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1. A Direct-Conversion Digital Beamforming Array Receiver with 800 MHz Channel Bandwidth at 28 GHz using Xilinx RF SoC

   Sravan Pulipati, Viduneth Ariyarathna (November 2019, 2019 IEEE International Conference on Microwaves, Antennas, Communications and Electronic Systems (COMCAS))

   null (Ed.)

   This paper discusses early results associated with a fully-digital direct-conversion array receiver at 28 GHz. The proposed receiver makes use of commercial off-the-shelf (COTS) electronics, including the receiver chain. The design consists of a custom 28 GHz patch antenna sub-array providing gain in the elevation plane, with azimuthal plane beamforming provided by real-time digital signal processing (DSP) algorithms running on a Xilinx Radio Frequency System on Chip (RF SoC). The proposed array receiver employs element-wise fully-digital array processing that supports ADC sample rates up to 2 GS/second and up to 1 GHz of operating bandwidth per antenna. The RF mixed-signal data conversion circuits and DSP algorithms operate on a single-chip RFSoC solution installed on the Xilinx ZCU1275 prototyping platform. 

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2. On-chip petahertz electronics for single-shot phase detection

   https://doi.org/10.1038/s41467-024-53788-z  

   Ritzkowsky, Felix; Yeung, Matthew; Bebeti, Engjell; Gebert, Thomas; Matsuyama, Toru; Budden, Matthias; Mainz, Roland E; Cankaya, Huseyin; Berggren, Karl K; Rossi, Giulio Maria; et al (December 2024, Nature Communications)

   Abstract Attosecond science has demonstrated that electrons can be controlled on the sub-cycle time scale of an optical waveform, paving the way towards optical frequency electronics. However, these experiments historically relied on high-energy laser pulses and detection not suitable for microelectronic integration. For practical optical frequency electronics, a system suitable for integration and capable of generating detectable signals with low pulse energies is needed. While current from plasmonic nanoantenna emitters can be driven at optical frequencies, low charge yields have been a significant limitation. In this work we demonstrate that large-scale electrically connected plasmonic nanoantenna networks, when driven in concert, enable charge yields sufficient for single-shot carrier-envelope phase detection at repetition rates exceeding tens of kilohertz. We not only show that limitations in single-shot CEP detection techniques can be overcome, but also demonstrate a flexible approach to optical frequency electronics in general, enabling future applications such as high sensitivity petahertz-bandwidth electric field sampling or logic-circuits. 

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3. Resolution-Adaptive All-Digital Spatial Equalization for mmWave Massive MU-MIMO

   https://doi.org/10.1109/SPAWC51858.2021.9593110  

   Castaneda, Oscar; Mirfarshbafan, Seyed Hadi; Ghajari, Shahaboddin; Molnar, Alyosha; Jacobsson, Sven; Durisi, Giuseppe; Studer, Christoph (September 2021, IEEE 22nd International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))

   All-digital basestation (BS) architectures for millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO), which equip each radio-frequency chain with dedicated data converters, have advantages in spectral efficiency, flexibility, and baseband-processing simplicity over hybrid analog-digital solutions. For all-digital architectures to be competitive with hybrid solutions in terms of power consumption, novel signal-processing methods and baseband architectures are necessary. In this paper, we demonstrate that adapting the resolution of the analog-to-digital converters (ADCs) and spatial equalizer of an all-digital system to the communication scenario (e.g., the number of users, modulation scheme, and propagation conditions) enables orders-of-magnitude power savings for realistic mmWave channels. For example, for a 256-BS-antenna 16-user system supporting 1 GHz bandwidth, a traditional baseline architecture designed for a 64-user worst-case scenario would consume 23 W in 28 nm CMOS for the ADC array and the spatial equalizer, whereas a resolution-adaptive architecture is able to reduce the power consumption by 6.7×. 

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4. Variational Bayes for Joint Channel Estimation and Data Detection in Few-Bit Massive MIMO Systems

   https://doi.org/10.1109/TSP.2024.3429009  

   Nguyen, Ly V; Swindlehurst, A Lee; Nguyen, Duy_H N (July 2024, IEEE Transactions on Signal Processing)

   Massive multiple-input multiple-output (MIMO) communications using low-resolution analog-to-digital converters (ADCs) is a promising technology for providing high spectral and energy efficiency with affordable hardware cost and power consumption. However, the use of low-resolution ADCs requires special signal processing methods for channel estimation and data detection since the resulting system is severely non-linear. This paper proposes joint channel estimation and data detection methods for massive MIMO systems with low-resolution ADCs based on the variational Bayes (VB) inference framework. We first derive matched-filter quantized VB (MF-QVB) and linear minimum mean-squared error quantized VB (LMMSE-QVB) detection methods assuming the channel state information (CSI) is available. Then we extend these methods to the joint channel estimation and data detection (JED) problem and propose two methods we refer to as MF-QVB-JED and LMMSE-QVB-JED. Unlike conventional VB-based detection methods that assume knowledge of the second-order statistics of the additive noise, we propose to float the elements of the noise covariance matrix as unknown random variables that are used to account for both the noise and the residual inter-user interference. We also present practical aspects of the QVB framework to improve its implementation stability. Finally, we show via numerical results that the proposed VB-based methods provide robust performance and also significantly outperform existing methods. 

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5. 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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