Sparsity Enhanced Mismatch Model for Robust Intercell Interference Management in Heterogeneous Networks With Doubly-Selective Fading Channels

Abstract

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.