Hanyoung Park

My research focuses on signal processing and optimization for wireless communication systems, with an emphasis on statistical inference, computational efficiency, and practical system constraints. I am particularly interested in developing mathematically grounded algorithms and understanding the fundamental limitations of communication systems. My research spans three main areas: low-complexity signal processing for massive MIMO systems, hardware impairments in wireless communications, and wireless resource allocation and optimization.

Please refer to the following description to see the detailed information on each research topic.

Index

Low-Complexity Signal Processing for Massive MIMO Systems

Hardware Impairments in Wireless Communications

Wireless Resource Allocation and Optimization

Low-Complexity Signal Processing for Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) systems employ large antenna arrays to improve spectral efficiency and support simultaneous communication with multiple users. However, increasing the number of antennas also increases the computational burden of receiver operations. This creates a need for signal processing algorithms that can exploit the inherent structure of wireless channels while maintaining manageable computational complexity.

My research focuses on developing low-complexity estimation algorithms for massive MIMO systems by leveraging statistical inference, signal sparsity, and mathematical properties of communication signals.

Low-Complexity Beamspace Channel Estimation

Millimeter-wave massive MIMO channels often exhibit sparse structures in the beamspace domain. These properties provide opportunities to reduce the computational complexity of channel estimation compared with conventional approaches that do not explicitly exploit statistical characteristics of channel sparsity. I have investigated a binary hypothesis testing-based approach to beamspace channel estimation. By statistically distinguishing active channel components from noise-dominated components, this approach exploits beamspace sparsity to enable computationally efficient channel estimation.

Related publication

H. Park and J.-W. Choi, "Binary Hypothesis Testing-Based Low-Complexity Channel Estimation for mmWave Massive MIMO Systems," under review.
journal article, submitted in Jun 2025. | related topic: massive MIMO, signal processing, wireless communications | link

Blind Statistical Estimation

Many communication algorithms require knowledge of statistical parameters, such as the signal-to-noise ratio (SNR) and mean squared error (MSE). However, obtaining these parameters through conventional estimation techniques may involve additional computational overhead or require information that is not directly available at the receiver. I have investigated low-complexity blind estimation techniques for multi-antenna communication systems. My research focuses on estimating SNR and MSE from received signals without relying on explicit knowledge of transmitted symbols, while exploiting statistical properties to reduce computational requirements.

Related publication

H. Park, H. Jang, and J.-W. Choi, "Low-Complexity Blind SNR Estimator for mmWave Multi-Antenna Communications," under review.
journal article, submitted in Oct 2026. | related topic: massive MIMO, signal processing, wireless communications | link
H. Park and J.-W. Choi, "Low-Complexity Blind Estimator of SNR and MSE for mmWave Multi-Antenna Communications," 2025 IEEE Globecom Workshops (GC Wkshps), 2025.
related topic: massive MIMO, signal processing, wireless communications | link

Hardware Impairments in Wireless Communications

Wireless communication systems are commonly analyzed under idealized hardware assumptions. In practical implementations, however, analog and mixed-signal components introduce impairments that can alter the received signal characteristics and degrade system performance. These effects become particularly important in massive MIMO systems, where hardware cost, power consumption, and signal processing requirements must be considered together.

My research investigates the impact of hardware impairments on wireless communication systems, focusing on both the mathematical characterization of nonideal hardware effects and the development of signal processing techniques that account for these imperfections.

Low-Complexity Beamspace Channel Estimation

Employing low-resolution analog-to-digital converters (ADCs) is an attractive approach to reducing the power consumption and hardware cost of massive MIMO receivers. However, coarse quantization introduces signal distortion that complicates channel estimation and may degrade communication performance. I have investigated low-complexity beamspace channel denoising for mmWave massive MIMO systems equipped with low-resolution ADCs.

My approach exploits statistical properties of beamspace channels to improve noisy channel estimates while accounting for quantization effects and computational constraints. The research also considers hardware implementation efficiency.

Related publication

H. Park, E. Kim, and J.-W. Choi, "Low-Complexity Beamspace Channel Denoiser for mmWave Massive MIMO with Low-Resolution ADCs," under review.
journal article, submitted in Oct 2026. | related topic: massive MIMO, signal processing, wireless communications | link

Impact of ADC Timing Jitter on Massive MIMO

In addition to quantization effects, practical ADCs suffer from timing jitter, which introduces uncertainty in signal sampling instants. Unlike conventional additive noise, sampling-time errors interact with the temporal characteristics of the received waveform, making their impact on communication performance dependent on signal and system properties.

I have investigated the effects of ADC timing jitter on massive MIMO systems through mathematical modeling and performance analysis. This research aims to provide a better understanding of how sampling imperfections influence the behavior and performance of multi-antenna communication systems.

Related publication

H. Park, G. Kim, and J.-W. Choi, "On the Impact of ADC Timing Jitter in Massive MIMO Systems," under review.
conference proceeding, submitted in Oct 2026. | related topic: massive MIMO, signal processing, wireless communications, hardware impairment

Wireless Resource Allocation and Optimization

Wireless communication systems must efficiently allocate limited resources among users with different channel conditions, traffic demands, and quality-of-service (QoS) requirements. Optimizing these decisions becomes increasingly challenging when instantaneous communication performance must be balanced against queueing delay, reliability, and computational complexity.

My research investigates optimization-based resource allocation methods that incorporate wireless channel characteristics, traffic dynamics, and system-level constraints. I am particularly interested in computationally efficient algorithms that balance multiple performance objectives.

Queue-Aware Scheduling for Multi-User MIMO

User scheduling is an important component of multi-user MIMO systems, where simultaneous transmissions can improve spectral efficiency but also introduce inter-user interference. Scheduling decisions based exclusively on channel conditions may not adequately reflect traffic demands and queueing delays.

I have investigated queue-aware scheduling for mmWave multi-user MIMO systems using optimization techniques based on Lyapunov optimization. By incorporating queue states and wireless channel information into scheduling decisions, this research addresses the tradeoff between achievable communication performance and queueing behavior.

Related publication

H. Park and J.-W. Choi, "Queue-Aware Optimization-Based Scheduling for mmWave Multi-User MIMO Indoor Small Cell," IEEE Communications Letters, 2025.
related topic: massive MIMO, signal processing, wireless communications, network management | link

QoS-Constrained Resource Pattern Design

Integrated sensing and communication (ISAC) systems must support communication and sensing functionalities using shared wireless resources. Designing resource patterns in such systems involves balancing communication reliability and sensing accuracy under limited resources.

I have investigated resource pattern design under QoS constraints, focusing on computationally efficient optimization methods that account for both communication and sensing requirements. This research explores approaches to reducing the search complexity of resource allocation while maintaining the desired system performance.

Related publication

H. Park, G. Kim, Y.-S. Song, and J.-W. Choi, "QoS-Constrained Resource Pattern Design for V2X-ISAC Systems," under review.
journal article, submitted in Oct 2026. | related topic: vehicle-to-everything (V2X), signal processing, wireless communications, network management, integrated sensing and communications (ISAC) | link

Low-Complexity Signal Processing for Massive MIMO Systems

Hardware Impairments in Wireless Communications

Wireless Resource Allocation and Optimization

Other Research Topics