Sparsity Enhanced Mismatch Model for Robust Spatial Intercell Interference Cancelation in Heterogeneous Networks

Abstract

Performance of precoder-based spatial intercell interference cancelation in heterogeneous networks is often hampered due to lack of accurate channel state information. Performance can be augmented by modifying the design of the precoder to incorporate the channel estimate mismatch by using deterministic and probabilistic mismatch models. Previously proposed models either have been deemed too conservative (deterministic) or are prone to error due to inaccuracy in the probability distribution function and corresponding parameters (stochastic). A new deterministic mismatch model is proposed herein in an attempt to alleviate these problems. Different from all previously proposed deterministic models, the proposed model, called sparsity enhanced mismatch model (SEMM), exploits the inherent sparse characteristics of MIMO interference channels. The SEMM has two variants, i.e., SEMM (angular) and SEMM (eigenmode). The SEMM incorporates a basis expansion model to bring forth the inherent sparsity, which exists in MIMO interference channels. In the context of precoder design for heterogeneous network, it is analytically shown, and by simulation, the proposed mismatch models enable the aggressor-transmitter (A-Tx) to allocate more transmission power to the sparse elements of the interfering link so that performance in the communicating link is enhanced compared with conventional norm ball mismatch model.