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Optimized CNN-based channel estimation for zero-padded uplink OFDMA in 5G new radio over fast-fading channels
(2026-07-01)
Mata, Tanairat
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Boonsrimuang, Pisit
This paper addresses a pilot-assisted channel estimation applicable to the uplink orthogonal frequency-division multiple-access with zero-padding in a 5G new radio. The adjacent uplink subchannels in the frequency domain are allocated separately for each user, and each subchannel assigns the pilot signal independently. This paper proposes a convolutional neural network-based channel estimation, including one-dimensional and two-dimensional architectures, designed to optimize the handling of rapid fading channel variations encountered in high-mobility scenarios. The estimation process leverages the subchannels of each user to enhance accuracy. Simulation results demonstrate the effectiveness of the proposed method in offering a better bit-error rate and a higher transmission data rate than the conventional channel estimation methods under challenging conditions. Finally, this paper discusses the considerable computational complexity of aspects of the lightweight two convolutional neural network architectures.
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Evolution of STBC-based OFDM-IM for wireless vehicular communication
(2026-01-01)
Mata, Tanairat
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Boonsrimuang, Pisit
This paper studies wireless vehicular communication (VehCom) in intelligent transportation systems using an orthogonal frequency division multiplexing with index modulation (OFDM-IM). In the concept of IM, data is transmitted not only through the modulated symbols but also via the indices of the active subcarriers. In contrast to the original OFDM, OFDM-IM activates only non-zero subcarriers, increasing energy efficiency. However, the pilot-assisted channel estimation (CE) method is a significant challenge in OFDM-IM, where the desired pilot subcarrier interval is related to the OFDM-IM subblock length. This paper proposes a walsh-scattered pilot-assisted CE for OFDM-IM VehCom. The optimum walsh-scattered pilot assignment is proposed to improve the transmission efficiency. Furthermore, a space-time block code with a high transmit diversity gain is employed for OFDM-IM VehCom to enhance VehCom's signal quality. The results show that the proposed method performs higher CE accuracy and better bit-error rate with significant spectral and energy efficiencies than conventional methods.
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Robust Channel Estimation for MIMO-OFDM-IM Full-Duplex Relaying under Residual Self-Interference in Time-Varying Fading
(2026-01-01)
Mata, Tanairat
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Boonsrimuang, Pisit
Full-duplex relaying can significantly improve spectral efficiency in vehicular communications, but its practical performance is constrained by residual self-interference (SI) and rapidly time-varying channels. This work proposes a Walsh–Hadamard and null (WHN) pilot with maximum likelihood (ML)–based channel estimation (CE) for MIMO OFDM-IM full-duplex relays. Unlike prior designs that prioritize either low complexity or estimation accuracy, the proposed WHN scheme improves the performance–complexity trade-off by leveraging asymmetric pilot intervals and structured orthogo nalization. Two widely recognized baselines are considered for comparison: (i) cyclic-shifted Zadoff–Chu (CS-ZC) preamble pilots representing the low-complexity DFT-domain benchmark, and (ii) scattered pilot-and-null (PN) patterns with ML refinement representing the high-accuracy benchmark used in LTE/5G systems. Results reveal a consistent carrier-to-noise power ratio (CNR)-dependent crossover: WHN outperforms CS-ZC when CNR≥28dBandsurpassesPNundermoderate-to-high mobility. At 35 dB CNR, WHN provides up to 35% BER reduction with only 29% complexity overhead. A standard-compliant link budget analysis further indicates non-trivial coverage gains under the evaluated Doppler regimes and considered residual SI model. All simulation assumptions, Doppler profiles, and SI models follow V2X guidelines, and results are validated across multiple random seeds. Overall, WHN offers a complexity-aware CE solution for full-duplex V2X systems.
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Artificial Neural Network for Air Pollutant Concentration Predictions Based on Aircraft Trajectories over Suvarnabhumi International Airport
(2025-04-01)
Kamsing, Patcharin
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Cao, Chunxiang
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Boonpook, Wuttichai
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Boonprong, Sornkitja
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Xu, Min
Air pollutant concentration prediction is essential not only for effective air quality management but also for planning aircraft and ground vehicle route networks in terminal areas. In this work, an artificial neural network (ANN) is used to predict the concentration levels of four types of air pollutants (CO, NO<inf>2</inf>, PM<inf>2.5</inf>, and PM<inf>10</inf>) at Suvarnabhumi International Airport. By leveraging Automatic Dependent Surveillance-Broadcast (ADS-B) historical data, aircraft trajectory pattern clustering is implemented by using K-means and Gaussian mixture model (GMM) clustering algorithms. Then, those trajectory patterns are inputted together with other flight data into ANN computation processes, resulting in an effective air pollutant prediction model for each kind of focus pollutant. The results demonstrate that the mean square errors (MSEs) of the predicted models for CO and PM<inf>2.5</inf> have acceptable values of 51.7622 and 53.9682, respectively, while the predicted model for NO<inf>2</inf> and PM<inf>10</inf> has MSEs of 139.6674 and 124.2517, respectively. This study contributes to the advancement of air pollutant prediction methodologies, facilitating better decision-making processes, proactive air quality management, and route network planning at airports. Although some prediction models for focused air pollutants have slightly high MSEs, further study is needed to enhance the prediction model capacity.
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Generating Large-Scale Datasets for Spacecraft Pose Estimation via a High-Resolution Synthetic Image Renderer
(2025-04-01)
Hematulin, Warunyu
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Kamsing, Patcharin
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Phisannupawong, Thaweerath
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Panyalert, Thanayuth
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Manuthasna, Shariff
The trend toward conducting vision-based spacecraft pose estimation using deep neural networks, which necessitates accurately labeled datasets for training, is addressed in this paper. A method for generating an image regression-labeled dataset for spacecraft pose estimation through simulations involving Unreal Engine 5 is proposed herein. This work provides detailed algorithms for pose sampling and image generation, making it easy to reproduce the employed dataset. The dataset consists of images obtained under harsh lighting conditions and high-resolution backgrounds, featuring spacecraft models including Dragon, Soyuz, Tianzhou, and the ascent vehicle of Chang’E-6. The dataset comprises 40,000 high-resolution images, which are evenly distributed, with 10,000 images for each spacecraft model in scenes with both the Earth and the Moon. Each image is labeled with multivariate pose vectors that represent the relative position and attitude of the corresponding spacecraft with respect to the camera. This work emphasizes the critical role of realistic simulations in creating cost-effective synthetic datasets for training neural network-based pose estimators and publicly available for further study.
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Joint Iterative Satellite Pose Estimation and Particle Swarm Optimization
(2025-02-01)
Kamsing, Patcharin
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Cao, Chunxiang
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Zhao, You
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Boonpook, Wuttichai
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Tantiparimongkol, Lalida
Satellite pose estimation (PE) is crucial for space missions and orbital maneuvering. High-accuracy satellite PE could reduce risks, enhance safety, and help achieve the objectives of close proximity and docking operations for autonomous systems by reducing the need for manual control in the future. This article presents a joint iterative satellite PE and particle swarm optimization (PE-PSO) method. The PE-PSO method uses the number of batches derived from satellite PE as the number of particles and keeps the number of epochs from the satellite PE process as the number of epochs for PSO. The objective function of PSO is the training function of the implemented network. The output obtained from the previous objective function is applied to update the new positions of the particles, which serve as the inputs of the current training function. The PE-PSO method is tested on synthetic Soyuz satellite image datasets acquired from the Unreal Rendered Spacecrafts On-Orbit Datasets (URSOs) under different preset hyperparameters. The proposed method significantly reduces the incurred loss, especially during the batch-processing operation of each epoch. The results illustrate the accuracy improvement attained by the PE-PSO method over epoch processing, but its time consumption is not distinct from that of the conventional method. In addition, PE-PSO achieves better performance by reducing the mean position estimation error by 13.1% and the mean orientation estimation error on the testing dataset by 29.1% based on the pretrained weights of Common Objects in Context (COCO). Additionally, PE-PSO improves the accuracy of the Soyuz_hard-based weight by 7.8% and 0.3% in terms of the mean position estimation error and mean orientation estimation error, respectively.
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Deep Learning-Based Channel Estimation With 1D CNN for OFDM Systems Under High-Speed Railway Environments
(2025-01-01)
Siriwanitpong, Aphitchaya
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Sanada, Kosuke
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Hatano, Hiroyuki
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Mori, Kazuo
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Boonsrimuang, Pisit
In OFDM wireless communications, channel estimation performance is compromised in high-speed railway environments owing to extremely fast multipath fading and severe Doppler effect. Recently, a deep learning approach has been employed to improve the channel estimation performance, however it encounters significant challenges due to its high computational complexity. In order to deal with these challenges, this paper proposes channel estimation employing deep learning with one-dimensional convolutional neural network (1D CNN) schemes to enhance conventional least squares (LS) estimation. The first scheme provides better performance compared to conventional LS estimation. However, it is only suitable for OFDM systems with full pilot symbols, leading to decreased transmission efficiency and high complexity. In order to address those problems, the second scheme develops 1D CNN-based channel estimation employing scattered pilot symbols to enhance transmission efficiency and reduce computational complexity. In comparison to conventional LS estimation and deep learning-based channel estimation with bi-gated recurrent unit (bi-GRU), the performance evaluation demonstrates that the proposed 1D CNN-based schemes simultaneously improve channel estimation performance, transmission efficiency, and reduce computational complexity.
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Efficient reduction of peak-to-average power ratio in multiple-input multiple-output orthogonal frequency-division multiplexing system by shuffling cluster sequences
(2024-12-01)
Mi, Si Sar
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Mata, Tanairat
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Boonsrimuang, Pornpawit
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Boonsrimuang, Pisit
We propose a shuffling cluster sequence technique without separate side information (SI) for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. In the proposed technique, the active subcarriers over two consecutive OFDM symbols are divided into (Formula presented.) clusters, and each cluster (packet frame) includes a header for ID#cluster and payload for (Formula presented.). The (Formula presented.) clusters are shuffled to reduce the peak-to-average power ratio (PAPR) of the time-domain OFDM signal, which includes the information data and SI signals, with a low computational complexity. At the receiver, the information data can be correctly reconstructed by ID#cluster in the header of each cluster, achieving a smaller bit error rate than the conventional MIMO-OFDM system without PAPR reduction. Moreover, our technique is comparable with the conventional partial transmit sequence technique without the impact of a separate SI signal even when increasing the number of transmitter antennas in a nonlinear multipath fading channel.
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Adaptive K-Repetition Transmission with Site Diversity Reception for Energy-Efficient Grant-Free URLLC in 5G NR
(2024-01-01)
Dataesatu, Arif
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Sanada, Kosuke
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Hatano, Hiroyuki
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Mori, Kazuo
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Boonsrimuang, Pisit
The fifth-generation (5G) new radio (NR) standard employs ultra-reliable and low-latency communication (URLLC) to provide real-time wireless interactive capability for the internet of things (IoT) applications. To satisfy the stringent latency and reliability demands of URLLC services, grant-free (GF) transmissions with the K-repetition transmission (K-Rep) have been introduced. However, fading fluctuations can negatively impact signal quality at the base station (BS), leading to an increase in the number of repetitions and raising concerns about interference and energy consumption for IoT user equipment (UE). To overcome these challenges, this paper proposes novel adaptive K-Rep control schemes that employ site diversity reception to enhance signal quality and reduce energy consumption. The performance evaluation demonstrates that the proposed adaptive K-Rep control schemes significantly improve communication reliability and reduce transmission energy consumption compared with the conventional K-Rep scheme, and then satisfy the URLLC requirements while reducing energy consumption.
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Pilot-Assisted Channel Estimation for SFBC MIMO-OFDM with Index Modulation in Higher Time-Varying Fading Channel
(2024-01-01)
Mata, Tanairat
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Boonsrimuang, Pisit
Orthogonal frequency division multiplexing (OFDM) with Index modulation can provide higher spectral and energy efficiencies. For multiple-input, multiple-output OFDM with space-frequency block coding (SFBC MIMO-OFDM), the transmitter can send the information signal with high transmit diversity gain to the receiver, improving system performance. In this paper, we propose a pilot-assisted channel estimation by using SFBC MIMO-OFDM with index modulation. The proposed system can perform a better bit-error-rate (BER) performance and higher transmission data rate than the conventional system in a higher time-varying fading channel.

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