arXiv subjects

Byung Moo Lee

Publications and source records attributed to Byung Moo Lee.

3 recordsLinked to original sources

arXiv2026

Efficient Automatic Modulation Classification for Next-Generation Wireless Networks

With the imminent development of sixth-generation (6G) networks, there will be a demand for high-accuracy, computationally-efficient, and low-inference time automatic modulation classification (AMC) algorithms. To address this need, we propose a new deep-learning based model for AMC that is called the threshold denoise recurrent neural network (TDRNN). The TDRNN combines an adaptive threshold denoising (TD) algorithm and a recurrent neural network (RNN) that together achieve high accuracy and fast inference. The TD module adaptively reduces the noise level of the received signal, while the RNN module performs the modulation classification on the denoised result. The two subsystems are jointly optimized to reach the optimal architecture. The proposed TDRNN is evaluated for various modulation schemes and signal-to-noise ratios (SNR). The experimental results demonstrate that the TDRNN outperforms existing methods in terms of accuracy, speed, and computational complexity making it an ideal solution for 6G wireless communication systems.

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arXiv2025

GPS-Aided Deep Learning for Beam Prediction and Tracking in UAV mmWave Communication

Millimeter-wave (mmWave) communication enables high data rates for cellular-connected Unmanned Aerial Vehicles (UAVs). However, a robust beam management remains challenging due to significant path loss and the dynamic mobility of UAVs, which can destabilize the UAV-base station (BS) link. This research presents a GPS-aided deep learning (DL) model that simultaneously predicts current and future optimal beams for UAV mmWave communications, maintaining a Top-1 prediction accuracy exceeding 70% and an average power loss below 0.6 dB across all prediction steps. These outcomes stem from a proposed data set splitting method ensuring balanced label distribution, paired with a GPS preprocessing technique that extracts key positional features, and a DL architecture that maps sequential position data to beam index predictions. The model reduces overhead by approximately 93% (requiring the training of 2 ~ 3 beams instead of 32 beams) with 95% beam prediction accuracy guarantees, and ensures 94% to 96% of predictions exhibit mean power loss not exceeding 1 dB.

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arXiv2019

Simultaneous Harvest-and-Transmit Ambient Backscatter Communications under Rayleigh Fading

Ambient backscatter communications is an emerging paradigm and a key enabler for pervasive connectivity of low-powered wireless devices. It is primarily beneficial in the Internet of things (IoT) and the situations where computing and connectivity capabilities expand to sensors and miniature devices that exchange data on a low power budget. The premise of the ambient backscatter communication is to build a network of devices capable of operating in a battery-free manner by means of smart networking, radio frequency (RF) energy harvesting and power management at the granularity of individual bits and instructions. Due to this innovation in communication methods, it is essential to investigate the performance of these devices under practical constraints. To do so, this article formulates a model for wireless-powered ambient backscatter devices and derives a closed-form expression of outage probability under Rayleigh fading. Based on this expression, the article provides the power-splitting factor that balances the tradeoff between energy harvesting and achievable data rate. Our results also shed light on the complex interplay of a power-splitting factor, amount of harvested energy, and the achievable data rates.

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