular domain, IEEE802.11n, ac, and ax in the Wi-Fi domain, as well as Bluetooth and Zigbee. The proposed DMD-based technique identifies the time domain signature of a signal by capturing embedded periodic features transmitted within the signal. Performance and accuracy were tested and validated using an experimental dataset collected for various time series, and raw-power measurements of the targeted technologies. Results showed that the developed DMD-based algorithm can differentiate and classify individual and coexisting wireless signals with high accuracy-greater than 90% for most cases. Furthermore, only a short timeless than one second-is required for identifying a signal and enabling implementation in real-time practical networks. The advantage of the developed technique over comparable techniques is lower complexity (i.e., shorter processing and training time, no channel estimation, no time/frequency synchronization, and no need for long observation-time intervals).

Signal Classification and Identification for Cognitive Radio

IRJET, 2022

SDR (software-defined radio) devices have gotten a lot of interest lately because to their low cost and ease of use when it comes to hands-on testing. In cognitive radio (CR), they may be utilised to create dynamic spectrum allocation (DSA) algorithms. These CRs are currently unable to determine which DSA method is most suited for a given situation, despite much study in both machine learning and signal processing. Machine learning and statistical signal processing approaches may be used to compare the spectrum sensing algorithms for CRs and spectrum observatories in resource restricted contexts. We've decided to take on the issues of detecting multiple transmitters and automatically classifying modulation patterns (AMC). Multiple transmitter identification algorithms using machine learning and statistical signal processing are evaluated side by side. For multi-transmitter identification, the machine learning method has an accuracy of 70 percent and 80 percent for two and five user systems, respectively, while the statistical signal processing technique has an accuracy of 50 percent for two and five user systems, respectively. Machine learning beats the signal processing technique for 1000 test samples in AMC, even if both algorithms have 100% accuracy beyond 10 dB for 100 test samples (64-QAM is an exception). Signal processing techniques in both situations take a fraction of the time needed by machine learning algorithms, according to the time comparison.

A Nation-Wide Wi-Fi RSSI Dataset: Statistical Analysis and Resulting Insights

2020

We present a dataset collected during ten months from a network comprising approximately 9500 double-band Access Points (APs), corresponding to Uruguay’s nation-wide one-to-one computing program’s internet provider. The dataset includes the transmission power, used channel and measured RSSI (Radio Signal Strength Indicator) that each AP senses every other AP in sight, with a granularity of an hour. This results in a total of more than 750 million measurements, one of the largest Wi-Fi datasets to date.In the study of this dataset we have first focused on a link-level analysis. Our contributions are fourfold. We verify that approximately only half of the RSSI time-series are actually stationary, and that in that case, they present strong time correlations. Moreover, the typical assumption that the channel is symmetrical is not true, even in the long-term, and we show that interference plays an important role on this asymmetry. Finally, we study attenuation in the 5 GHZ band and show ...