6 research outputs found
EC-GSM-IoT Network Synchronization with Support for Large Frequency Offsets
EDGE-based EC-GSM-IoT is a promising candidate for the billion-device
cellular IoT (cIoT), providing similar coverage and battery life as NB-IoT. The
goal of 20 dB coverage extension compared to EDGE poses significant challenges
for the initial network synchronization, which has to be performed well below
the thermal noise floor, down to an SNR of -8.5 dB. We present a low-complexity
synchronization algorithm supporting up to 50 kHz initial frequency offset,
thus enabling the use of a low-cost +/-25 ppm oscillator. The proposed
algorithm does not only fulfill the 3GPP requirements, but surpasses them by 3
dB, enabling communication with an SNR of -11.5 dB or a maximum coupling loss
of up to 170.5 dB.Comment: Wireless Communications and Networking Conference (WCNC), 201
Efficient and Accurate Frequency Estimation of Multiple Superimposed Exponentials in Noise
The estimation of the frequencies of multiple superimposed exponentials in
noise is an important research problem due to its various applications from
engineering to chemistry. In this paper, we propose an efficient and accurate
algorithm that estimates the frequency of each component iteratively and
consecutively by combining an estimator with a leakage subtraction scheme.
During the iterative process, the proposed method gradually reduces estimation
error and improves the frequency estimation accuracy. We give theoretical
analysis where we derive the theoretical bias and variance of the frequency
estimates and discuss the convergence behaviour of the estimator. We show that
the algorithm converges to the asymptotic fixed point where the estimation is
asymptotically unbiased and the variance is just slightly above the Cramer-Rao
lower bound. We then verify the theoretical results and estimation performance
using extensive simulation. The simulation results show that the proposed
algorithm is capable of obtaining more accurate estimates than state-of-art
methods with only a few iterations.Comment: 10 pages, 10 figure
An Open-Source LoRa Physical Layer Prototype on GNU Radio
LoRa is the proprietary physical layer (PHY) of LoRaWAN, which is a popular
Internet-of-Things (IoT) protocol enabling low-power devices to communicate
over long ranges. A number of reverse engineering attempts have been published
in the last few years that helped to reveal many of the LoRa PHY details. In
this work, we describe our standard compatible LoRa PHY software-defined radio
(SDR) prototype based on GNU Radio. We show how this SDR prototype can be used
to develop and evaluate receiver algorithms for LoRa. As an example, we
describe the sampling time offset and the carrier frequency offset estimation
and compensation blocks. We experimentally evaluate the error rate of LoRa,
both for the uncoded and the coded cases, to illustrate that our publicly
available open-source implementation is a solid basis for further research.Comment: GNU Radio source code available at:
https://tcl.epfl.ch/resources-and-sw/lora-phy
LOW SNR TRESHOLD IN RAPID ESTIMATORS OF COMPLEX SINUSOIDALS
A task of estimation of complex sinusoid frequency is considered. A particular but practically important case of low signal-to-noise ratio (SNR) is studied. The low SNR threshold, commonly overlooked in development of the rapid estimator of complex sinusoidals, is addressed. Signals of different length are considered and SNR is varied in wide limits. It is demonstrated that a simple interpolation with factor 2 lowers the SNR threshold by 1.5dB for the most complicated practical situations. Further interpolation does not bring any improvement. This allows proposing a compromise practical algorithm that provides accuracy close to the limit and is still very simple and fast
Estimating Frequency by Interpolation Using Least Squares Support Vector Regression
Discrete Fourier transform-(DFT-) based maximum likelihood (ML) algorithm is an important part of single sinusoid frequency estimation. As signal to noise ratio (SNR) increases and is above the threshold value, it will lie very close to Cramer-Rao lower bound (CRLB), which is dependent on the number of DFT points. However, its mean square error (MSE) performance is directly proportional to its calculation cost. As a modified version of support vector regression (SVR), least squares SVR (LS-SVR) can not only still keep excellent capabilities for generalizing and fitting but also exhibit lower computational complexity. In this paper, therefore, LS-SVR is employed to interpolate on Fourier coefficients of received signals and attain high frequency estimation accuracy. Our results show that the proposed algorithm can make a good compromise between calculation cost and MSE performance under the assumption that the sample size, number of DFT points, and resampling points are already known