Optimal design of constant-modulus channel training sequences
Zhongju Wang, Prabhu Babu, Daniel P. Palomar
Abstract
Unimodular sequences have been widely used in communications and radars, for which some numerical algorithms have been proposed recently to obtain good autocorrelation properties [1,2]. Design of such "good" sequences, however, does not take into account any prior information of the channel to be estimated. Although shaping the autocorrelation of a training sequence may imply a good performance, it may be advantageous to directly optimize the performance measure of interest. In this paper, we consider the problem of optimal constant-modulus training sequence design for MMSE estimation of the channel impulse response and conditional mutual information maximization. Efficient iterative algorithms based on the majorization-minorization framework are proposed for each formulation. Numerical examples show that our proposed training sequences achieve better performances than that of low sidelobes or random phases.
BibTeX
@inproceedings{icassp2016_optimaldesignofc,
title = {Optimal design of constant-modulus channel training sequences},
author = {Zhongju Wang and Prabhu Babu and Daniel P. Palomar},
booktitle = {ICASSP 2016},
year = {2016}
}