TY - GEN
T1 - Big Data Viewpoint on Channel Information Measures Based on ACE Algorithm
AU - Liu, Shanyun
AU - She, Rui
AU - Lu, Jiaxun
AU - Fan, Pingyi
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/28
Y1 - 2018/8/28
N2 - In this paper, we focus on the mutual information, which can characterize the transmission ability because it shows correlation between channel input and channel output. Shannon entropy and mutual information are the cornerstones of information theory. In addition, Chernoff information is another fundamental channel information measure, and it describe the maximum achievable exponent of the error probability in hypothesis testing. Uased on alternating conditional expectation (ACE) algorithm, we decompose these two mutual information. In fact, their decomposition results are similar in big data prespective. In this sense, these two kinds of mutual information are just different measures of the same information quantity. This paper also deduces that the channel performance only depends on channel parameters and the decomposition results of a new proposed mutual information should agree with the impact of the parameters.
AB - In this paper, we focus on the mutual information, which can characterize the transmission ability because it shows correlation between channel input and channel output. Shannon entropy and mutual information are the cornerstones of information theory. In addition, Chernoff information is another fundamental channel information measure, and it describe the maximum achievable exponent of the error probability in hypothesis testing. Uased on alternating conditional expectation (ACE) algorithm, we decompose these two mutual information. In fact, their decomposition results are similar in big data prespective. In this sense, these two kinds of mutual information are just different measures of the same information quantity. This paper also deduces that the channel performance only depends on channel parameters and the decomposition results of a new proposed mutual information should agree with the impact of the parameters.
KW - ACE
KW - Big Data
KW - Chernoff Information
KW - Mutual information
KW - Shannon entropy
UR - https://www.scopus.com/pages/publications/85053899623
U2 - 10.1109/IWCMC.2018.8450424
DO - 10.1109/IWCMC.2018.8450424
M3 - 会议稿件
AN - SCOPUS:85053899623
SN - 9781538620700
T3 - 2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018
SP - 1530
EP - 1535
BT - 2018 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 14th International Wireless Communications and Mobile Computing Conference, IWCMC 2018
Y2 - 25 June 2018 through 29 June 2018
ER -