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1. Massive MIMO Channel Estimation with 1-Bit Spatial Sigma-delta ADCs

   https://doi.org/10.1109/ICASSP.2019.8682842  

   Rao, Shilpa; Swindlehurst, A. Lee; Pirzadeh, Hessam (May 2019, 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   We consider channel estimation for an uplink massive multiple input multiple output (MIMO) system where the base station (BS) uses a first-order spatial Sigma-Delta (Σ△) analog-to-digital converter (ADC) array. The Σ△ array consists of closely spaced sensors which oversample the received signal and provide a coarsely quantized (1-bit) output. We develop a linear minimum mean squared error (LMMSE) estimator based on the Bussgang decomposition that reformulates the nonlinear quantizer model using an equivalent linear model plus quantization noise. The performance of the proposed Σ△ LMMSE estimator is compared via simulation to channel estimation using standard 1-bit quantization and also infinite resolution ADCs. 

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2. A 49-63 GHz Phase-locked FMCW Radar Transceiver for High Resolution Applications

   Liu, Xuyang; Maktoomi, Md Hedayatullah; Alesheikh, Mahdi; Heydari, Payam; Aghasi, Hamidreza (October 2023, IEEE European Solid State Circuits Conference)

   TPC of IEEE ESSCIRC Conference (Ed.)

   This paper presents an mmWave FMCW radar that can achieve sub-centimeter-scale range resolution at 14- GHz chirp-bandwidth while maintaining the radar range beyond 50 meters. To meet the requirements on power efficiency, chirp linearity, and signal-to-noise ratio (SNR), a phase-locked steppedchirp FMCW radar architecture is introduced. Specifically, a fully integrated radar transceiver comprising an interleaved frequency-segmented phase-locked transmitter and a segmented receiver architecture with high sensitivity is presented. The proposed design addresses the limitations of conventional typeII phase-locked loops (PLLs) in extending the radar bandwidth across multiple sub-bands with identical chirp profiles. Fabricated in a 22nm FD-SOI technology, the prototype chip comprises two sub-bands with 14 GHz of free-running bandwidth and 10 GHz of phase-locked bandwidth. The system achieves -101.7 dBc/Hz phase noise at 1 MHz offset, 8 dBm of effective isotropic radiated power (EIRP), 10 dB noise figure (NF), and 362.6 mW collective power consumption of transmitter and receiver arrays. 

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3. Hardware Trigger Processor for the MDT System

   https://doi.org/10.22323/1.313.0148  

   Costa de Paiva, Thiago (January 2018, Pos proceedings of science)

   null (Ed.)

   We are developing a low-latency hardware trigger processor for the Monitored Drift Tube system in the Muon spectrometer. The processor will fit candidate Muon tracks in the drift tubes in real time, improving significantly the momentum resolution provided by the dedicated trigger chambers. We present a novel pure-FPGA implementation of a Legendre transform segment finder, an associative-memory alternative implementation, an ARM (Zynq) processor-based track fitter, and compact ATCA carrier board architecture. The ATCA architecture is designed to allow a modular, staged approach to deployment of the system and exploration of alternative technologies. 

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4. HyDREA: Utilizing Hyperdimensional Computing For A More Robust and Efficient Machine Learning System

   https://doi.org/10.1145/3524067  

   Morris, Justin; Ergun, Kazim; Khaleghi, Behnam; Imani, Mohsen; Aksanli, Baris; Rosing, Tajana (January 2022, ACM Transactions on Embedded Computing Systems)

   Today’s systems, rely on sending all the data to the cloud, and then use complex algorithms, such as Deep Neural Networks, which require billions of parameters and many hours to train a model. In contrast, the human brain can do much of this learning effortlessly. Hyperdimensional (HD) Computing aims to mimic the behavior of the human brain by utilizing high dimensional representations. This leads to various desirable properties that other Machine Learning (ML) algorithms lack such as: robustness to noise in the system and simple, highly parallel operations. In this paper, we propose \(\mathsf {HyDREA} \) , a Hy per D imensional Computing system that is R obust, E fficient, and A ccurate. We propose a Processing-in-Memory (PIM) architecture that works in a federated learning environment with challenging communication scenarios that cause errors in the transmitted data. \(\mathsf {HyDREA} \) adaptively changes the bitwidth of the model based on the signal to noise ratio (SNR) of the incoming sample to maintain the accuracy of the HD model while achieving significant speedup and energy efficiency. Our PIM architecture is able to achieve a speedup of 28 × and 255 × better energy efficiency compared to the baseline PIM architecture for Classification and achieves 32 × speed up and 289 × higher energy efficiency than the baseline architecture for Clustering. \(\mathsf {HyDREA} \) is able to achieve this by relaxing hardware parameters to gain energy efficiency and speedup while introducing computational errors. We show experimentally, HD Computing is able to handle the errors without a significant drop in accuracy due to its unique robustness property. For wireless noise, we found that \(\mathsf {HyDREA} \) is 48 × more robust to noise than other comparable ML algorithms. Our results indicate that our proposed system loses less than \(1\% \) Classification accuracy, even in scenarios with an SNR of 6.64. We additionally test the robustness of using HD Computing for Clustering applications and found that our proposed system also looses less than \(1\% \) in the mutual information score, even in scenarios with an SNR under 7 dB , which is 57 × more robust to noise than K-means. 

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5. Efficient, nonparametric removal of noise and recovery of probability distributions from time series using nonlinear-correlation functions: Photon and photon-counting noise

   https://doi.org/10.1063/5.0212157  

   Dhar, Mainak; Berg, Mark A (July 2024, The Journal of Chemical Physics)

   A preceding paper [M. Dhar, J. A. Dickinson, and M. A. Berg, J. Chem. Phys. 159, 054110 (2023)] shows how to remove additive noise from an experimental time series, allowing both the equilibrium distribution of the system and its Green’s function to be recovered. The approach is based on nonlinear-correlation functions and is fully nonparametric: no initial model of the system or of the noise is needed. However, single-molecule spectroscopy often produces time series with either photon or photon-counting noise. Unlike additive noise, photon noise is signal-size correlated and quantized. Photon counting adds the potential for bias. This paper extends noise-corrected-correlation methods to these cases and tests them on synthetic datasets. Neither signal-size correlation nor quantization is a significant complication. Analysis of the sampling error yields guidelines for the data quality needed to recover the properties of a system with a given complexity. We show that bias in photon-counting data can be corrected, even at the high count rates needed to optimize the time resolution. Using all these results, we discuss the factors that limit the time resolution of single-molecule spectroscopy and the conditions that would be needed to push measurements into the submicrosecond region. 

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