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

IEEE Trans. Wireless Commun.

2023

Baseband processing algorithms often require knowledge of the noise power, signal power, or signal-to-noise ratio (SNR). In practice, these parameters are typically unknown and must be estimated. Furthermore, the mean-square error (MSE) is a desirable metric to be minimized in a variety of estimation and signal recovery algorithms. However, the MSE cannot directly be used as it depends on the true signal that is generally unknown to the estimator. In this paper, we propose novel blind estimators for the average noise power, average receive signal power, SNR, and MSE. The proposed estimators can be computed at low complexity and solely rely on the large-dimensional and sparse nature of the processed data. Our estimators can be used (i) to quickly track some of the key system parameters while avoiding additional pilot overhead, (ii) to design low-complexity nonparametric algorithms that require such quantities, and (iii) to accelerate more sophisticated estimation or recovery algorithms. We conduct a theoretical analysis of the proposed estimators for a Bernoulli complex Gaussian (BCG) prior, and we demonstrate their efficacy via synthetic experiments. We also provide three application examples that deviate from the BCG prior in millimeter-wave multi-antenna and cell-free wireless systems for which we develop nonparametric denoising algorithms that improve channel-estimation accuracy with a performance comparable to denoisers that assume perfect knowledge of the system parameters.

Section: A Prior Art In Blind and Nonparametric Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low-Complexity Blind Parameter Estimation in Wireless Systems with Noisy Sparse Signals

Gallyas-Sanhueza

IEEE Trans. Wireless Commun.

2023

“…Here, L denotes the total number of arriving paths, α ∈ C is the channel gain of the th path, and a(φ ) is the complexvalued sinusoid vector, where φ ∈ [0, 2π) is determined by the th path's angle-of-arrival. If the number of paths L is small compared to BS antenna array size B, which is the case in mmWave massive MIMO [6], then the beamspace channel vector ĥ = Fh of each UE will be sparse; this enables low-complexity baseband processing algorithms, such as data detectors and channel estimators [10]- [12], [28].…”

Section: A Beamspace Massive Mu-mimo Processingmentioning

confidence: 99%

SMUL-FFT: A Streaming Multiplierless Fast Fourier Transform

Mirfarshbafan

Taner

IEEE Trans. Circuits Syst. II

2021

Self Cite

Beamspace processing is an emerging paradigm to reduce hardware complexity in all-digital millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) basestations. This approach exploits sparsity of mmWave channels but requires spatial discrete Fourier transforms (DFTs) across the antenna array, which must be performed at the baseband sampling rate. To mitigate the resulting DFT hardware implementation bottleneck, we propose a fully-unrolled Streaming MUltiplierLess (SMUL) fast Fourier Transform (FFT) engine that performs one transform per clock cycle. The proposed SMUL-FFT architecture avoids hardware multipliers by restricting the twiddle factors to a sum-of-powers-of-two, resulting in substantial power and area savings. Compared to state-ofthe-art FFTs, our SMUL-FFT ASIC designs in 65 nm CMOS demonstrate more than 45% and 17% improvements in energyefficiency and area-efficiency, respectively, without noticeably increasing the error-rate in mmWave massive MIMO systems. I. INTRODUCTIONMillimeter-wave (mmWave) communication [1], [2] promises significantly increased data-rates due to the availability of large contiguous frequency bands. Massive multiuser multipleinput multiple-output (MU-MIMO) [3] is a key technology to combat the high path loss of mmWave propagation [2] while enabling simultaneous communication with multiple user equipments (UEs) in the same frequency band. The higher baseband sampling rates needed to support larger bandwidths at mmWave frequencies, combined with the large number of antennas in massive MU-MIMO, result in new challenges for analog and digital hardware design. A. Fast Fourier Transforms for Beamspace ProcessingMmWave channels typically comprise only a few dominant propagation paths [1], [2], making them sparse in the beamspace domain [4]- [9]. Beamspace processing exploits this sparsity to reduce the computational complexity of baseband processing [10]-[12]. This approach, which is described in detail in Section II-A, calls for spatial discrete Fourier transforms (DFTs) operated at the baseband sampling rate in order to convert the received signals at the antenna array into the beamspace domain-in high-bandwidth mmWave communication systems, billions of spatial DFTs must be computed per second.

“…We emphasize that in (2), the residual distortion e is not necessarily independent of the sparse signal s. System Model 2 is relevant in the following scenarios: (i) When estimating a sparse signal s from a noisy measurement y by applying an (entry-wise) denoising or estimation function, producing the signal estimate s = µ(y). This scenario finds use for channelvector denosing [11], [12], for example. (ii) When modeling nonlinearlties caused by hardware impairments [15], in which case the distorted version of the noisy received signal can be expressed as r = µ(y).…”

Section: A System Modelsmentioning

confidence: 99%

“…We emphasize that in (2), the residual distortion e is not necessarily independent of the sparse signal s. System Model 2 is relevant when applying (entry-wise) estimation or denoising functions, which finds use for channel-vector denosing [11], [12], or to model nonlinearlties at the output of System Model 1 when modeling the impact of hardware impairments [15].…”

Section: Low-complexity Blind Estimators a System Modelsmentioning

confidence: 99%

Blind SNR Estimation and Nonparametric Channel Denoising in Multi-Antenna mmWave Systems

Gallyas-Sanhueza

ICC 2021 - IEEE International Conference on Communications

2021

Self Cite

We propose blind estimators for the average noise power, receive signal power, signal-to-noise ratio (SNR), and mean-square error (MSE), suitable for multi-antenna millimeter wave (mmWave) wireless systems. The proposed estimators can be computed at low complexity and solely rely on beamspace sparsity, i.e., the fact that only a small number of dominant propagation paths exist in typical mmWave channels. Our estimators can be used (i) to quickly track some of the key quantities in multi-antenna mmWave systems while avoiding additional pilot overhead and (ii) to design efficient nonparametric algorithms that require such quantities. We provide a theoretical analysis of the proposed estimators, and we demonstrate their efficacy via synthetic experiments and using a nonparametric channel-vector denoising task with realistic multi-antenna mmWave channels.