Duangsuwan, Sarun

Preferred name

Duangsuwan, Sarun

Alternative Name

Duangsuwan, S.

Main Affiliation

KMITL Prince of Chumphon Campus

sarun.du@kmitl.ac.th

Now showing 1 - 10 of 38

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Detection of data symbol in a massive MIMO systems for 5G wireless communication
(2017-10-19)
Duangsuwan, Sarun
;
Jamjareegulgarn, Punyawi
The massive MIMO technique has played the most important role in 5G wireless communication. It is anticipated that the new techniques employed in massive MIMO will not only improve peak service data rates significantly, but also enhance capacity, coverage, low-latency, efficiency flexibility, compatibility and convergence, thus meeting the focusing demands imposed by optimal detection. This paper presents the optimal detection of data symbol in massive MIMO for 5G wireless communication. Based on the frequency non-selective fading MIMO channel, we consider three difference detectors for recovering the transmitted data symbols and evaluate their performance for Rayleigh fading and additive white Gaussian noise (AWGN). At the results, we show that the probability of error rate (PER) performance of the detectors are significantly discussed.
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Multipath interference cancellation in multiantenna system with blind channel estimation method
(2017-01-18)
Duangsuwan, Sarun
;
Promwong, Sathaporn
This paper describes on evaluating performance of blind channel estimation methods to cancel the multipath interference from a severity of channel measurement. The proposed methods are considered in an optimal receiver behind the multiantenna system that provides by approaching a zero-forcing (ZF) and minimum mean square error (MMSE) based on the blind signal processing techniques. In this paper, we measured the 4 × 4 MIMO calibration kit with 2.45 GHz sleeve monopole array antennas and employed a vector network analyzer (VNA) to make of channel sounding. The measurement results after data processing is demonstrated by a realistic received signal and we point out how to evaluate the multipath interference with the proposed methods. For the MIMO multiantenna system, we guarantee that the proposed blind channel estimation methods helps to cancel the multipath fading channel and maintains an uncertainty of receiving signals in multiantenna system as perfectly.
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Underwater Drone-Enabled Wireless Communication Systems for Smart Marine Communications: A Study of Enabling Technologies, Opportunities, and Challenges
(2025-11-01)
Duangsuwan, Sarun
;
Klubsuwan, Katanyoo
Highlights: What are the main findings? This paper reviews underwater wireless communication methods, including acoustic, optical, and RF communication, in marine applications, and explores the potential of existing underwater drones. This paper examines the opportunities and challenges of hybrid wireless communication systems for underwater drones. What is the implication of the main finding? This paper considers the integration of underwater drones, IoUT, AI-driven data, VR, and DT for smart marine communications. Underwater drones such as autonomous underwater vehicles (AUVs) and remotely operated vehicles (ROVs) are revolutionizing underwater operations and are essential for advanced marine applications like environmental monitoring, deep-sea exploration, and marine surveillance. In this paper, we concentrate on the enabling technologies and wireless communication strategies for underwater drones. Specifically, we analyze acoustic, optical, and radio frequency (RF) approaches, along with their respective advantages and disadvantages. We investigate the potential of integrating underwater drone-enabled wireless communication systems for smart marine communications. The study highlights the benefits of combining acoustic, optical, and RF methods to improve connectivity and data reliability. A hybrid underwater communication system is ideal for underwater drones because it can reduce latency, increase data throughput, and improve adaptability under various underwater conditions, supporting smart marine communications. The future direction involves developing hybrid communication frameworks that incorporate the Internet of Underwater Things (IoUT), AI-driven data, virtual reality (VR), and digital twin (DT) technologies, enabling a next-generation smart marine ecosystem.
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Analysis of Bottomside Thickness Parameter-Based TEC at Equatorial and Low Latitude Stations for Global Navigation Satellite Systems
(2018-07-02)
Jamjareegulgarn, Punyawi
;
Duangsuwan, Sarun
;
Tangtrakunphaisan, Udomsit
This paper studies the total electron content (TEC) at equatorial and low latitude stations which is computed using an equation of bottomside thickness parameter with correction factor (B2botP-new) during the solar maximum of 24th solar cycle. The computed TEC is used to compute the ionospheric time delay subsequent-tially. The ionospheric stations in this work include Ramey, Ascension Island, and Jicamarca. The results show that the diurnal and seasonal variations of B2botP-new have the same trends as that of B0-obs as well as the B2botP-new values are close to the BO-obs values clearly for all three stations. The electron density diffuses from the equator toward the EIA region (15°N and 15°S) during the period of 14-23 LT. The proposed TEC (TEC-P) are computed using the B2botP-new, and then the ionospheric delay is also computed using the TEC-P. The computed TEC-P and Id-P are close to the TEC-obs and Id-obs, but they should be further studied for a solar cycle (11 years).
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Quantitative Measurement of Path Loss Model Adaptation Using the Least Squares Method in an Urban DVB-T2 System
(2018-01-01)
Keawbunsong, Pitak
;
Duangsuwan, Sarun
;
Supanakoon, Pichaya
;
Promwong, Sathaporn
The aim of this paper was to propose quantitative measurement of path loss model adaptation in urban radio propagation for a second-generation, terrestrial digital video broadcasting standard (DVB-T2) system. The measurement data was analyzed using data processing based on the least squares (LS) method to verify the probabilistic quantitation of realistic data measurement such as mean error (ME), root mean square error (RMSE), and standard deviation of error (SD), as well as relative error (RE). To distinguish the experimental evaluation, the researchers compared between the conventional Hata path loss model and the proposed model. The result showed that path loss based on the proposed model was more accurate in predicting the quantitative measurement of propagation data properly.
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Performance Analysis of Power Outage Probability for Drone based IoT Connectivity Network
(2019-12-01)
Duangsuwan, Sarun
;
Chusongsang, Anna
;
Promwong, Sathaporn
In this paper, we present a performance analysis of power outage probability of using on drone communicated with an internet of things (IoT) connectivity network. We obtain that there are several IoT devices are connected with a drone as simultaneously. Therefore, the power outage probability (POP) is analyzed based on the verifying of drone height which ensures the best performance in term of connectivity network. Experimental results confirm that this analysis is very useful for drone usage as drone small cell or hot-spot to distribute the transmitting power to coverage all IoT devices.
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Path Loss Characterization Using Machine Learning Models for GS-to-UAV-Enabled Communication in Smart Farming Scenarios
(2021-01-01)
Duangsuwan, Sarun
;
Juengkittikul, Phakamon
;
Myint Maw, Myo
The purpose of this paper was to predict the path loss characterization of the ground-to-air (G2A) communication channel between the ground sensor (GS) and unmanned aerial vehicle (UAV) using machine learning (ML) models in smart farming (SF) scenarios. Two ML algorithms such as support vector regression (SVR) and artificial neural network (ANN) were studied to analyze the measured data in different scenarios with Napier and Ruzi grass farms as the measurement locations. The proposed empirical GS-to-UAV two-ray (GUT-R) model and the ML models were compared to characterize path loss prediction models. The performances of the path loss prediction models were evaluated using the statistical error indicators in different measurement locations and UAV trajectories. To obtain the statistical error indicators, the accuracy path loss results of UAV trajectory at 2 m altitudes showed the SVR model (MAE = 1.252 dB, RMSE = 3.067 dB, and R2 = 0.972) and the ANN model (MAE = 1.150 dB, RMSE = 2.502 dB, and R2 = 0.981) for the Napier scenario. In the Ruzi scenario, the SVR model (MAE = 1.202 dB, RMSE = 2.962 dB, and R2 = 0.965) and the ANN model (MAE = 1.146 dB, RMSE = 2.507 dB, and R2 = 0.983) were presented. For UAV trajectory at 5 m altitudes, the SVR model (MAE = 2.125 dB, RMSE = 4.782 dB, and R2 = 0.933) and the ANN model (MAE = 2.025 dB, RMSE = 4.439 dB, and R2 = 0.950) were resulted in the Napier scenario. In the Ruzi scenario, the SVR model (MAE = 2.112 dB, RMSE = 4.682 dB, and R2 = 0.935) and the ANN model (MAE = 2.016 dB, RMSE = 4.407 dB, and R2 = 0.954) were displayed. The proposed ML models using SVR and ANN can optimally predict the path loss characterization in SF scenarios, where the accuracy was 95% for the SVR and 97% for the ANN.
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Empirical Path Loss Channel Characterization Based on Air-to-Air Ground Reflection Channel Modeling for UAV-Enabled Wireless Communications
(2021-01-01)
Supramongkonset, Jatuporn
;
Duangsuwan, Sarun
;
Maw, Myo Myint
;
Promwong, Sathaporn
The purpose of this work was to investigate the air-to-air channel model (A2A-CM) for unmanned aerial vehicle- (UAV-) enabled wireless communications. Specifically, a low-altitude small UAV needs to characterize the propagation mechanisms from ground reflection. In this paper, the empirical path loss channel characterizations of A2A ground reflection CM based on different scenarios were presented by comparing the wireless communication modules for UAVs. Two types of wireless communication modules both WiFi 2.4 GHz and LoRa 868 MHz frequency were deployed to study the path loss channel characterization between Tx-UAV and Rx-UAV. To investigate the path loss, three types of experimental channel models, such as CM1 grass floor, CM2 soil floor, and CM3 rubber floor, were considered under the ground reflection condition. The analytical A2A Two-Ray (A2AT-R) model and the modified Log-Distance model were simulated to compare the correlation with the measurement data. The measurement results in the CM3 rubber floor scenario showed the impact from the ground reflection at 1 m to 3 m Rx-UAV altitudes both 2.4 GHz and 868 MHz which was converged to the A2AT-R model and related to the modified Log-Distance model above 3 m. It clear that there is no ground reflection effect from the CM1 grass floor and CM2 soil floor. This work showed that the analytical A2AT-R model and the modified Log-Distance model can deploy to model the path loss of A2A-CM by using WiFi and LoRa wireless modules.
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A Study of A2A Channel Modeling for Small UAV-Enabled Wireless Communication
(2022-01-01)
Supramongkonset, Jatuporn
;
Duangsuwan, Sarun
;
Promwong, Sathaporn
The objective of this work is to study the ground reflection modeling case study of air-to-air (A2A) channel modeling for an unmanned aerial vehicle (UAV)-enabled wireless communications. We consider the Internet of Things (IoT) WiFi modules at 2.4 GHz standard enabled with the transmitter Tx- UAV and the receiver Rx-UAV. The received signal strength indicator (RSSI) and path loss characteristics are addressed by using the free space path loss (FSPL) model, air-to-air two- ray (A2AT-R) model, and the modified Log-distance path loss model. The measurement results indicated that the limit of power constraint of WiFi modules was a level at 10 m Rx-UAV altitude, and the received sensitivity was limited at -95 dBm. It is shown that the impact of ground reflection path loss was characterized at 1-3 m Rx-Uavaltitudes which are accordingly related to the A2AT-R model and the relative resulting with the modified Log- distance model above 4 m Rx-UAV altitudes.
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Performance Analysis of Unmanned Aerial Vehicle Assisted Wireless IoT Sensors Based on Air-to-Ground Communication Model for Smart Farming
(2023-01-01)
Duangsuwan, Sarun
;
Promwong, Sathaporn
We used an unmanned aerial vehicle (UAV) and IoT as a new platform for soil moisture monitoring based on the air-to-ground (A2G) communication model. We investigated an energyefficient UAV trajectory by considering the power outage probability and transmission rate for UAV-assisted wireless IoT sensor connectivity. We considered the closed-form power outage probability for IoT sensors located within the coverage zone of a UAV drone small cell. We conducted experiments in the Napier and Ruzi grass farms with IoT sensors for detecting soil moisture along a drip line irrigation system located in the fields. The power outage probability is given for different UAV heights and transmission rates, which contributes to reliable communication with IoT sensors.

Duangsuwan, Sarun

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