Adaptive LMS-type filter for cyclostationary signals

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations


Adaptive filters are employed in many signal pro- cessing and communications systems. Commonly, the design and analysis of adaptive algorithms, such as the least mean-squares (LMS) algorithm, is based on the assumptions that the signals are wide-sense stationary (WSS). However, in many cases, including, for example, interference-limited wireless communications and power line communications, the considered signals are jointly cyclostationary. In this paper we propose a new LMS-type algorithm for adaptive filtering of jointly cyclostationary signals using the time-averaged mean-squared error objective. When the considered signals are jointly WSS, the proposed algorithm specializes to the standard LMS algorithm. We characterize the performance of the algorithm without assuming specific distributions on the considered signals, and derive conditions for convergence. We then evaluate the performance of the proposed algorithm, called time-averaged LMS, in a simulation study of practical channel estimation scenarios. The results show a very good agreement between the theoretical and empirical performance measures.

Original languageEnglish
Title of host publicationISWCS 2016 - 13th International Symposium on Wireless Communication Systems, Proceedings
PublisherVDE Verlag GmbH
Number of pages5
ISBN (Electronic)9781509020614
StatePublished - 19 Oct 2016
Event13th International Symposium on Wireless Communication Systems, ISWCS 2016 - Poznan, Poland
Duration: 20 Sep 201623 Sep 2016

Publication series

NameProceedings of the International Symposium on Wireless Communication Systems
ISSN (Print)2154-0217
ISSN (Electronic)2154-0225


Conference13th International Symposium on Wireless Communication Systems, ISWCS 2016

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Communication


Dive into the research topics of 'Adaptive LMS-type filter for cyclostationary signals'. Together they form a unique fingerprint.

Cite this