TY - GEN
T1 - Capacity of the vector Gaussian channel in the small amplitude regime
AU - Dytso, Alex
AU - Poor, H. Vincent
AU - Shitz, Shlomo Shamai
N1 - Publisher Copyright:
© 2018 IEEE Information Theory Workshop, ITW 2018. All rights reserved.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - This paper studies the capacity of an ndimensional vector Gaussian noise channel subject to the constraint that an input must lie in the ball of radius R centered at the origin. It is known that in this setting the optimizing input distribution is supported on a finite number of concentric spheres. However, the number, the positions and the probabilities of the spheres are generally unknown. This paper characterizes necessary and sufficient conditions on the constraint R such that the input distribution supported on a single sphere is optimal. The maximum R¯n, such that using only a single sphere is optimal, is shown to be a solution of an integral equation. Moreover, it is shown that R¯n scales as √n and the exact limit of √R¯n/ √n is found.
AB - This paper studies the capacity of an ndimensional vector Gaussian noise channel subject to the constraint that an input must lie in the ball of radius R centered at the origin. It is known that in this setting the optimizing input distribution is supported on a finite number of concentric spheres. However, the number, the positions and the probabilities of the spheres are generally unknown. This paper characterizes necessary and sufficient conditions on the constraint R such that the input distribution supported on a single sphere is optimal. The maximum R¯n, such that using only a single sphere is optimal, is shown to be a solution of an integral equation. Moreover, it is shown that R¯n scales as √n and the exact limit of √R¯n/ √n is found.
UR - http://www.scopus.com/inward/record.url?scp=85062086222&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062086222&partnerID=8YFLogxK
U2 - 10.1109/ITW.2018.8613508
DO - 10.1109/ITW.2018.8613508
M3 - Conference contribution
AN - SCOPUS:85062086222
T3 - 2018 IEEE Information Theory Workshop, ITW 2018
BT - 2018 IEEE Information Theory Workshop, ITW 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE Information Theory Workshop, ITW 2018
Y2 - 25 November 2018 through 29 November 2018
ER -