TY - JOUR
T1 - Robust Power Allocation for Parallel Gaussian Channels with Approximately Gaussian Input Distributions
AU - Cao, Wei
AU - Dytso, Alex
AU - Faub, Michael
AU - Feng, Gang
AU - Poor, H. Vincent
N1 - Funding Information:
Manuscript received July 29, 2019; revised December 18, 2019; accepted February 17, 2020. Date of publication February 28, 2020; date of current version June 10, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61871099 and Grant 61631004, in part by the U.S. National Science Foundation under Grant CCF-0939370 and Grant CCF-1513915, and in part by the German Research Foundation (DFG) under Grant 424522268. This article was presented in part in [1]. The associate editor coordinating the review of this article and approving it for publication was Y. Liu. (Corresponding author: Wei Cao.) Wei Cao and Gang Feng are with the National Key Lab of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China, and also with the Center for Intelligent Networking and Communications, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: wcao@std.uestc.edu.cn; fenggang@uestc.edu.cn).
Publisher Copyright:
© 2002-2012 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - In both wired and wireless communication networks, power allocation is an important technique to improve system performance. This paper investigates the power allocation problem for parallel Gaussian channels from an information-theoretic perspective with the aim of maximizing the sum of mutual informations (i.e., an achievable data rate). If all the inputs are Gaussian, it is well-known that the waterfilling policy provides an optimal solution. For arbitrary input distributions, a generalization of waterfilling, so-called mercury/waterfilling, provides an optimal solution in terms of the minimum mean square errors (MMSEs). However, the difficulty of obtaining closed-form analytical expressions of the MMSE often makes computing the mercury/waterfilling solution challenging. This paper proposes a robust waterfilling power allocation (RPA) policy for parallel Gaussian channels when the input distributions are close to Gaussian distributions in the Kullback-Leibler (KL) divergence (relative entropy). First, it is shown that the proposed policy results in water-levels that are close to the optimal ones in a well-defined sense. Second, tight bounds for the loss in mutual information (data rate) are given. This bounded loss property makes the proposed power allocation policy robust and approximately optimal, which is illustrated by means of various simulation setups. Moreover, the RPA policy provides a general framework for solving the power allocation problem for parallel channels, with the classical waterfilling being included as a special case. Finally, the RPA policy is argued to be scalable with the number of users since it inherently uses the classical low complexity waterfilling.
AB - In both wired and wireless communication networks, power allocation is an important technique to improve system performance. This paper investigates the power allocation problem for parallel Gaussian channels from an information-theoretic perspective with the aim of maximizing the sum of mutual informations (i.e., an achievable data rate). If all the inputs are Gaussian, it is well-known that the waterfilling policy provides an optimal solution. For arbitrary input distributions, a generalization of waterfilling, so-called mercury/waterfilling, provides an optimal solution in terms of the minimum mean square errors (MMSEs). However, the difficulty of obtaining closed-form analytical expressions of the MMSE often makes computing the mercury/waterfilling solution challenging. This paper proposes a robust waterfilling power allocation (RPA) policy for parallel Gaussian channels when the input distributions are close to Gaussian distributions in the Kullback-Leibler (KL) divergence (relative entropy). First, it is shown that the proposed policy results in water-levels that are close to the optimal ones in a well-defined sense. Second, tight bounds for the loss in mutual information (data rate) are given. This bounded loss property makes the proposed power allocation policy robust and approximately optimal, which is illustrated by means of various simulation setups. Moreover, the RPA policy provides a general framework for solving the power allocation problem for parallel channels, with the classical waterfilling being included as a special case. Finally, the RPA policy is argued to be scalable with the number of users since it inherently uses the classical low complexity waterfilling.
KW - Power allocation
KW - approximately Gaussian input
KW - robust
KW - waterfilling
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U2 - 10.1109/TWC.2020.2975784
DO - 10.1109/TWC.2020.2975784
M3 - Article
AN - SCOPUS:85076811546
SN - 1536-1276
VL - 19
SP - 3685
EP - 3699
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
IS - 6
M1 - 9018391
ER -