TY - GEN
T1 - An outer loop link adaptation for BICM-OFDM that learns
AU - Wahls, Sander
AU - Poor, H. Vincent
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Link adaptation
KW - Machine learning algorithms
KW - OFDM
KW - Unsupervised learning
UR - http://www.scopus.com/inward/record.url?scp=84885825446&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885825446&partnerID=8YFLogxK
U2 - 10.1109/SPAWC.2013.6612144
DO - 10.1109/SPAWC.2013.6612144
M3 - Conference contribution
AN - SCOPUS:84885825446
SN - 9781467355773
T3 - IEEE Workshop on Signal Processing Advances in Wireless Communications, SPAWC
SP - 719
EP - 723
BT - 2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2013
T2 - 2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2013
Y2 - 16 June 2013 through 19 June 2013
ER -