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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. 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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3. Bayesian Iterative Channel Estimation and Turbo Equalization for Multiple-Input–Multiple-Output Underwater Acoustic Communications

   https://doi.org/10.1109/JOE.2019.2956299  

   Qin, Xiangzhao ; Qu, Fengzhong ; Zheng, Yahong Rosa ( July 2020 , IEEE Journal of Oceanic Engineering)

   This article investigates a robust receiver scheme for a single carrier, multiple-input–multiple-output (MIMO) underwater acoustic (UWA) communications, which uses the sparse Bayesian learning algorithm for iterative channel estimation embedded in Turbo equalization (TEQ). We derive a block-wise sparse Bayesian learning framework modeling the spatial correlation of the MIMO UWA channels, where a more robust expectation–maximization algorithm is proposed for updating the joint estimates of channel impulse response, residual noise, and channel covariance matrix. By exploiting the spatially correlated sparsity of MIMO UWA channels and the second-order a priori channel statistics from the training sequence, the proposed Bayesian channel estimator enjoys not only relatively low complexity but also more stable control of the hyperparameters that determine the channel sparsity and recovery accuracy. Moreover, this article proposes a low complexity space-time soft decision feedback equalizer (ST-SDFE) with successive soft interference cancellation. Evaluated by the undersea 2008 Surface Processes and Acoustic Communications Experiment, the improved sparse Bayesian learning channel estimation algorithm outperforms the conventional Bayesian algorithms in terms of the robustness and complexity, while enjoying better estimation accuracy than the orthogonal matching pursuit and the improved proportionate normalized least mean squares algorithms. We have also verified that the proposed ST-SDFE TEQ significantly outperforms the low-complexity minimum mean square error TEQ in terms of the bit error rate and error propagation. 

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4. 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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5. 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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