On the Use of Uncalibrated Digital Phased Arrays for Blind Signal Separation for Interference Removal in Congested Spectral Bands

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Virginia Tech


With usable spectrum becoming increasingly more congested, the need for robust, adaptive communications to take advantage of spatially-separated signal sources is apparent. Traditional phased array beamforming techniques used for interference removal rely on perfect calibration between elements and precise knowledge of the array configuration; however, if the exact array configuration is not known (unknown or imperfect assumption of element locations, unknown mutual coupling between elements, etc.), these traditional beamforming techniques are not viable, so a blind beamforming approach is required. A novel blind beamforming approach is proposed to address complex narrow-band interference environments where the precise array configuration is unknown. The received signal is decomposed into orthogonal narrow-band partitions using a polyphase filter-bank channelizer, and a rank-reduced version of the received matrix on each sub-channel is computed through reconstruction by retaining a subset of its singular values. The wideband spectrum is synthesized through a near-perfect polyphase reconstruction filter, and a composite wideband spectrum is obtained from the maximum eigenvector of the resulting covariance matrix.The resulting process is shown to suppress numerous interference sources (in special cases even with more than the degrees of freedom of the array), all without any knowledge of the primary signal of interest. Results are validated with both simulation and wireless laboratory over-the-air experimentation.



Beamforming, Digital Phased Arrays, Interference Suppression, Singular Value Decomposition