Gaussianity Testing as an Effective Technique for Detecting Discontinuous Transmission in 5G Networks

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. INDEX TERMSGaussianity testing, energy detection, discontinuous transmission.

The novel approach to the closed-form average bit error rate calculation for the Nakagami-m fading channel

“…It can be pointed out that the solutions to a large number of modern wireless communications problems depend heavily on the normality assumption of some stochastic physical effects (e.g., reflection, multiple diffractions, fading); thus, the expressions with Gauss Q-function and its squared version naturally arise. An important example is the problem of signal detection with the help of the energy-based spectrum sensing procedure for ad hoc systems and networks, including polar-coding transmission for 5G [41], MIMO-OFDM Cognitive Radio Systems [42], listenbefore-talk procedure in 5G networks [43], etc.…”

Computationally Efficient Wideband Spectrum Sensing through Cumulative Distribution Function and Machine Learning

“…Referring to the study [29] is valuable to relate the results obtained to other alternative methods. In a similar impulse transmission scenario, the authors present the effectiveness of detection based on statistical approaches and relate them to the fundamental techniques of energy detection and its modification, utilizing short windows.…”

“…Comparison with previously studied approaches shows that the proposed solution, even with a short frame of 1024 samples, ensures better detectability than energy detection or direct sample distribution analysis. Reference to results obtained for 4096 samples in the frame indicates that statistical methods significantly benefit from increased frame length used to build the empirical distribution (for details, see [29]).…”