M-CMA for digital signal processing adaptive antennas with microwave beamforming

Abstract

Abstrucr This paper proposes a novel algorithm, called M-CMA (Modified Constant Modulus algorithm), which is unparalleled to give adaptability to microwave beamforming phased array antennas. Since microwave analog beamformers basically require much less RF devices than digital beamformer, the microwave analog beamformers based on M-CMA, that is adaptive microwave beamformers, can be cheaply fabricated. Therefore, they are much suitable for mobile communication systems where both miniaturization and low-cost are required for the equipment. Actually, M-CMA obtains the gradient vector by perturbing the microwave beamforming control voltage. Though M-CMA is implemented with a digital signal processor, M-CMA controls the microwave analog beamformer by utilizing the gradient vector. The microwave analog beamformer based on M-CMA is analyzed to have the following characteristics. (1) The beamformer can point its main beam to the desired direction in AWGN (Additive White Gaussian Noise) channels. (2) Although the beamformer has possibility to fail in ill solutions in CCI (CO-Channel Interference) channels, M-CMA can converge to the optimum solution when the desired direction is roughly a priori known.

Fast Adaptive Microwave Beamforming Using Array Signal Estimation

“…In (13) is a matrix with all columns equal to and is a matrix with columns equal to the perturbation vectors. Using (12) and assuming that is invertible, is then given by (15) In (15) when approaches a null matrix, can be calculated from . By increasing the correlation coefficients in (14), approaches a null matrix.…”

“…This prohibits direct use of existing adaptive beamforming algorithms which are devised for multiport structures. Perturbation techniques have been proposed to enable MBF structures to employ multiport adaptive beamforming in [9]- [15], [13]. In [9] a perturbation algorithm is proposed which for an -element array uses perturbations to find the gradient estimate in each iteration for the constrained least mean square (CLMS) algorithm.…”