An integrated sparsity and model-based probabilistic framework for estimating the spatial variations of communication channels

In this paper, we consider estimating the spatial variations of a wireless channel, based on a small number of measurements. We propose an integrated sparsity and model-based channel prediction framework. Our approach properly takes advantage of both channel compressibility in the frequency domain and channel probabilistic characterization in the spatial domain. We test our framework using outdoor and indoor channel measurements. The results confirm the superior performance of the proposed integrated approach.