• DocumentCode
    3228179
  • Title

    An outer loop link adaptation for BICM-OFDM that learns

  • Author

    Wahls, Sander ; Poor, H. Vincent

  • Author_Institution
    Dept. of Electr. Eng., Princeton Univ., Princeton, NJ, USA
  • fYear
    2013
  • fDate
    16-19 June 2013
  • Firstpage
    719
  • Lastpage
    723
  • Abstract
    Wireless BICM-OFDM systems usually perform some link adaptation procedure in order to adapt their transmission parameters to the changing channel. It is common practice to choose modulation and code rate based on thresholds on the signal-to-noise ratios (inner loop link adaptation), while these thresholds are shifted in an external control loop (outer loop link adaptation). This paper proposes a new approach for adjusting the threshold offset. Adaptive kernel regression is used in order to learn the relationship between offsets, the channel state, and packet error rates for each code rate in an online fashion. The proposed algorithm exploits this knowledge when selecting offsets. This is in contrast to current approaches, which do not anticipate the effect of changes to the offset but rely on probing only. Another advantage is that frequency-selective modulation can (but does not have to) be employed. Some less-known arguments in favor of frequency-selective modulation are pointed out.
  • Keywords
    OFDM modulation; channel coding; error statistics; frequency modulation; interleaved codes; modulation coding; radio links; regression analysis; telecommunication computing; unsupervised learning; adaptive kernel regression; bit interleaved coded modulation; channel state; code rate; external control loop; frequency selective modulation; inner loop link adaptation; machine learning; orthogonal frequency division multiplexing; outer loop link adaptation; packet error rate; signal to noise ratio; threshold offset; transmission parameter; wireless BICM-OFDM system; Adaptation models; Kernel; Modulation; OFDM; Signal processing algorithms; Signal to noise ratio; Wireless communication; Link adaptation; Machine learning algorithms; OFDM; Unsupervised learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing Advances in Wireless Communications (SPAWC), 2013 IEEE 14th Workshop on
  • Conference_Location
    Darmstadt
  • ISSN
    1948-3244
  • Type

    conf

  • DOI
    10.1109/SPAWC.2013.6612144
  • Filename
    6612144