
This paper presents a self-contained factorization for the delay Vandermonde matrix (DVM), which is the super class of the discrete Fourier transform, using sparse and companion matrices. An efficient DVM algorithm is proposed to reduce the complexity of radio-frequency (RF) $N$-beam analog beamforming systems. There exist applications for wideband multi-beam beamformers in wireless communication networks such as 5G/6G systems, system capacity can be improved by exploiting the improvement of the signal to noise ratio (SNR) using coherent summation of propagating waves based on their directions of propagation. The presence of a multitude of RF beams allows multiple independent wireless links to be established at high SNR, or used in conjunction with multiple-input multiple-output (MIMO) wireless systems, with the overall goal of improving system SNR and therefore capacity. To realize such multi-beam beamformers at acceptable analog circuit complexities, we use sparse factorization of the DVM in order to derive a low arithmetic complexity DVM algorithm. The paper also establishes an error bound and stability analysis of the proposed DVM algorithm. The proposed efficient DVM algorithm is aimed at implementation using analog realizations. For purposes of evaluation, the algorithm can be realized using both digital hardware as well as software defined radio platforms.
25 pages, 2 figures
Signal Processing (eess.SP), Antenna (radio), Computational Mechanics, FOS: Physical sciences, Applied Physics (physics.app-ph), Distributed Estimation, Quantum mechanics, Matrix decomposition, Engineering, Adaptive Filtering in Non-Gaussian Signal Processing, Beamforming, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, efficient algorithms, Mathematics - Numerical Analysis, complexity and performance of algorithms, Electrical Engineering and Systems Science - Signal Processing, Electrical and Electronic Engineering, approximation algorithms, Eigenvalues and eigenvectors, Signal-to-noise ratio (imaging), Iterative Decoding, Variable Step-Size Algorithms, Electronic engineering, Sparse System Identification, Physics, Physics - Applied Physics, Numerical Analysis (math.NA), Delay vandermonde matrix, Computer science, TK1-9971, Algorithm, MIMO, Millimeter Wave Communications for 5G and Beyond, wireless communications, Vandermonde matrix, Physical Sciences, Wideband, Wireless, Telecommunications, Electrical engineering. Electronics. Nuclear engineering, Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing, self-recursive algorithms
Signal Processing (eess.SP), Antenna (radio), Computational Mechanics, FOS: Physical sciences, Applied Physics (physics.app-ph), Distributed Estimation, Quantum mechanics, Matrix decomposition, Engineering, Adaptive Filtering in Non-Gaussian Signal Processing, Beamforming, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, efficient algorithms, Mathematics - Numerical Analysis, complexity and performance of algorithms, Electrical Engineering and Systems Science - Signal Processing, Electrical and Electronic Engineering, approximation algorithms, Eigenvalues and eigenvectors, Signal-to-noise ratio (imaging), Iterative Decoding, Variable Step-Size Algorithms, Electronic engineering, Sparse System Identification, Physics, Physics - Applied Physics, Numerical Analysis (math.NA), Delay vandermonde matrix, Computer science, TK1-9971, Algorithm, MIMO, Millimeter Wave Communications for 5G and Beyond, wireless communications, Vandermonde matrix, Physical Sciences, Wideband, Wireless, Telecommunications, Electrical engineering. Electronics. Nuclear engineering, Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing, self-recursive algorithms
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