Abstract

The paper investigates statistical distribution testing-based detection methods in an intermittent signal detection scenario. The relevance of the research is driven by 5G networks based on packet transmission, incorporating the concept of cognitive radio and adapting spectrum detection methods from Long Term Evolution (LTE) Licensed-Assisted Access (LAA). The conducted study refers to the recently proposed methods based on testing goodness-of-fit (GoF) of statistical distributions, which are compared with a conventional energy detector. The authors examine the applicability of well-known GoF methods in intermittent transmission, as they require reconsideration in 5G communication systems, and investigate the behavior of the innovative energy-based GoF. The experiments are carried out for different transmitter activity factors, i.e., channel occupancy and signal-to-noise ratio (SNR), demonstrating the superiority of the GoF-based methods in general and particularly the invented GoF test over other energy-based detectors for discontinuous signals detection.

Highlights

  • In recent years, many technologies have been developed for wireless systems based on the IEEE 802.15.4 and IEEE 802.11 standards

  • As we evaluate them as an alternative to solutions based on an energy detector, in a comparative study we combine the above techniques with the adaptation of energy detection to intermittent transmission proposed in [21]

  • Due to processing performed on a finite number of samples, the authors provide an expression describing the fluctuations of the field of tails with respect to M and N, which follows the Gaussian distribution with parameters

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Summary

INTRODUCTION

Many technologies have been developed for wireless systems based on the IEEE 802.15.4 and IEEE 802.11 standards. The correct operation of a modified energy detector in a dynamic radio channel requires prior gathering of information on transmitter activity, such as average and current signal duration, channel occupancy, and signal and noise parameters, such as noise variance and signal-to-noise ratio (SNR) In this case, the energy detector, which was intended as a light and simple solution, is excessively overloaded with the determination of auxiliary parameters. As a group of techniques operating on a single assumption of the expected noise distribution, they meet the requirements of unknown signals detection In such a case, the use of GoF and in particular Gaussianity and normality testing methods needs to be reconsidered.

ENERGY-BASED DETECTORS
GOODNESS-OF-FIT TESTING
SIMULATION AND NUMERICAL RESULTS
Findings
DISCUSSION
CONCLUSION
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