TY - GEN

T1 - A beta-beta achievability bound with applications

AU - Yang, Wei

AU - Collins, Austin

AU - Durisi, Giuseppe

AU - Polyanskiy, Yury

AU - Poor, H. Vincent

N1 - Funding Information:
This work was supported in part by the US National Science Foundation (NSF) under Grants CCF-1420575 and ECCS-1343210, by the Swedish Research Council, under grant 3222452, by the Center for Science of Information (CSoI), an NSF Science and Technology Center, under grant agreement CCF- 09-39370, and by the NSF CAREER award CCF-12-53205
Publisher Copyright:
© 2016 IEEE.

PY - 2016/8/10

Y1 - 2016/8/10

N2 - A channel coding achievability bound expressed in terms of the ratio between two Neyman-Pearson β functions is proposed. This bound is the dual of a converse bound established earlier by Polyanskiy and Verdú (2014). The new bound turns out to simplify considerably the analysis in situations where the channel output distribution is not a product distribution, for example due to a cost constraint or a structural constraint (such as orthogonality or constant composition) on the channel inputs. Connections to existing bounds in the literature are discussed. The bound is then used to derive 1) the channel dispersion of additive non-Gaussian noise channels with random Gaussian codebooks, 2) the channel dispersion of an exponential-noise channel, 3) a second-order expansion for the minimum energy per bit of an additive white Gaussian noise channel, and 4) a lower bound on the maximum coding rate of a multiple-input multiple-output Rayleigh-fading channel with perfect channel state information at the receiver, which is the tightest known achievability result.

AB - A channel coding achievability bound expressed in terms of the ratio between two Neyman-Pearson β functions is proposed. This bound is the dual of a converse bound established earlier by Polyanskiy and Verdú (2014). The new bound turns out to simplify considerably the analysis in situations where the channel output distribution is not a product distribution, for example due to a cost constraint or a structural constraint (such as orthogonality or constant composition) on the channel inputs. Connections to existing bounds in the literature are discussed. The bound is then used to derive 1) the channel dispersion of additive non-Gaussian noise channels with random Gaussian codebooks, 2) the channel dispersion of an exponential-noise channel, 3) a second-order expansion for the minimum energy per bit of an additive white Gaussian noise channel, and 4) a lower bound on the maximum coding rate of a multiple-input multiple-output Rayleigh-fading channel with perfect channel state information at the receiver, which is the tightest known achievability result.

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U2 - 10.1109/ISIT.2016.7541783

DO - 10.1109/ISIT.2016.7541783

M3 - Conference contribution

AN - SCOPUS:84985919284

T3 - IEEE International Symposium on Information Theory - Proceedings

SP - 2669

EP - 2673

BT - Proceedings - ISIT 2016; 2016 IEEE International Symposium on Information Theory

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE International Symposium on Information Theory, ISIT 2016

Y2 - 10 July 2016 through 15 July 2016

ER -