“Orthogonalized” sparsity enhanced mismatch models for wireless heterogeneous networks

Performance of MIMO precoder for heterogeneous networks can be hindered by a lack of accurate channel state information. The sparsity enhanced mismatch model (SEMM) has been proposed recently to account for the channel estimate mismatch problem by exploiting the inherent sparse characteristics of MIMO interference channels. When (single user-MIMO) SU-MIMO precoder design takes into account the SEMM, it was shown to have better transmission performance compared to the conventional norm ball mismatch model (NBMM) in a single-user multi-victims scenario. However, when communicating and interference channels are highly correlated, which can happen frequently in ultra-dense heterogeneous networks, performance of the SEMM precoder degrades and in some cases, underperforms the NBMM precoder. An “orthogonalized” SEMM (OSEMM) is proposed herein to modify the SEMM such that it is better suited for correlated channels. The concept of orthogonalization of channels is not new but this work uses it to enhance the SEMM, which creates synergy between transmission performance and robustness toward channel mismatch error. Two variants of the OSEMM are proposed, namely the OSEMM-LQ and OSEMM-SVD, to modify the basis expansion model that is an integral part of the SEMM. The resulting mismatch model influences the design of the SU-MIMO precoder that aims to maximize certain transmission criterion. Even though SU-MIMO precoding is considered, a new channel correlation definition, which acts as a metric for the OSEMM, is given that allows for user selection in a multiuser scenario such that optimal performance can be attained for the targeted user. Analytical and simulation results are given that highlight the difference in performance between the two variants of the OSEMM.