Adaptive thresholding algorithm for detection of coded signals in narrowband powerline communication systems

ABSTRACT

Powerline Communication (PLC) signals are affected by a combination of different sources of noise. Among them, impulse noise represents the most harmful component, making the use of encoding algorithms an essential requirement. Algorithms based on Complementary Sequences (CS) are an attractive option for signal detection in conditions of low signal-to-noise ratios (SNR). One of the main characteristics of CS is the high SNR gain by computing the sum of their autocorrelation functions, which, in a noiseless scenario, results in a Kronecker delta. In the presence of noise, the amplitude of main deltas is no longer proportional to the length of the sequences, and a variable number of random amplitude sidelobes emerge around it. This work analyses some of the algorithms present in the bibliography and proposes a novel dynamic algorithm to detect the correlation deltas immersed in the PLC impulse noise. The proposal is based on the estimation of the noise variance and the correlation peaks in order to compute a near-optimal validation threshold. The proposal is simulated and evaluated in a PLC environment with a Middleton Class A noise model, and the algorithm performance is compared in a real communication test over the electrical network of the university building.

KEYWORDS:

• Powerline communications
• thresholding
• peak detection
• coded communications

Acknowledgments

This work was supported in part by the Universidad Nacional de Mar del Plata (UNMDP), the Universidad Nacional de Misiones (UNAM), Argentina, the Consejo Nacional de Investigaciones Científicas y Tecnológicas (CONICET), Argentina, by the Ministerio de Ciencia, Tecnología e Innovación Productiva (MINCYT), Argentina, and by the Agencia Nacional de Promoción Científica y Tecnológica (ANPCYT), Argentina.

Disclosure of potential conflicts of interest

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Sergio Moya

Sergio E. Moya was born in Argentina in 1986.He received the B.S. degree in electronics engineering from Universidad Nacional de Misiones (UNM, Argentina) in 2011, and the Ph.D. degree in electronics from Universidad Nacional de Mar del Palta (UNMDP, Argentina) in 2016. He is currently professor at the UNM and his research topics include digital signal processing and device development for environmental conservation.

Matías Hadad

Matías N. Hadad was born in Argentina in 1985. He was awarded his B.S. degree in Electronics Engineering by the Universidad Nacional de Mar del Plata (UNMDP, Argentina) in 2010, and his Ph.D. in Electronics by same university in 2013.He is member of the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina, and is currently with the Instituto de Investigaciones Científicas y Tecnológicas en Electrónica (ICYTE). His research interests include signal processing, power line communications and smart grids.

Patricio Donato

Patricio G. Donato was born in Argentina in 1975. He received the B.S. degree in electronics engineering from Universidad Nacional de la Patagonia San Juan Bosco (UNPSJB, Argentina) in 2000, and the Ph.D. degree in electronics from Universidad de Alcalá (UAH, Spain), in 2005. He is member of the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina, and is currently with the Instituto de Investigaciones Científicas y Tecnológicas en Electrónica (ICYTE). His current research interests include smart grids, power quality and signal processing.

Marcos Funes

Marcos A. Funes was born in Argentina in 1974. He was awarded his B.S. degree in Electronics Engineering by the Universidad Nacional de Mar del Plata (UNMDP, Argentina) in 1999 and his Ph.D. in Electronics by same university in 2007. He is currently with the Department of Electronics of UNMDP and member of the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) from 2009. His current research interests include digital signal processing, microgrids and smart grids.