Boonsrimuang, Pisit

Preferred name

Boonsrimuang, Pisit

Alternative Name

Boonsrimuang, P.

Main Affiliation

School of Engineering

pisit.bo@kmitl.ac.th

Now showing 1 - 10 of 22

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An Effective Channel Estimation for Massive MIMO–OFDM System
(2020-09-01)
Mata, Tanairat
;
Boonsrimuang, Pisit
The massive Multiple-Input and Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO–OFDM) system provides a high data transmission for the next generation mobile communication i.e. 4G, 5G, etc. In the practical MIMO–OFDM system, N points of IFFT/FFT is larger than M data subcarriers (N> M) in each OFDM symbol to reject the aliasing after D/A converter. To demodulate information data, the channel responses for all MIMO channel links need to be estimated so as to employ in MIMO data detection for demodulation at the receiver. The discrete Fourier transform estimator (DFE) was proposed for the system which can estimate the MIMO channels accurately when N= M. However, its accuracy will be hugely degraded when N> M because of the oversampling of data transmission. To improve the estimation accuracy when N> M, the maximum likelihood estimator (MLE) was proposed for the system which can achieve higher estimation accuracy than that of the DFE. However, its accuracy will be degraded a lot in the massive MIMO–OFDM system when N> M, due to the estimation error increased in proportion to the increasing of N<inf>T</inf> transmit antennas. To solve these problems, this paper proposes a direct time-domain estimator (DTE) with preamble symbol with scattered-pilot (preamble-SCP) for the massive MIMO–OFDM system when N> M. In the proposed method, it is presented with three salient features; achieving higher estimation accuracy with keeping almost the same computational complexity as the conventional estimators, improving Bit- Error- Rate (BER) with low-complexity MIMO data detection, and providing higher transmission data rate compared with the MLE. Using the normalizedMSE, BER and throughput evaluated by computer simulations, it can be verified that the proposed DTE with preamble-SCP obviously provides higher estimation accuracy, better BER with low-complexity MIMO data detection, and much higher transmission data rate which is approximately 32.5 Mbps gain over the MLE at 5 MHz-BW respectively.
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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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Performance Evaluation of the M-QAM Enhanced Subcarrier Index Modulation in the Multipath Fading Channel with the Non-Linear Amplifier
(2023-01-01)
Mi, Si Sar
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Siriwanitpong, Aphitchaya
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Boonsrimuang, Pornpawit
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Boonsrimuang, Pisit
One of the specifications of the next-generation mobile communication is to reduce the power consumption to transmit the signal. Enhanced subcarrier index modulation (eSIM-OFDM) is a novel multicarrier modulation scheme. However, the eSIM-OFDM scheme degraded a little frequency utilization than the conventional orthogonal frequency division multiplexing (C-OFDM) scheme at the higher M-QAM modulation techniques. It has a special feature to transmit the signal when compared with the C-OFDM scheme. Its special feature increased the power efficiency and improved the bit error rate (BER) performance than the C-OFDM scheme. In the literature, the BER performance of the eSIM-OFDM scheme is analyzed in the additive white Gaussian noise (AWGN) channel and compared BER performance of the C-OFDM scheme. But in the literature, the BER performance of the eSIM-OFDM scheme and C-OFDM scheme were not evaluated in the multipath fading channel. Because eSIM-OFDM includes a special feature called inactive and active subcarriers. In this paper, we will evaluate the BER performance of higher order modulated eSIM-OFDM signal in the multipath Rician fading channel by using the Zadoff-Chu sequence pilot in the non-linear mobile communication system and will compare the BER performance with the C-OFDM scheme. From the simulation results, we can see that the BER performance of the eSIM-OFDM scheme is much better than the C-OFDM scheme in the multipath Rician fading non-linear channel. In this paper, computer simulation will evaluate the BER performance of the higher order modulated eSIM-OFDM scheme in the multipath Rician fading non-linear channel.
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A Deep Learning-Based Channel Estimation for High-Speed Train Environments
(2022-01-01)
Siriwanitpong, Aphitchaya
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Boonsrimuang, Pornpawit
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Mori, Kazuo
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Boonsrimuang, Pisit
A communication system in the railway track environment provides constant channel coefficient property as trains travel on a predetermined route and speed. This channel characteristic provides advantages in designing a channel estimation. This paper proposes a channel estimation algorithm based on a deep learning network called the Convolutional Neural Network (CNN). The CNN has trained with an average Channel Frequency Response (CFR) dataset on railway track environments with different multi-path fading and noises. The CFR can be estimated using a known pilot symbol as conventional methods. The estimated CFR is also used to select the right CFR for multi-path fading compensation. The CNN will then classify the estimated CFR by recognizing estimated channel characteristics and determining the most matched estimated channel for equalization of received signals in a data channel. The simulation results show that the performance of the deep learning algorithms outperforms that of the conventional algorithms. Furthermore, the proposed method delivers better Bit Error Rate (BER) performance since the deep learning-based channel estimation can categorize the features of the channel characteristics with different multi-path and doppler shifts.
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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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Adaptive K-Repetition Transmission Employing Site Diversity Reception for 5G NR Uplink Grant-Free URLLC
(2023-01-01)
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 Internet of Things (IoT) is currently being employed for wireless real-time interactive systems with ultra-reliable and low-latency communications (URLLC) that provide high service quality for the fifth generation (5G) new radio (NR) standard. Grant-free (GF) transmission with K-Repetition (K-Rep) is a transmission technique that aims to meet the URLLC requirements. However, the K-Rep increases the number of packet transmissions, which may cause severe interference and substantial energy consumption. This paper employs site diversity reception to the GF K-Rep scheme and proposes its adaptive control mechanism to decrease the number of repeated transmissions in order to improve communication reliability and energy consumption for IoT URLLC users. According to our simulation results, the proposed site diversity reception for the K-Rep scheme can significantly improve the reliability compared with the conventional K-Rep under single-cell reception. Additionally, the proposed adaptive K-Rep control scheme can reduce the transmission energy consumption greatly, hence saving battery resource of IoT URLLC users.
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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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Low-complexity based TDE Method for OFDM signal in higher time-varying fading channels
(2020-01-01)
Tanangsanakool, Saha
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Reangsuntea, Pongsathorn
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Mori, Kazuo
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Boonsrimuang, Pisit
Orthogonal Frequency Division Multiplexing (OFDM) signal would be damaged significantly by inter-carrier interference (ICI) in higher time-varying fading channels. The ICI leads to fatal degradation of bit error rate (BER) performance due to the loss of orthogonality among subcarriers. To solve this problem, this paper proposes a high accuracy time-domain channel impulse response (CIR) estimation method and low-complexity based time-domain equalization (TDE) method for solving the simultaneous equations instead of using an inverse matrix calculation which can achieve better BER performance and lower computation complexity even in higher time-varying fading channels. The salient features of proposed method are to employ a time-domain training sequence (TS) in the estimation of channel impulse response (CIR) instead of using pilot subcarriers in the frequency domain and to employ the time domain equalization (TDE) method with maximum likelihood (ML) estimation instead of using a conventional frequency domain equalization (FDE) method. This paper also proposes a low-complexity iterative method for solving the simultaneous equations instead of using an inverse matrix calculation, which remains the computation complexity up to 7.8% of inverse matrix calculation with the same BER performance but achieves the BER performance when compared with the conventional method. This paper presents various simulation results in higher time-varying fading channels (vehicle speed ≈ 381 km/hrs) to demonstrate the effectiveness of the proposed method as compared with conventional FDE and TDE methods.
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System Performance Enhancement with Energy Efficiency Based Sleep Control for 5G Heterogeneous Cellular Networks
(2022-04-01)
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
This paper presents an improved sleep control algorithm for small base stations (SBSs) in 5G New Radio (NR) heterogeneous cellular networks (HetNets). HetNets consist of various base station tiers, including macro base stations (MBSs) and small base stations (SBSs), and have been suggested as a promising solution to enhance wireless coverage and network capacity, employing many SBSs into the MBS coverage. However, power consumption increases significantly as a result of an increase in the number of the SBSs. To solve this problem, the SBS sleep control has been proposed to reduce power consumption for the SBSs and improves energy efficiency, whereas it deteriorates system throughput compared with no sleep control system, consequently degrading the quality of service (QoS) performance at user equipments (UEs). This paper proposes an enhanced algorithm for SBS sleep control based on energy efficiency as a decision criterion for SBS operating state. From the evaluation results through computer simulation, the proposed scheme can provide improved performance for both energy efficiency and system throughput simultaneously, that is it can improve energy efficiency while maintaining almost the same system throughput as the no sleep control system. Concretely, the proposed scheme has the 14.89% improvement in energy efficiency while providing almost the same system throughput of over 99%, compared with no sleep control system

Boonsrimuang, Pisit

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