arXiv subjects

Hanyoung Park

Publications and source records attributed to Hanyoung Park.

4 recordsLinked to original sources

arXiv2026

Low-Complexity Blind SNR Estimator for mmWave Multi-Antenna Communications

In this paper, we propose a low-complexity blind estimator for the average noise power, average signal power, and signal-to-noise ratio (SNR) in millimeter-wave (mmWave) massive multi-antenna uplink systems. In particular, the proposed method is designed to operate using only a single received signal sample, without relying on pilot signals, iterative optimization, or multiple observations, and without requiring prior knowledge of the transmitted signal. By exploiting the inherent sparsity of mmWave channels in the beamspace domain, the estimator identifies noise-dominant components through a sorting-based procedure combined with a finite-difference criterion. This separation is further supported by the order statistics of noise power under Gaussian assumptions, enabling statistically grounded discrimination between signal and noise elements. The average noise power is estimated from the identified noise-only components, and the signal power and SNR are subsequently obtained through simple arithmetic operations. The proposed algorithm achieves low computational complexity and is well-suited for real-time implementation. To demonstrate its practical feasibility, a hardware-efficient very large-scale integration (VLSI) architecture is developed and implemented on a AMD-Xilinx Kintex UltraScale+ KCU116 Evaluation Kit, with corresponding field-programmable gate array (FPGA) results provided. The implementation exhibits low latency and sublinear scaling of hardware resource utilization with respect to the number of antennas, and enables parameter estimation within a duration shorter than a single symbol of conventional wireless systems. Simulation results verify that the proposed estimator achieves high estimation accuracy compared to existing single-sample-based methods.

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arXiv2026

Low-Complexity Beamspace Channel Denoiser for mmWave Massive MIMO with Low-Resolution ADCs

In this paper, we propose a low-complexity beamspace channel denoising algorithm for millimeter-wave (mmWave) massive multi-input multi-output (MIMO) systems with low-resolution analog-to-digital converters (ADCs). The proposed method exploits the inherent sparsity of mmWave channels in the beamspace domain and formulates the denoising problem as a Bayesian binary hypothesis testing under a Bernoulli-complex Gaussian prior. To capture the distortion induced by low-resolution ADCs in a complexity-efficient manner, thermal noise and quantization noise are jointly modeled as a composite noise. Based on this modeling, a closed-form threshold value and a hard-thresholding-based denoising rule are derived to distinguish signal-dominant and noise-dominant components. The resulting algorithm avoids computationally intensive operations such as matrix inversion, iterative optimization, and parameter searching, and achieves near-linear computational complexity with respect to the number of antennas. Furthermore, a hardware-efficient very large-scale integration (VLSI) architecture is developed to enable practical deployment of the proposed algorithm, and is implemented on an AMD-Xilinx Kintex UltraScale+ KCU116 FPGA platform. The design incorporates hardware-aware simplifications and an efficient processing structure, leading to significantly lower latency and reduced hardware resource utilization compared to existing hardware implementations, along with sublinear scaling as the number of antennas increases. Extensive simulation results demonstrate that the proposed method achieves performance comparable to computationally intensive existing approaches while significantly reducing computational complexity.

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arXiv2026

Binary Hypothesis Testing-Based Low-Complexity Beamspace Channel Estimation for mmWave Massive MIMO Systems

Millimeter-wave (mmWave) communications have gained attention as a key technology for high-capacity wireless systems, owing to the wide available bandwidth. However, mmWave signals suffer from their inherent characteristics such as severe path loss, poor scattering, and limited diffraction, which necessitate the use of large antenna arrays and directional beamforming, typically implemented through massive MIMO architectures. Accurate channel estimation is critical in such systems, but its computational complexity increases proportionally with the number of antennas. This may become a significant burden in mmWave systems where channels exhibit rapid fluctuations and require frequent updates. In this paper, we propose a low-complexity channel denoiser based on Bayesian binary hypothesis testing and beamspace sparsity. By modeling each sparse beamspace component as a mixture of signal and noise under a Bernoulli-complex Gaussian prior, we formulate a likelihood ratio test to detect signal-relevant elements. Then, a hard-thresholding rule is applied to suppress noise-dominant components in the noisy channel vector. Despite its extremely low computational complexity, the proposed method achieves channel estimation accuracy that is comparable to that of complex iterative or learning-based approaches. This effectiveness is supported by both theoretical analysis and numerical evaluation, suggesting that the method can be a viable option for mmWave systems with strict resource constraints.

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arXiv2026

Low-Complexity Blind Estimator of SNR and MSE for mmWave Multi-Antenna Communications

To enhance the robustness and resilience of wireless communication and meet performance requirements, various environment-reflecting metrics, such as the signal-to-noise ratio (SNR), are utilized as the system parameter. To obtain these metrics, training signals such as pilot sequences are generally employed. However, the rapid fluctuations of the millimeter-wave (mmWave) propagation channel often degrade the accuracy of such estimations. To address this challenge, various blind estimators that operate without pilot have been considered as potential solutions. However, these algorithms often involve a training phase for machine learning or a large number of iterations, which implies prohibitive computational complexity, making them difficult to employ for real-time services and the system less resilient to dynamic environment variation. In this paper, we propose blind estimators for average noise power, signal power, SNR, and mean-square error (MSE) that do not require knowledge of the ground-truth signal or involve high computational complexity. The proposed algorithm leverages the inherent sparsity of mmWave channel in beamspace domain, which makes the signal and noise power components more distinguishable.

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