A Fast DVM Algorithm for Wideband Time-Delay Multi-Beam Beamformers

Perera, Sirani M.; Lingsch, Levi; Madanayake, Arjuna; Mandal, Soumyajit; Mastronardi, Nicola

doi:10.1109/tsp.2022.3231182

Cited by 2 publications

(13 citation statements)

References 49 publications

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“…To overcome the above challenge, we proposed an

DVM algorithm having sparse factors to achieve multi-directional TTD wideband beamforming to solve the longstanding beam squint problem in [ 11 ]. In [ 11 ], the “multiplication counts” in the frequency domain—albeit in the analog domain—are a combined result of analog gains (amplifiers) and time delays in the SFG when realized in a time-domain circuit.…”

Section: Review: Analog Dvm Beamformersmentioning

confidence: 99%

“…We obtain an approximated transfer function for the digital all-pass Thiran fractional delay filter. Thus, with this and from the prior work on DVM [ 7 , 8 , 9 , 10 , 11 ], we propose a low-complexity wideband TTD digital DVM algorithm using Thiran fractional delays.…”

Section: Digital Dvm Beamfomers Using Thiran Filtersmentioning

confidence: 99%

“…Typically, for N elements, N -independent beams can be realized by considering a short duration time delay

, where T is the time taken for the radio signal to propagate between two neighboring elements in the aperture. The parallel realization of N beams using a network of TTDs with durations set to integer multiples of

leads to an N -beam TTD multi-beam beamformer with an underlying mathematical framework given by the DVM [ 7 , 8 , 9 , 10 , 11 ]. The DVM is similar in concept to a spatial DFT; however, unlike the case of the DFT which uses a fixed complex coefficient in the matrix–vector operation, the DVM uses frequency-dependent weights defined as integer multiples of the smallest TTD component

…”

Section: Introductionmentioning

confidence: 99%

“…The analog realization of low-complexity and/or radix-2 DVM algorithms was previously discussed in references [ 7 , 8 , 9 , 10 , 11 ]. The structure of the DVM offers a framework for analyzing TTD-based wideband multi-beam beamforming [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

“…For example, wideband signals applied to DFT-based multi-beams lead to an undesirable “beam squint” effect. The DVM entries, referred to as

in [ 7 , 8 , 9 , 10 , 11 ], serve as the foundation for a TTD-based beamformer. It is important to note that the DFT matrix equals the matrix

at a single temporal frequency because the DVM covers a wide range of multiple temporal frequencies.…”

Section: Introductionmentioning

confidence: 99%

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Thiran Filters for Wideband DSP-Based Multi-Beam True Time Delay RF Sensing Applications

Perera,

Rathnasekara,

Madanayake

2024

Sensors

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The ability to sense propagating electromagnetic plane waves based on their directions of arrival (DOAs) is fundamental to a range of radio frequency (RF) sensing, communications, and imaging applications. This paper introduces an algorithm for the wideband true time delay digital delay Vandermonde matrix (DVM), utilizing Thiran fractional delays that are useful for realizing RF sensors having multiple look DOA support. The digital DVM algorithm leverages sparse matrix factorization to yield multiple simultaneous RF beams for low-complexity sensing applications. Consequently, the proposed algorithm offers a reduction in circuit complexity for multi-beam digital wideband beamforming systems employing Thiran fractional delays. Unlike finite impulse response filter-based approaches which are wideband but of a high filter order, the Thiran filters trade usable bandwidth in favor of low-complexity circuits. The phase and group delay responses of Thiran filters with delays of a fractional sampling period will be demonstrated. Thiran filters show favorable results for sample delay blocks with a temporal oversampling factor of three. Thiran fractional delays of orders three and four are mapped to Xilinx FPGA RF-SoC technologies for evaluation. The preliminary results of the APF-based Thiran fractional delays on FPGA can potentially be used to realize DVM factorizations using application-specific integrated circuit (ASIC) technology.

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“…To overcome the above challenge, we proposed an

Section: Review: Analog Dvm Beamformersmentioning

confidence: 99%

Section: Digital Dvm Beamfomers Using Thiran Filtersmentioning

confidence: 99%

“…Typically, for N elements, N -independent beams can be realized by considering a short duration time delay

…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…For example, wideband signals applied to DFT-based multi-beams lead to an undesirable “beam squint” effect. The DVM entries, referred to as

in [ 7 , 8 , 9 , 10 , 11 ], serve as the foundation for a TTD-based beamformer. It is important to note that the DFT matrix equals the matrix

at a single temporal frequency because the DVM covers a wide range of multiple temporal frequencies.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Thiran Filters for Wideband DSP-Based Multi-Beam True Time Delay RF Sensing Applications

Perera,

Rathnasekara,

Madanayake

2024

Sensors

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Multi-Beam Beamforming-Based ML Algorithm to Optimize the Routing of Drone Swarms

Myers,

Perera,

McLewee

et al. 2024

Drones

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The advancement of wireless networking has significantly enhanced beamforming capabilities in Autonomous Unmanned Aerial Systems (AUAS). This paper presents a simple and efficient classical algorithm to route a collection of AUAS or drone swarms extending our previous work on AUAS. The algorithm is based on the sparse factorization of frequency Vandermonde matrices that correspond to each drone, and its entries are determined through spatiotemporal data of drones in the AUAS. The algorithm relies on multibeam beamforming, making it suitable for large-scale AUAS networking in wireless communications. We show a reduction in the arithmetic and time complexities of the algorithm through theoretical and numerical results. Finally, we also present an ML-based AUAS routing algorithm using the classical AUAS algorithm and feed-forward neural networks. We compare the beamformed signals of the ML-based AUAS routing algorithm with the ground truth signals to minimize the error between them. The numerical error results show that the ML-based AUAS routing algorithm enhances the accuracy of the routing. This error, along with the numerical and theoretical results for over 100 drones, provides the basis for the scalability of the proposed ML-based AUAS algorithms for large-scale deployments.

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A Fast DVM Algorithm for Wideband Time-Delay Multi-Beam Beamformers

Cited by 2 publications

References 49 publications

Thiran Filters for Wideband DSP-Based Multi-Beam True Time Delay RF Sensing Applications

Thiran Filters for Wideband DSP-Based Multi-Beam True Time Delay RF Sensing Applications

Multi-Beam Beamforming-Based ML Algorithm to Optimize the Routing of Drone Swarms

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