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1. Sparse Beamspace Equalization for Massive MU-MIMO MMWave Systems

   https://doi.org/10.1109/ICASSP40776.2020.9052952  

   Mirfarshbafan, Seyed Hadi; Studer, Christoph (May 2020, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP))

   null (Ed.)

   We propose equalization-based data detection algorithms for all-digital millimeter-wave (mmWave) massive multiuser multiple-input multiple-out (MU-MIMO) systems that exploit sparsity in the beamspace domain to reduce complexity. We provide a condition on the number of users, basestation antennas, and channel sparsity for which beamspace equalization can be less complex than conventional antenna-domain processing. We evaluate the performance-complexity trade-offs of existing and new beamspace equalization algorithms using simulations with realistic mmWave channel models. Our results reveal that one of our proposed beamspace equalization algorithms achieves up to 8× complexity reduction under line-of-sight conditions, assuming a sufficiently large number of transmissions within the channel coherence interval. 

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2. 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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3. Sparsity-Adaptive Beamspace Channel Estimation for 1-Bit mmWave Massive MIMO Systems

   https://doi.org/10.1109/SPAWC48557.2020.9154213  

   Gallyas-Sanhueza, Alexandra; Mirfarshbafan, Seyed Hadi; Ghods, Ramina; Studer, Christoph (May 2020, IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC))

   null (Ed.)

   We propose sparsity-adaptive beamspace channel estimation algorithms that improve accuracy for 1-bit data converters in all-digital millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) basestations. Our algorithms include a tuning stage based on Stein's unbiased risk estimate (SURE) that automatically selects optimal denoising parameters depending on the instantaneous channel conditions. Simulation results with line-of-sight (LoS) and non-LoS mmWave massive MIMO channel models show that our algorithms improve channel estimation accuracy with 1-bit measurements in a computationally-efficient manner. 

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4. Optimality of the Discrete Fourier Transform for Beamspace Massive MU-MIMO Communication

   https://doi.org/10.1109/ISIT45174.2021.9518255  

   Taner, Sueda; Studer, Christoph (July 2021, IEEE International Symposium on Information Theory)

   null (Ed.)

   Beamspace processing is an emerging technique to reduce baseband complexity in massive multiuser (MU) multipleinput multiple-output (MIMO) communication systems operating at millimeter-wave (mmWave) and terahertz frequencies. The high directionality of wave propagation at such high frequencies ensures that only a small number of transmission paths exist between user equipments and basestation (BS). In order to resolve the sparse nature of wave propagation, beamspace processing traditionally computes a spatial discrete Fourier transform (DFT) across a uniform linear antenna array at the BS where each DFT output is associated with a specific beam. In this paper, we study optimality conditions of the DFT for sparsity-based beamspace processing with idealistic mmWave channel models and realistic channels. To this end, we propose two algorithms that learn unitary beamspace transforms using an l4-norm-based sparsity measure, and we investigate their optimality theoretically and via simulations. 

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5. VLSI Design of a Nonparametric Equalizer for Massive MU-MIMO

   Jeon, Charles; Mirza, Gulnar; Maleki, Arian; Studer, Christoph (October 2017, Asilomar Conference on Signals, Systems, and Computers)

   Linear minimum mean-square error (L-MMSE) equalization is among the most popular methods for data detection in massive multi-user multiple-input multiple-output (MU-MIMO) wireless systems. While L-MMSE equalization enables near-optimal spectral efficiency, accurate knowledge of the signal and noise powers is necessary. Furthermore, corresponding VLSI designs must solve linear systems of equations, which requires high arithmetic precision, exhibits stringent data dependencies, and results in high circuit complexity. This paper proposes the first VLSI design of the NOnParametric Equalizer (NOPE), which avoids knowledge of the transmit signal and noise powers, provably delivers the performance of L-MMSE equalization for massive MU-MIMO systems, and is resilient to numerous system and hardware impairments due to its parameter-free nature. Moreover, NOPE avoids computation of a matrix inverse and only requires hardware-friendly matrix-vector multiplications. To showcase the practical advantages of NOPE, we propose a parallel VLSI architecture and provide synthesis results in 28nm CMOS. We demonstrate that NOPE performs on par with existing data detectors for massive MU-MIMO that require accurate knowledge of the signal and noise powers. 

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