TY - GEN
T1 - Channel Input Adaptation via Natural Type Selection
AU - Tridenski, Sergey
AU - Zamir, Ram
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/15
Y1 - 2018/8/15
N2 - We propose an on-line algorithm for adapting the input of an unknown or slowly varying channel, while keeping reliable communication at some fixed rate R during the adaptation process. The purpose of the algorithm is to push the generating distribution of an i.i.d. random code toward the input that achieves the channel capacity C. The algorithm uses one bit feedback per each transmission block, that acknowledges whether the decoded codeword crossed some pre-determined threshold T > R, with respect to some 'fitness' metric. In the rare event of threshold crossing, the encoder and decoder update the input distribution according to the type of the current codeword, while the decoder updates the fitness metric. We show that for a large block length, this algorithm simulates computation of the channel correct-decoding exponent, and it leads to the capacity-achieving input if we set T = C.1
AB - We propose an on-line algorithm for adapting the input of an unknown or slowly varying channel, while keeping reliable communication at some fixed rate R during the adaptation process. The purpose of the algorithm is to push the generating distribution of an i.i.d. random code toward the input that achieves the channel capacity C. The algorithm uses one bit feedback per each transmission block, that acknowledges whether the decoded codeword crossed some pre-determined threshold T > R, with respect to some 'fitness' metric. In the rare event of threshold crossing, the encoder and decoder update the input distribution according to the type of the current codeword, while the decoder updates the fitness metric. We show that for a large block length, this algorithm simulates computation of the channel correct-decoding exponent, and it leads to the capacity-achieving input if we set T = C.1
UR - http://www.scopus.com/inward/record.url?scp=85052431883&partnerID=8YFLogxK
U2 - 10.1109/ISIT.2018.8437624
DO - 10.1109/ISIT.2018.8437624
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AN - SCOPUS:85052431883
SN - 9781538647806
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 226
EP - 230
BT - 2018 IEEE International Symposium on Information Theory, ISIT 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Symposium on Information Theory, ISIT 2018
Y2 - 17 June 2018 through 22 June 2018
ER -