Super-Exponential Blind Adaptive Beamforming

Abstract

Abstract-The objective of the beamforming with the exploitation of a sensor array is to enhance the signals of the sources from desired directions, suppress the noises and the interfering signals from other directions, and/or simultaneously provide the localization of the associated sources. In this paper, we present a higher order cumulant-based beamforming algorithm, namely, the super-exponential blind adaptive beamforming algorithm, which is extended from the super-exponential algorithm (SEA) and the inverse filter criteria (IFC). While both SEA and IFC assume noise-free conditions, this requirement is no longer needed, and all the noise components are taken into account in the proposed algorithm. Two special conditions are derived under which the proposed blind beamforming algorithm achieves the performance of the corresponding optimal nonblind beamformer in the sense of minimum mean square error (MMSE). Simulation results show that the proposed algorithm is effective and robust to diverse initial weight vectors; its performance with the use of the fourth-order cumulants is close to that of the nonblind optimal MMSE beamformer.Index Terms-Adaptive array processing, blind beamforming, high-order cumulants, inverse filter criteria (IFC), super-exponential algorithm (SEA).

Semi-blind adaptive beamforming for high-throughput quadrature amplitude modulation systems

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Constant modulus algorithm aided soft decision directed scheme for blind space–time equalisation of SIMO channels

“…For high-order QAM signalling over frequency-selective channels, however, the complexity of the blind DW-ILSP algorithm may become prohibitive. In the context of blind beamforming, the CMA-type blind adaptive algorithm has been used before in [24][25][26][27]. However, the combined CMA and SDD (or DD) scheme has not been employed for blind adaptive SIMO systems, especially not in conjunction with high-order QAM schemes.…”

Recursive least squares semi-blind beamforming for MIMO using decision directed adaptation and constant modulus criterion

“…However, pure training-based schemes require a high training overhead, thus considerably reducing the achievable system throughput. Pure blind beamforming [29,30,31,32,33] does not reduce the achievable system throughput at the expense of high computational complexity and slow convergence. Moreover, blind beamforming results in unavoidable estimation and decision ambiguities [34] .…”