Steering Matrices Research Articles

A General Coupling-Based Model Framework for Wideband MIMO Channels

A general coupling-based model framework for wideband multiple-input multiple-output (MIMO) channels is presented in this paper. Under this framework, the channel state information (CSI) tensor can be expressed by the product of a coupling tensor, a complex Gaussian tensor and three unitary matrices. The unitary matrices can be either eigenbases or steering matrices in different domains. The coupling tensor reflects the relationship between the column vectors of these unitary matrices. The complex Gaussian tensor is used to describe the small-scale fading. Several realizations of this framework are introduced, including the wideband Kronecker-based (WKB) model, the wideband eigenvalue-decomposition-based (WEB) model, the wideband virtual presentation (WVP) model and the wideband hybrid (WHY) model. To evaluate the performance of these models, channel measurements were carried out in different indoor scenarios both at Tsinghua University and Lund University. The results show that these models have good agreement with the measured data. Furthermore, we can see that the WHY model can provide a tradeoff between complexity and accuracy in channel synthesis.

Nonparametric Missing Sample Spectral Analysis and Its Applications to Interrupted SAR

We consider nonparametric adaptive spectral analysis of complex-valued data sequences with missing samples occurring in arbitrary patterns. We first present two high-resolution missing-data spectral estimation algorithms: the Iterative Adaptive Approach (IAA) and the Sparse Learning via Iterative Minimization (SLIM) method. Both algorithms can significantly improve the spectral estimation performance, including enhanced resolution and reduced sidelobe levels. Moreover, we consider fast implementations of these algorithms using the Conjugate Gradient (CG) technique and the Gohberg-Semencul-type (GS) formula. Our proposed implementations fully exploit the structure of the steering matrices and maximize the usage of the fast Fourier transform (FFT), resulting in much lower computational complexities as well as much reduced memory requirements. The effectiveness of the adaptive spectral estimation algorithms is demonstrated via several numerical examples including both 1-D spectral estimation and 2-D interrupted synthetic aperture radar (SAR) imaging examples.