Imperial College London
Portrait Picture

Dr. Ayush Bhandari

Faculty of Engineering, Department of Electrical and Electronic Engineering

Senior Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6233a.bhandari Website

 
 
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Location

 

802Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

54 results found

Liu Z, Bhandari A, Clerckx B, 2023, λ–MIMO: massive MIMO via modulo sampling, IEEE Transactions on Communications, ISSN: 0090-6778

Massive multiple-input multiple-output (M-MIMO) architecture is the workhorse of modern communication systems. Currently, two fundamental bottlenecks, namely, power consumption and receiver saturation, limit the full potential achievement of this technology. These bottlenecks are intricately linked with the analog-to-digital converter (ADC) used in each radio frequency (RF) chain. The power consumption in M–MIMO systems grows exponentially with the ADC’s bit budget while ADC saturation causes permanent loss of information. This motivates the need for a solution that can simultaneously tackle the above-mentioned bottlenecks while offering advantages over existing alternatives such as low-resolution ADCs. Taking a radically different approach to this problem, we propose λ–MIMO architecture which uses modulo ADCs ( M λ –ADC) instead of a conventional ADC. Our work is inspired by the Unlimited Sampling Framework. M λ –ADC in the RF chain folds high dynamic range signals into low dynamic range modulo samples, thus alleviating the ADC saturation problem. At the same time, digitization of modulo signal results in high resolution quantization. In the novel λ–MIMO context, we discuss baseband signal reconstruction, detection and uplink achievable sum-rate performance. The key takeaways of our work include, (a) leveraging higher signal-to-quantization noise ratio (SQNR), (b) detection and average uplink sum-rate performances comparable to a conventional, infinite-resolution ADC when using a 1-2 bit M λ –ADC. This enables higher order modulation schemes e.g. 1024 QAM that seemed previously impossible, (c) superior trade-off between energy efficiency and bit budget, thus resulting in higher power efficiency. Numerical simulations and modulo ADC based hardware experiments corroborate our theory and reinforce the clear benefits of λ–MIMO approach.

Journal article

Shtendel G, Florescu D, Bhandari A, 2023, Unlimited Sampling of Bandpass Signals: Computational Demodulation via Undersampling, IEEE Transactions on Signal Processing, Vol: 71, Pages: 4134-4145, ISSN: 1053-587X

Bandpass signals are an important sub-class of bandlimited signals that naturally arise in a number of application areas but their high-frequency content poses an acquisition challenge. Consequently, 'Bandpass Sampling Theory' has been investigated and applied in the literature. In this article, we consider the problem of modulo sampling of bandpass signals with the main goal of sampling and recovery of high dynamic range inputs. Our work is inspired by the Unlimited Sensing Framework (USF). In the USF, the modulo operation folds high dynamic range inputs into low dynamic range, modulo samples. This fundamentally avoids signal clipping. Given that the output of the modulo nonlinearity is non-bandlimited, bandpass sampling conditions never hold true. Yet, we show that bandpass signals can be recovered from a modulo representation despite the inevitable aliasing. Our main contribution includes proof of sampling theorems for recovery of bandpass signals from an undersampled representation, reaching sub-Nyquist sampling rates. On the recovery front, by considering both time- and frequency-domain perspectives, we provide a holistic view of the modulo bandpass sampling problem. On the hardware front, we include ideal, non-ideal and generalized modulo folding architectures that arise in the hardware implementation of modulo analog-to-digital converters. Numerical simulations corroborate our theoretical results. Bridging the theory-practice gap, we validate our results using hardware experiments, thus demonstrating the practical effectiveness of our methods.

Journal article

Bhandari A, 2022, Back in the US-SR: unlimited sampling and sparse super-resolution with Its hardware validation, IEEE Signal Processing Letters, Vol: 29, Pages: 1047-1051, ISSN: 1070-9908

The Unlimited Sensing Framework (USF) is a digital acquisition protocol that allows for sampling and reconstruction of high dynamic range signals. By acquiring modulo samples, the USF circumvents the clipping or saturation problem that is a fundamental bottleneck in conventional analog-to-digital converters (ADCs). In the context of the USF, several works have focused on bandlimited function classes and recently, a hardware validation of the modulo sampling approach has been presented. In a different direction, in this paper we focus on non-bandlimited function classes and consider the well-known super-resolution problem; we study the recovery of sparse signals (Dirac impulses) from low-pass filtered, modulo samples. Taking an end-to-end approach to USF based super-resolution, we present a novel recovery algorithm (US-SR) that leverages a doubly sparse structure of the modulo samples. We derive a sampling criterion for the US-SR method. A hardware experiment with the modulo ADC demonstrates the empirical robustness of our method in a realistic, noisy setting, thus validating its practical utility.

Journal article

Florescu D, Bhandari A, 2022, Time Encoding via Unlimited Sampling: Theory, Algorithms and Hardware Validation, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 70, Pages: 4912-4924, ISSN: 1053-587X

Journal article

Florescu D, Krahmer F, Bhandari A, 2022, The Surprising Benefits of Hysteresis in Unlimited Sampling: Theory, Algorithms and Experiments, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 70, Pages: 616-630, ISSN: 1053-587X

Journal article

Beckmann M, Bhandari A, Krahmer F, 2022, The Modulo Radon Transform: Theory, Algorithms, and Applications, SIAM JOURNAL ON IMAGING SCIENCES, Vol: 15, Pages: 455-490, ISSN: 1936-4954

Journal article

Beckmann M, Bhandari A, 2022, MR. TOMP : INVERSION OF THE MODULO RADON TRANSFORM (MRT) VIA ORTHOGONAL MATCHING PURSUIT (OMP), IEEE International Conference on Image Processing (ICIP), Publisher: IEEE, Pages: 3748-3752, ISSN: 1522-4880

Conference paper

Fernandez-Menduina S, Krahmer F, Leus G, Bhandari Aet al., 2022, Computational Array Signal Processing via Modulo Non-Linearities, IEEE TRANSACTIONS ON SIGNAL PROCESSING, Vol: 70, Pages: 2168-2179, ISSN: 1053-587X

Journal article

Feuillen T, Alaee-Kerahroodi M, Bhandari A, Shankar BMR, Ottersten Bet al., 2022, Unlimited Sampling for FMCW Radars: A Proof of Concept, IEEE Radar Conference (RadarConf), Publisher: IEEE, ISSN: 1097-5764

Conference paper

Bhandari A, 2022, UNLIMITED SAMPLING WITH SPARSE OUTLIERS: EXPERIMENTS WITH IMPULSIVE AND JUMP OR RESET NOISE, 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 5403-5407, ISSN: 1520-6149

Conference paper

Florescu D, Bhandari A, 2022, MODULO EVENT-DRIVEN SAMPLING: SYSTEM IDENTIFICATION AND HARDWARE EXPERIMENTS, 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 5747-5751, ISSN: 1520-6149

Conference paper

Florescu D, Bhandari A, 2022, UNLIMITED SAMPLING WITH LOCAL AVERAGES, 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 5742-5746, ISSN: 1520-6149

Conference paper

Bhandari A, Krahmer F, Poskitt T, 2021, Unlimited sampling from theory to practice: fourier-prony recovery and prototype ADC, IEEE Transactions on Signal Processing, Vol: 70, Pages: 1131-1141, ISSN: 1053-587X

Following the Unlimited Sampling strategy to alleviate the omnipresent dynamic range barrier, we study the problem of recovering a bandlimited signal from point-wise modulo samples, aiming to connect theoretical guarantees with hardware implementation considerations. Our starting point is a class of non-idealities that we observe in prototyping an unlimited sampling based analog-to-digital converter. To address these non-idealities, we provide a new Fourier domain recovery algorithm. Our approach is validated both in theory and via extensive experiments on our prototype analog-to-digital converter, providing the first demonstration of unlimited sampling for data arising from real hardware, both for the current and previous approaches. Advantages of our algorithm include that it is agnostic to the modulo threshold and it can handle arbitrary folding times. We expect that the end-to-end realization studied in this paper will pave the path for exploring the unlimited sampling methodology in a number of real world applications.

Journal article

Bouis V, Krahmer F, Bhandari A, 2021, Multidimensional Unlimited Sampling: A Geometrical Perspective, 28th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 2314-2318, ISSN: 2076-1465

Conference paper

Fernandez-Menduina S, Krahmer F, Leus G, Bhandari Aet al., 2021, DoA Estimation via Unlimited Sensing, 28th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 1866-1870, ISSN: 2076-1465

Conference paper

Bhandari A, Beckmann M, Krahmer F, 2021, The Modulo Radon Transform and its Inversion, 28th European Signal Processing Conference (EUSIPCO), Publisher: IEEE, Pages: 770-774, ISSN: 2076-1465

Conference paper

Florescu D, Krahmer F, Bhandari A, 2021, EVENT-DRIVEN MODULO SAMPLING, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 5435-5439

Conference paper

Bhandari A, Krahmer F, Raskar R, 2020, On unlimited sampling and reconstruction, IEEE Transactions on Signal Processing, Vol: 69, Pages: 3827-3839, ISSN: 1053-587X

Shannon's sampling theorem is one of the cornerstone topics that is well understood and explored, both mathematically and algorithmically. That said, practical realization of this theorem still suffers from a severe bottleneck due to the fundamental assumption that the samples can span an arbitrary range of amplitudes. In practice, the theorem is realized using so-called analog--to--digital converters (ADCs) which clip or saturate whenever the signal amplitude exceeds the maximum recordable ADC voltage thus leading to a significant information loss. In this paper, we develop an alternative paradigm for sensing and recovery, called the Unlimited Sampling Framework. It is based on the observation that when a signal is mapped to an appropriate bounded interval via a modulo operation before entering the ADC, the saturation problem no longer exists, but one rather encounters a different type of information loss due to the modulo operation. Such an alternative setup can be implemented, for example, via so-called folding or self-reset ADCs, as they have been proposed in various contexts in the circuit design literature. The key task that we need to accomplish in order to cope with this new type of information loss is to recover a bandlimited signal from its modulo samples. In this paper we derive conditions when perfect recovery is possible and complement them with a stable recovery algorithm. The sampling density required to guarantee recovery is independent of the maximum recordable ADC voltage and depends on the signal bandwidth only. Our recovery guarantees extend to measurements affected by bounded noise, which includes the case of round-off quantization. Numerical experiments validate our approach. For example, it is possible to recover functions with amplitudes orders of magnitude higher than the ADC's threshold from quantized modulo samples upto the unavoidable quantization error. Applications of the unlimited sampling paradigm can be found in a number of fields su

Journal article

Bhandari A, Conde MH, Loffeld O, 2020, One-Bit Time-Resolved Imaging, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol: 42, Pages: 1630-1641, ISSN: 0162-8828

Journal article

Bhandari A, Krahmer F, 2020, HDR IMAGING FROM QUANTIZATION NOISE, IEEE International Conference on Image Processing (ICIP), Publisher: IEEE, Pages: 101-105, ISSN: 1522-4880

Conference paper

Bhandari A, Graf O, Krahmer F, Zayed Aet al., 2020, ONE-BIT SAMPLING IN FRACTIONAL FOURIER DOMAIN, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Publisher: IEEE, Pages: 9140-9144, ISSN: 1520-6149

Conference paper

Beckmann M, Krahmer F, Bhandari A, 2020, HDR TOMOGRAPHY VIA MODULO RADON TRANSFORM, IEEE International Conference on Image Processing (ICIP), Publisher: IEEE, Pages: 3025-3029, ISSN: 1522-4880

Conference paper

Bhandari A, Zayed AI, 2019, Shift-invariant and sampling spaces associated with the special aline Fourier transform, APPLIED AND COMPUTATIONAL HARMONIC ANALYSIS, Vol: 47, Pages: 30-52, ISSN: 1063-5203

Journal article

Bhandari A, Eldar YC, 2019, Sampling and super resolution of sparse signals beyond the Fourier domain, IEEE Transactions on Signal Processing, Vol: 67, Pages: 1508-1521, ISSN: 1053-587X

Recovering a sparse signal from its low-pass projections in the Fourier domain is a problem of broad interest in science and engineering and is commonly referred to as super resolution. In many cases, however, Fourier domain may not be the natural choice. For example, in holography, low-pass projections of sparse signals are obtained in the Fresnel domain. Similarly, time-varying system identification relies on low-pass projections on the space of linear frequency modulated signals. In this paper, we study the recovery of sparse signals from low-pass projections in the Special Affine Fourier Transform domain (SAFT). The SAFT parametrically generalizes a number of well-known unitary transformations that are used in signal processing and optics. In analogy to the Shannon's sampling framework, we specify sampling theorems for recovery of sparse signals considering three specific cases: 1) sampling with arbitrary, bandlimited kernels, 2) sampling with smooth, time-limited kernels, and 3) recovery from Gabor transform measurements linked with the SAFT domain. Our work offers a unifying perspective on the sparse sampling problem which is compatible with the Fourier, Fresnel, and Fractional Fourier domain-based results. In deriving our results, we introduce the SAFT series (analogous to the Fourier series) and the short-time SAFT, and study convolution theorems that establish a convolution-multiplication property in the SAFT domain.

Journal article

Conde MH, Bhandari A, Loffeld O, 2019, Nonuniform Sampling of Echoes of Light, 13th International Conference on Sampling Theory and Applications (SampTA), Publisher: IEEE

Conference paper

Bhandari A, Krahmer F, 2019, On Identifiability in Unlimited Sampling, 13th International Conference on Sampling Theory and Applications (SampTA), Publisher: IEEE

Conference paper

Graf O, Bhandari A, Krahmer F, 2019, ONE-BIT UNLIMITED SAMPLING, 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 5102-5106, ISSN: 1520-6149

Conference paper

Batenkov D, Bhandari A, Blu T, 2019, RETHINKING SUPER-RESOLUTION: THE BANDWIDTH SELECTION PROBLEM, 44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 5087-5091, ISSN: 1520-6149

Conference paper

Conde MH, Bhandari A, Kerstein T, Buxbaum B, Loffeld Oet al., 2019, Live Demonstration: Multiple-Path Depth Imaging with Time-of-Flight Sensors, 18th IEEE Sensors Conference, Publisher: IEEE, ISSN: 1930-0395

Conference paper

Bhandari A, Zayed AI, 2018, Convolution and Product Theorems for the Special Affine Fourier Transform, Frontiers in Orthogonal Polynomials and <i>q</i>-Series, Publisher: WORLD SCIENTIFIC, Pages: 119-137

Book chapter

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