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.

Transmission over doubly-selective fading (DSF) interference channel often relies on the use of robust precoder due to a lack of accurate channel state information, with performance often depending on the conservativeness of the mismatch model. Previously proposed mismatch models either have been deemed too conservative (deterministic models) or are prone to error due to inaccuracy in the probability density function (pdf) and corresponding parameters (stochastic models). A deterministic mismatch model called Sparsity Enhanced Mismatch Model - Reverse discrete prolate spheroidal sequence, or SEMMR, is proposed herein in an attempt to alleviate this problem. Different from all previously deterministic models, the proposed model exploits the inherent sparse characteristics of DSF interference channels which lead to a two-stage robust transceiver design that outperforms precoding only strategy incorporating conventional norm ball mismatch model (NBMM). The inherent sparsity in the channel is brought forth by modeling the channel using a basis expansion model (BEM) where discrete prolate spheroidal sequence (DPSS) is used as a basis. Analytical and simulation results are provided to validate the performance gains of the SEMMR transceiver over the NBMM precoder.