• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.11-19
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• 2021

In this paper, we propose a non-orthogonal multiple access (NOMA) method based on channel alignment to simultaneously transmit multiple unicast and multicast streams in frequency-efficient manner. In this method, all receivers in a multicast cluster use the receive beamforming vectors that align their channels, and the base station uses the aligned channel information to design the transmit beamforming vectors that eliminate interference between multicast clusters. Using the effective receive channel information combined with the transmit beamforming vectors, unicast receivers design their own receive beamforming vectors that eliminate interference between unicast receivers. Since the proposed method effectively eliminates the interference, it achieves a higher sum rate than the existing orthogonal multiple access (OMA) method in high SNR regions. In addition, we present a hybrid method that exploits the benefits of the proposed NOMA method and the existing OMA method. Depending on the channel state, the hybrid method adaptively employs the existing OMA method, which improves the received signal power, in low SNR regions and the proposed NOMA method, which effectively eliminates the interference, in high SNR regions, thereby achieving a good sum rate over the entire SNR region.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.2
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• pp.267-273
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• 2021

Non-orthogonal multiple access is the promised candidates in the next generation wireless networks to improve the spectral efficiency by superposing multiple signals. In general, the superposition coding is performed using the difference in channel gain between users based on the user's power allocation. However, when user pairs have the similar channel gain problem, NOMA can not be allowed in the scenario. To overcome this problem, phase rotation based NOMA is presented to increase minimum distance between superposed signals in the constellation point. This paper proposed a novel non-orthogonal multiple access based index modulation using phase rotation. The additional bits can transfer using the index bits that is allocated according to the activated state of the phase rotation. Simulation results are shown that bit error rate and achievable sum rate are better than conventional NOMA.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.37-42
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• 2021

NOMA (Non-orthogonal multiple Access) system has been focused on the next generation cellular system for higher spectral efficiency. However, this requires user scheduling as the NOMA system is a multi-user system which accesses simultaneously. There are two representative scheduling schemes, proportionate scheduling (FP) and cumulative distribution function (CFD) scheduling. The PF scheduling is applied, the cell edge user is hard to obtain a transmit opportunity. Recently, CDF scheduling is obviously noted that it offers the same possibility of transmission for a user regardless of the location in a cell. We consider an uplink NOMA system with CDF scheduling, and obtain the channel access probabilities, the outage probabilities of the system with different number of users and different kinds of weights through simulation. The results indicate that the likelihood of each user accessing the channel is the same and the probability of failure decreases as the number of users increases. We found that the effect of the probability of failure is negligible as the weight of the cell edge user increases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.1
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• pp.114-121
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• 2022

In this paper, we consider a user centric clustering in order to guarantee the performance of the users in cell free multiple-input multiple-output (MIMO) network. In the user centric clustering scheme, by using large scale fading coefficients of the connected access points (APs), each user decides own cluster with the APs having the higher the large scale fading coefficients than threshold value compared to the highest large scale fading coefficient. In the determined user centric clusters, the APs design the beamformers and power allocations in the distributed manner and the APs cooperatively transmit data to users by using beamformers and power allocations. In the simulation results, we verify the performance of user centric clustering in terms of the spectral efficiency and we also find the optimal threshold value in the given configuration.

• Journal of IKEEE
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• v.26 no.2
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• pp.287-291
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• 2022

In general, as the amplitude and phase information of the physical layer channel impulse response change rapidly in time and frequency according to the high-speed movement of the vehicles in V2X communication systems, it is difficult to accurately estimate these channels at the receiving end. In order to effectively overcome this problem, midamble-based channel estimation methods in which mid-ambles are periodically inserted into a packet have been recently considered. However, as the number of midambles increases, we suffer from the spectral efficiency loss. To relieve such a loss, in this paper, we propose a new bidirectional averaging channel estimation method that combines the existing data pilot-based channel estimation methods and the mid-ambles. Finally, through the simulation results, we demonstrate that the proposed method outperforms the existing mid-amble method in terms of packet error rate with fewer number of mid-ambles.

• Journal of IKEEE
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• v.26 no.1
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• pp.62-66
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• 2022

Recently, there have been many research efforts based on data-based deep learning technologies to deal with the interference problem between heterogeneous wireless communication devices in unlicensed frequency bands. However, existing approaches are commonly based on the use of complex neural network models, which require high computational power, limiting their efficiency in resource-constrained network interfaces and Internet of Things (IoT) devices. In this study, we address the problem of classifying heterogeneous wireless technologies including Wi-Fi and ZigBee in unlicensed spectrum bands. We focus on a data-driven approach that employs a supervised-learning method that uses received signal strength indicator (RSSI) data to train Deep Convolutional Neural Networks (CNNs). We propose a simple measurement methodology for collecting RSSI training data which preserves temporal and spectral properties of the target signal. Real experimental results using an open-source 2.4 GHz wireless development platform Ubertooth show that the proposed sampling method maintains the same accuracy with only a 10% level of sampling data for the same neural network architecture.

• Journal of Radiation Protection and Research
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• v.47 no.4
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• pp.214-225
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• 2022

Background: The conventional cerium-doped Gd₂Al₂Ga₃O₁₂ (GAGG(Ce)) scintillator-based gamma-ray imager has a bulky detector, which can lead to incorrect positioning of the gammaray source if the shielding against background radiation is not appropriately designed. In addition, portability is important in complex environments such as inside nuclear power plants, yet existing gamma-ray imager based on a tungsten mask tends to be weighty and therefore difficult to handle. Motivated by the need to develop a system that is not sensitive to background radiation and is portable, we changed the material of the scintillator and the coded aperture. Materials and Methods: The existing GAGG(Ce) was replaced with Bi₄Ge₃O₁₂ (BGO), a scintillator with high gamma-ray detection efficiency but low energy resolution, and replaced the tungsten (W) used in the existing coded aperture with lead (Pb). Each BGO scintillator is pixelated with 144 elements (12 × 12), and each pixel has an area of 4 mm × 4 mm and the scintillator thickness ranges from 5 to 20 mm (5, 10, and 20 mm). A coded aperture consisting of Pb with a thickness of 20 mm was applied to the BGO scintillators of all thicknesses. Results and Discussion: Spectroscopic characterization, imaging performance, and image quality evaluation revealed the 10 mm-thick BGO scintillators enabled the portable gamma-ray imager to deliver optimal performance. Although its performance is slightly inferior to that of existing GAGG(Ce)-based gamma-ray imager, the results confirmed that the manufacturing cost and the system's overall weight can be reduced. Conclusion: Despite the spectral characteristics, imaging system performance, and image quality is slightly lower than that of GAGG(Ce), the results show that BGO scintillators are preferable for gamma-ray imaging systems in terms of cost and ease of deployment, and the proposed design is well worth applying to systems intended for use in areas that do not require high precision.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.93-101
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• 2024

Accurate field crop classification is essential for various agricultural applications, yet existing methods face challenges due to diverse crop types and complex field conditions. This study aimed to address these issues by combining support vector machine (SVM) models with multi-seasonal unmanned aerial vehicle (UAV) images, texture information extracted from Gray Level Co-occurrence Matrix (GLCM), and RGB spectral data. Twelve high-resolution UAV image captures spanned March-October 2021, while field surveys on three dates provided ground truth data. We focused on data from August (-A), September (-S), and October (-O) images and trained four support vector classifier (SVC) models (SVC-A, SVC-S, SVC-O, SVC-AS) using visual bands and eight GLCM features. Farm maps provided by the Ministry of Agriculture, Food and Rural Affairs proved efficient for open-field crop identification and served as a reference for accuracy comparison. Our analysis showcased the significant impact of hyperparameter tuning (C and gamma) on SVM model performance, requiring careful optimization for each scenario. Importantly, we identified models exhibiting distinct high-accuracy zones, with SVC-O trained on October data achieving the highest overall and individual crop classification accuracy. This success likely stems from its ability to capture distinct texture information from mature crops.Incorporating GLCM features proved highly effective for all models,significantly boosting classification accuracy.Among these features, homogeneity, entropy, and correlation consistently demonstrated the most impactful contribution. However, balancing accuracy with computational efficiency and feature selection remains crucial for practical application. Performance analysis revealed that SVC-O achieved exceptional results in overall and individual crop classification, while soybeans and rice were consistently classified well by all models. Challenges were encountered with cabbage due to its early growth stage and low field cover density. The study demonstrates the potential of utilizing farm maps and GLCM features in conjunction with SVM models for accurate field crop classification. Careful parameter tuning and model selection based on specific scenarios are key for optimizing performance in real-world applications.

• The Journal of the Acoustical Society of Korea
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• v.27 no.2
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• pp.66-74
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• 2008

In this paper, we propose new techniques that can improve the quality of AAC and SBR encoders comprised in low bitrate HE-AAC. To reduce the pre-echo artifacts often occurring for transient blocks in AAC, we propose an extended Temporal Noise Shaping (sTNS) in which the frequency range is selectively extended down to the low-frequency region. Also, for he high-frequency region being coded by SBR encoder, tones are identified through a sinusoidal modeling and their frequencies are adjusted within the QMF band in order to reduce the noise floor due to aliasing. Spectrograms of the decoded signals were compared and listening tests were conducted to evaluate the proposed algorithm. Results confirmed the effectiveness of the proposed algorithm.

• Journal of Korean Society of Forest Science
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• v.90 no.4
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• pp.476-487
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• 2001

In this study, three tree species (Populus euramericana, Kalopanax pictus and Quercus serrata) exhibiting different levels of shade tolerance were employed to investigate photosynthetic responses to the lower light condition on forest floors. Chlorophyll contents, spectral properties and photosynthetic characteristics were examined by using the tree species grown under high light intensity (PPFD $920{\mu}mol\;m^{-2}s^{-1}$) or low light intensity (PPFD $80{\mu}mol\;m^{-2}s^{-1}$). Plants grown under the low light intensity tended to have reduced leaf area, chlorophyll content per unit leaf area, light absorption and respiration in the shade intolerant tree species of P. euramericana. However, the shade tolerant species K. pictus and Q. serrata showed increased leaf area, chlorophyll content and net apparent quantum yield. Therefore, light absorption and light utilization efficiency were improved under the low light intensity. Also shade tolerant species maintained activities of photosystems and $CO_2$ fixation systems with little changes in low light intensity condition.