Spatial de-correlation of the UTRA-FDD radio channel

For smart antenna deployment in Frequency Division Duplex (FDD) applications the downlink weight vector can be derived from the uplink steering vector, assuming a degree of correlation between these channels. It is shown here that de-correlation between uplink and downlink power azimuth spectrums (PAS) is significant for frequency offsets as small as 5MHz in urban environments. Results reported here are computed from measured data taken within the UTRA (UMTS Terrestrial Radio Interface) frequency band for cluttered urba

Simulation of Multi-element Antenna Systems for Navigation Applications

The application of user terminals with multiple antenna inputs for use with the global satellite navigation systems like GPS and Galileo becomes more and more attraction in last years. Multiple antennas may be spread over the user platform and provide signals required for the platform attitude estimation or may be arranged in an antenna array to be used together with array processing algorithms for improving signal reception, e.g. for multipath and interference mitigation. In order to generate signals for testing of receivers with multiple antenna inputs and corresponding receiver algorithms in a laboratory environment a unique HW signal simulation tool for wavefront simulation has been developed. The signals for a number of antenna elements in a flexible user defined geometry are first generated as digital signals in baseband and then mixed up to individual RF-outputs. The paper describes the principle function of the system and addresses some calibration issues. Measurement set-ups and results of data processing with simulated signals for different applications are shown and discussed

Twenty-Five Years of Advances in Beamforming: From Convex and Nonconvex Optimization to Learning Techniques

Beamforming is a signal processing technique to steer, shape, and focus an electromagnetic wave using an array of sensors toward a desired direction. It has been used in several engineering applications such as radar, sonar, acoustics, astronomy, seismology, medical imaging, and communications. With the advances in multi-antenna technologies largely for radar and communications, there has been a great interest on beamformer design mostly relying on convex/nonconvex optimization. Recently, machine learning is being leveraged for obtaining attractive solutions to more complex beamforming problems. This article captures the evolution of beamforming in the last twenty-five years from convex-to-nonconvex optimization and optimization-to-learning approaches. It provides a glimpse of this important signal processing technique into a variety of transmit-receive architectures, propagation zones, paths, and conventional/emerging applications