Link adaptation for BICM-OFDM through adaptive kernel regression

Sander Wahls, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

The packet error rate (PER) of wireless BICM-OFDM systems is notoriously difficult to predict analytically. This remains true even if all subcarriers use a common modulation and coding scheme (MCS). Link adaptation, which here shall be understood as the process of adapting the MCS in order to maximize goodput, therefore remains a major challenge. Non-parametric learning is an elegant way to evade the lack of robust analytical models. Learning from multidimensional features is particularly interesting because one-dimensional features can characterize frequency-selective channels only roughly. However, most of the literature discusses methods that are not truly online. Either the computational costs become unbearable over time or the method saturates and effectively stops learning. The modified k nearest neighbors algorithm (k-NN) seems to be the only exception currently. However, k-NN has well-known weaknesses in learning from small sample sets. Two adaptive kernel regression (AKR) methods are therefore proposed instead. Simulation results are reported for a setup in which several practically relevant conditions that have been mostly ignored in previous studies using multidimensional features (imperfect channel knowledge, Doppler shift, feedback delay, collisions) are modeled.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages5136-5140
Number of pages5
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Link adaptation
  • Machine learning algorithms
  • OFDM
  • Unsupervised learning
  • Wireless communication

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  • Cite this

    Wahls, S., & Poor, H. V. (2013). Link adaptation for BICM-OFDM through adaptive kernel regression. In 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings (pp. 5136-5140). [6638641] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). https://doi.org/10.1109/ICASSP.2013.6638641