Transmission over flat-fading multiple-input and multiple-output (MIMO) and doubly selective fading single-input and single-output (SISO) interference channels often relies on the use of a robust precoder due to a lack of accurate channel state information, with performance often depending on the conservativeness of the mismatch model. In the literature, cognitive small cells using such a transmission scheme have been proposed for use in heterogeneous networks, which can provide increased cell density with minimum effect from interference. Cognitive sensing and transmission are keys in enabling cognitive small cells. Inaccuracy in sensing can lead to performance degradation during transmission. An overview about sensing technologies will be briefly provided in this chapter. An extensive exposition of robust transmission techniques will then follow, which utilize a new channel mismatch model called sparsity- enhanced mismatch model (SEMM). 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). The SEMM, and its derivative SEMM-Reverse discrete prolate spheroidal sequence (SEMMR), will be shown herein in an attempt to alleviate this problem. Different from all previously proposed deterministic models, the SEMM and SEMMR exploit the inherent sparse characteristics of MIMO and doubly selective fading interference channels that lead to the SEMM precoder and SEMMR transceiver, which outperform previously proposed precoding's only strategy of incorporating the conventional norm ball mismatch model (NBMM).
|主出版物標題||Bio-Inspired Computation in Telecommunications|
|出版狀態||Published - 6 2月 2015|