This paper proposes a low-complexity wideband beamforming subarray for millimeter wave (mmW) 5G wireless communications. The multibeam subarray is based on using a novel delay Vandermonde matrix (DVM) algorithm to efficiently generate analog true-time-delay beams that have no beam squint. A factorization for the DVM leading to low-complexity analog realizations is provided and complexity analysis for real and complex inputs is derived. The DVM is a special case of a Vandermonde matrix but with complex nodes that lack any special properties (unlike the discrete Fourier transform matrix). Error bounds for the DVM are established and then analyzed for numerical stability. Mixed-signal CMOS integrated circuits designs are proposed for the implementation of DVM multibeam algorithms along with low-complexity digital realizations to achieve hybrid beamforming for mmW applications. Analog-digital hybrid mmW multibeam beamforming circuits and systems are designed, for example, with eight beams at 28 GHz and simulated in cadence for functional verification.

This paper presents a self-contained factorization for the Vandermonde matrices associated with true-time delay based wideband analog multi-beam beamforming using antenna arrays. The proposed factorization contains sparse and orthogonal matrices. Novel self-recursive radix-2 algorithms for Vandermonde matrices associated with true time delay based delay-sum filterbanks are presented to reduce the circuit complexity of multi-beam analog beamforming systems. The proposed algorithms for Vandermonde matrices by a vector attain O(N logN) delay-amplifier circuit counts. Error bounds for the Vandermode matrices associated with true-time delay are established and then analyzed for numerical stability. The potential for real-world circuit implementation of the proposed algorithms will be shown through signal flow graphs that are the starting point for high-frequency analog circuit realizations.

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.

This paper presents a sparse factorization for the delay Vandermonde matrix (DVM) along with fast, exact, radix-2, and recursive algorithms to compute the DVM-vector product for wideband multi-beam antenna arrays. The proposed algorithms enable low-complexity wideband beamformers in emerging millimeter-wave wireless communication networks by reducing the complexity of $N$ -beam wideband beamforming from $\mathcal {O}(N^{2})$ to $\mathcal {O}(N \mathrm{\: log\:} N)$ , where $N=2^{r}(r \geq 1)$ . As a result, the algorithms are faster than the brute-force computation of the DVM-vector product and more efficient than the direct realization of true-time-delay-based multi-beam beamformers. The proposed low-complexity algorithms' signal flow graph (SFG) is also presented to highlight their suitability for hardware implementations. The 2-D frequency responses of DVM-based beamformers are explained through an array signal processing example. Simulation results suggest that integrated circuit (IC) implementations of the SFG significantly reduce chip area and power consumption.

This paper introduces a collection of scaling methods for generating 2 N-point DCT-II approximations based on N-point low-complexity transformations. Such scaling is based on the Hou recursive matrix factorization of the exact 2 N-point DCT-II matrix. Encompassing the widely employed Jridi-Alfalou-Meher scaling method, the proposed techniques are shown to produce DCT-II approximations that outperform the transforms resulting from the JAM scaling method according to total error energy and mean squared error. Orthogonality conditions are derived and an extensive error analysis based on statistical simulation demonstrates the good performance of the introduced scaling methods. A hardware implementation is also provided demonstrating the competitiveness of the proposed methods when compared to the JAM scaling method.

This paper presents stable, radix-2, completely recursive discrete cosine transform algorithms DCT-I and DCT-III solely based on DCT-I, DCT-II, DCT-III, and DCT-IV having sparse and orthogonal factors. Error bounds for computing the completely recursive DCT-I, DCT-II, DCT-III, and DCT-IV algorithms having sparse and orthogonal factors are addressed. Signal flow graphs are demonstrated based on the completely recursive DCT-I, DCT-II, DCT-III, and DCT-IV algorithms having orthogonal factors. Finally image compression results are presented based on the recursive 2D DCT-II and DCT-IV algorithms for image size 512 by 512 pixels with transfer block sizes 8 by 8, 16 by 16, and 32 by 32 with 93.75% absence of coefficients in each transfer block.