An official website of the United States government
The radio spectrum is a scarce and extremely valuable resource that demands careful real-time monitoring and dynamic resource allocation. Dynamic spectrum access (DSA) is a new paradigm for managing the radio spectrum, which requires AI/ML-driven algorithms for optimum performance under rapidly changing channel conditions and possible cyber-attacks in the electromagnetic domain. Fast sensing across multiple directions using array processors, with subsequent AI/ML-based algorithms for the sensing and perception of waveforms that are measured from the environment is critical for providing decision support in DSA. As part of directional and wideband spectrum perception, the ability to finely channelize wideband inputs using efficient Fourier analysis is much needed. However, a fine-grain fast Fourier transform (FFT) across a large number of directions is computationally intensive and leads to a high chip area and power consumption. We address this issue by exploiting the recently proposed approximate discrete Fourier transform (ADFT), which has its own sparse factorization for real-time implementation at a low complexity and power consumption. The ADFT is used to create a wideband multibeam RF digital beamformer and temporal spectrum-based attention unit that monitors 32 discrete directions across 32 sub-bands in real-time using a multiplierless algorithm with low computational complexity. The output of this spectral attention unit is applied as a decision variable to an intelligent receiver that adapts its center frequency and frequency resolution via FFT channelizers that are custom-built for real-time monitoring at high resolution. This two-step process allows the fine-gain FFT to be applied only to directions and bands of interest as determined by the ADFT-based low-complexity 2D spacetime attention unit. The fine-grain FFT provides a spectral signature that can find future use cases in neural network engines for achieving modulation recognition, IoT device identification, and RFI identification. Beamforming and spectral channelization algorithms, a digital computer architecture, and early prototypes using a 32-element fully digital multichannel receiver and field programmable gate array (FPGA)-based high-speed software-defined radio (SDR) are presented.
more »
« less
Thiran Filters for Wideband DSP-Based Multi-Beam True Time Delay RF Sensing Applications
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.
more »
« less
- PAR ID:
- 10556158
- Publisher / Repository:
- Sensors MDPI
- Date Published:
- Journal Name:
- Sensors
- Volume:
- 24
- Issue:
- 2
- ISSN:
- 1424-8220
- Page Range / eLocation ID:
- 576
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Feature extraction, such as spectral occupancy, interferer energy and type, or direction-of-arrival, from wideband radio-frequency (RF) signals finds use in a growing number of applications as it enhances RF transceivers with cognitive abilities and enables parameter tuning of traditional RF chains. In power and cost limited applications, e.g., for sensor nodes in the Internet of Things, wideband RF feature extraction with conventional, Nyquist-rate analog-to-digital converters is infeasible. However, the structure of many RF features (such as signal sparsity) enables the use of compressive sensing (CS) techniques that acquire such signals at sub-Nyquist rates; while such CS-based analog-to-information (A2I) converters have the potential to enable low-cost and energy-efficient wideband RF sensing, they suffer from a variety of real-world limitations, such as noise folding, low sensitivity, aliasing, and limited flexibility. This paper proposes a novel CS-based A2I architecture called non-uniform wavelet sampling. Our solution extracts a carefully-selected subset of wavelet coefficients directly in the RF domain, which mitigates the main issues of existing A2I converter architectures. For multi-band RF signals, we propose a specialized variant called non-uniform wavelet bandpass sampling (NUWBS), which further improves sensitivity and reduces hardware complexity by leveraging the multi-band signal structure. We use simulations to demonstrate that NUWBS approaches the theoretical performance limits of ℓ₁-norm-based sparse signal recovery. We investigate hardware-design aspects and show ASIC measurement results for the wavelet generation stage, which highlight the efficacy of NUWBS for a broad range of RF feature extraction tasks in cost- and power-limited applications.more » « less
-
The self-interference (SI) channels in full-duplex (FD) radios have large nano-second-scale delay spreads, which poses a significant challenge in designing SI cancelers that can emulate the SI channel over wide bandwidths. Passive implementations of high delay lines have a prohibitively large form factor and loss when implemented on silicon, whereas active implementations suffer from noise and linearity penalties. In this work, we leverage time-interleaved multi-path switched-capacitor (SC) circuits to provide large wideband delays with a small form factor and low power (LP) consumption to implement RF and baseband (BB) cancelers in an FD receiver (RX). We utilize capacitor stacking to obtain passive voltage gain to compensate for the loss of these delay elements, thus permitting an increased number of interleaved paths and, hence, a higher delay. Furthermore, to reduce the RX noise figure (NF) penalty due to injecting the cancellation signal into the receiver, we introduce a novel low-noise trans-impedance amplifier (LNTA) architecture, which injects the cancellation signal into RX and also accomplishes finite impulse response (FIR) filter weighting and summation. The FD receiver is implemented in a standard 65-nm CMOS process and operates from 0.1 to 1 GHz. The RF/BB canceler delay cells have real-/complex-valued weighting with delays rangingmore » « less
-
When using ultra-wideband signaling on massive multiple-input multiple-output (mMIMO) systems, the electromagnetic wave incurs an extra delay (across the array elements) comparable to or larger than the symbol duration, which translates into a shift in beam direction known as the beam squint effect. The beam squinting problem degrades the array gain and reduces the system capacity. This paper proposes a novel transceiver design based on lens antenna subarray and analog subband filters to compensate for the beam squinting effect. Specifically, the proposed design chunks the wideband signal from the phase shifters into groups of narrowband signals and controls their squints through an exhaustive search-based switching/precoding mechanism under the lenses. Furthermore, a simplified, thresholded search-based precoding algorithm is proposed, which demonstrates good performance while significantly minimizing complexity. The proposed system is analyzed in terms of beam gain, complexity, power consumption, and capacity. The numerical results demonstrate significant performance enhancement for the proposed system design as compared to the conventional mMIMO system with an uncompensated beam squinting problem.more » « less
-
null (Ed.)Massive multi-user multiple-input multiple-output (MU-MIMO) wireless systems operating at millimeter-wave (mmWave) frequencies enable simultaneous wideband data transmission to a large number of users. In order to reduce the complexity of MU precoding in all-digital basestation architectures that equip each antenna element with a pair of data converters, we propose a two-stage precoding architecture which first generates a sparse precoding matrix in the beamspace domain, followed by an inverse fast Fourier transform that converts the result to the antenna domain. The sparse precoding matrix requires a small amount of multipliers and enables regular hardware architectures, which allows the design of hardware-efficient all-digital precoders. Simulation results demonstrate that our methods approach the error-rate performance of conventional Wiener filter precoding with more than 2x lower complexity.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/s24020576
