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The task of rain detection, or wet-dry classification
using measurements from commercial microwave links (CMLs)
is a subject that been studied in depth. However, these studies
are based on direct measurement of the signal level, which
is known to be attenuated by rain. In this paper we present,
for the first time an empirical study on rain classification using
records of transmissions errors in the CMLs. Based on a dataset
of measurements taken from operational cellular backhaul
networks and meteorological measurements, and using long

To strike a balance between energy efficiency and data quality control, this paper proposes a Neyman-Pearson type sensor censoring scheme for distributed sparse signal recovery via compressive-sensing based on wireless sensor networks. In the proposed approach, each sensor node employs a sparse sensing vector with known support for data compression, meanwhile enabling making local inference about the unknown support of the sparse signal vector of interest.

One of the fundamental problems for hybrid beamformer design in MIMO systems is that, in Singular-Value-Decomposition (SVD) based beamforming, the analog beamformer is limited to constant modulus. In order to solve this issue, an algorithm is proposed in this paper. Specifically, the parallel data streams are expressed mathematically, and then the math expression of the analog beamformer is achieved through maximizing the power of desired data streams while minimizing the power of interference.