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1. SPADE: Sparsity-Adaptive Equalization for MMwave Massive MU-MIMO

   https://doi.org/10.1109/SSP49050.2021.9513825  

   Mirfarshbafan, Seyed Hadi; Studer, Christoph (July 2021, IEEE Statistical Signal Processing Workshop (SSP))

   We propose SParsity-ADaptive Equalization (SPADE), a novel approach to reduce the effective number of multiplications in sparse inner products by adaptively skipping multiplications that have little to no effect on the result. We apply SPADE to beamspace linear minimum mean square error (LMMSE) spatial equalization in all-digital millimeter-wave (mmWave) massive multiuser multiple-input multiple-output (MU-MIMO) systems. We propose a SPADE-based architecture that mutes insignificant multiplications to offer power savings. We use simulation results with line-of-sight (LoS) and non-LoS mmWave channel models to demonstrate that SPADE-LMMSE performs on par with state-of-the-art beamspace equalizers in terms of bit error-rate, while requiring significantly lower preprocessing complexity. 

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2. Regularized Neural Detection for Millimeter Wave Massive Mimo Communication Systems with One-Bit Adcs

   https://doi.org/10.1109/ICASSP49357.2023.10096921  

   Sant, Aditya; Rao, Bhaskar D. (June 2023, ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece, 2023)

   Multi-user massive MIMO signal detection from one-bit received measurements strongly depends on the wireless channel. To this end, majority of the model and learning-based approaches address detector design for the rich-scattering, homogeneous Rayleigh fading channel. Our work proposes detection for one-bit massive MIMO for the lower diversity mmWave channel. We analyze the limitations of the current state-of-the-art gradient descent (GD)-based joint multiuser detection of one-bit received signals for the mmWave channels. Addressing these, we introduce a new framework to ensure equitable per-user performance, in spite of joint multi-user detection. This is realized by means of: (i) a parametric deep learning system, i.e., the mmW-ROBNet, (ii) a constellation-aware loss function, and (iii) a hierarchical detection training strategy. The experimental results corroborate this proposed approach for equitable per-user detection. 

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3. Joint CFO and channel estimation in millimeter wave systems with one-bit ADCs

   https://doi.org/10.1109/CAMSAP.2017.8313145  

   Myers, Nitin Jonathan; Heath, Robert W. (December 2017, Proc. of the : IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP))

   We develop a method to jointly estimate the carrier frequency offset (CFO) and the narrowband channel in millimeter wave (mmWave) MIMO systems operating with one-bit analog-to-digital converters (ADCs). We assume perfect timing synchronization and transform the underlying CFO-channel optimization problem to a higher dimensional space using lifting techniques. Exploiting the sparse nature of mmWave MIMO channels in the angle domain, we perform the joint estimation by solving a noisy quantized compressed sensing problem of the lifted version, using generalized approximate message passing. Simulation results show that our method is able to recover both the channel and the CFO using one-bit measurements. 

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4. BEACHES: Beamspace Channel Estimation for Multi-Antenna mmWave Systems and Beyond

   https://doi.org/10.1109/SPAWC.2019.8815576  

   Ghods, Ramina; Gallyas-Sanhueza, Alexandra; Mirfarshbafan, Seyed Hadi; Studer, Christoph (July 2019, IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))

   Massive multi-antenna millimeter wave (mmWave) and terahertz wireless systems promise high-bandwidth communication to multiple user equipments in the same time-frequency resource. The high path loss of wave propagation at such frequencies and the fine-grained nature of beamforming with massive antenna arrays necessitates accurate channel estimation to fully exploit the advantages of such systems. In this paper, we propose BEAmspace CHannel EStimation (BEACHES), a low-complexity channel estimation algorithm for multi-antenna mmWave systems and beyond. BEACHES leverages the fact that wave propagation at high frequencies is directional, which enables us to denoise the (approximately) sparse channel state information in the beamspace domain. To avoid tedious parameter selection, BEACHES includes a computationally-efficient tuning stage that provably minimizes the mean-square error of the channel estimate in the large-antenna limit. To demonstrate the efficacy of BEACHES, we provide simulation results for line-of-sight (LoS) and non-LoS mmWave channel models. 

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5. A Resolution-Adaptive 8 mm ² 9.98 Gb/s 39.7 pJ/b 32-Antenna All-Digital Spatial Equalizer for mmWave Massive MU-MIMO in 65nm CMOS

   https://doi.org/10.1109/ESSCIRC53450.2021.9567843  

   Castaneda, Oscar; Boynton, Zachariah; Mirfarshbafan, Seyed Hadi; Huang, Shimin; Ye, Jamie C.; Molnar, Alyosha; Studer, Christoph (September 2021, IEEE 47th European Solid State Circuits Conference (ESSCIRC))

   All-digital millimeter-wave (mmWave) massive multi-user multiple-input multiple-output (MU-MIMO) receivers enable extreme data rates but require high power consumption. In order to reduce power consumption, this paper presents the first resolution-adaptive all-digital receiver ASIC that is able to adjust the resolution of the data-converters and baseband-processing engine to the instantaneous communication scenario. The scalable 32-antenna, 65 nm CMOS receiver occupies a total area of 8 mm 2 and integrates analog-to-digital converters (ADCs) with programmable gain and resolution, beamspace channel estimation, and a resolution-adaptive processing-in-memory spatial equalizer. With 6-bit ADC samples and a 4-bit spatial equalizer, our ASIC achieves a throughput of 9.98 Gb/s while being at least 2× more energy-efficient than state-of-the-art designs. 

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