TY - GEN
T1 - Error Exponents of Typical Random Codes
AU - Merhav, Neri
N1 - Publisher Copyright: © 2018 IEEE.
PY - 2018/8/15
Y1 - 2018/8/15
N2 - We define the error exponent of the typical random code as the long-block limit of the negative normalized expectation of the logarithm of the error probability of the random code, as opposed to the traditional random coding error exponent, which is the limit of the negative normalized logarithm of the expectation of the error probability. For the ensemble of uniformly randomly drawn fixed composition codes, we provide exact error exponents of typical random codes for a general discrete memoryless channel (DMC) and a wide class of (stochastic) decoders, collectively referred to as the generalized likelihood decoder (GLD). This ensemble of fixed composition codes is shown to be no worse than any other ensemble of independent codewords that are drawn under a permutation-invariant distribution (e.g., i.i.d. codewords). We also present relationships between the error exponent of the typical random code and the ordinary random coding error exponent, as well as the expurgated exponent for the GLD. Finally, we demonstrate that our analysis technique is applicable also to more general communication scenarios, such as list decoding (for fixed-size lists) as well as decoding with an erasure/list option in Forney's sense. All proofs appear in the full version of this paper, https://arxiv.org/pdf/708.07301.pdf.
AB - We define the error exponent of the typical random code as the long-block limit of the negative normalized expectation of the logarithm of the error probability of the random code, as opposed to the traditional random coding error exponent, which is the limit of the negative normalized logarithm of the expectation of the error probability. For the ensemble of uniformly randomly drawn fixed composition codes, we provide exact error exponents of typical random codes for a general discrete memoryless channel (DMC) and a wide class of (stochastic) decoders, collectively referred to as the generalized likelihood decoder (GLD). This ensemble of fixed composition codes is shown to be no worse than any other ensemble of independent codewords that are drawn under a permutation-invariant distribution (e.g., i.i.d. codewords). We also present relationships between the error exponent of the typical random code and the ordinary random coding error exponent, as well as the expurgated exponent for the GLD. Finally, we demonstrate that our analysis technique is applicable also to more general communication scenarios, such as list decoding (for fixed-size lists) as well as decoding with an erasure/list option in Forney's sense. All proofs appear in the full version of this paper, https://arxiv.org/pdf/708.07301.pdf.
UR - http://www.scopus.com/inward/record.url?scp=85052452819&partnerID=8YFLogxK
U2 - 10.1109/ISIT.2018.8437827
DO - 10.1109/ISIT.2018.8437827
M3 - منشور من مؤتمر
SN - 9781538647806
T3 - IEEE International Symposium on Information Theory - Proceedings
SP - 1675
EP - 1679
BT - 2018 IEEE International Symposium on Information Theory, ISIT 2018
T2 - 2018 IEEE International Symposium on Information Theory, ISIT 2018
Y2 - 17 June 2018 through 22 June 2018
ER -