• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.3542-3566
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• 2018

In this paper, we investigate a buffer-aided wireless powered cooperative communication network (WPCCN), in which the source and relay harvest the energy from a dedicated power beacon via wireless energy transfer, then the source transmits the data to the destination through the relay. Both the source and relay are equipped with an energy buffer to store the harvested energy in the energy transfer stage. In addition, the relay is equipped with a data buffer and can temporarily store the received information. Considering the buffer-aided WPCCN, we propose two buffer-aided relaying protocols, which named as the buffer-aided harvest-then-transmit (HtT) protocol and the buffer-aided joint mode selection and power allocation (JMSPA) protocol, respectively. For the buffer-aided HtT protocol, the time-averaged achievable rate is obtained in closed form. For the buffer-aided JMSPA protocol, the optimal adaptive mode selection scheme and power allocation scheme, which jointly maximize the time-averaged throughput of system, are obtained by employing the Lyapunov optimization theory. Furthermore, we drive the theoretical bounds on the time-averaged achievable rate and time-averaged delay, then present the throughput-delay tradeoff achieved by the joint JMSPA protocol. Simulation results validate the throughput performance gain of the proposed buffer-aided relaying protocols and verify the theoretical analysis.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.167-174
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• 2022

In metaheuristic algorithms such as Genetic Algorithm (GA), initial population has a significant impact as it affects the time such algorithm takes to obtain an optimal solution to the given problem. In addition, it may influence the quality of the solution obtained. In the machine learning field, feature selection is an important process to attaining a good performance model; Genetic algorithm has been utilized for this purpose by scientists. However, the characteristics of Genetic algorithm, namely random initial population generation from a vector of feature elements, may influence solution and execution time. In this paper, the use of a statistical algorithm has been introduced (Chi2) for feature relevant checks where p-values of conditional independence were considered. Features with low p-values were discarded and subject relevant subset of features to Genetic Algorithm. This is to gain a level of certainty of the fitness of features randomly selected. An ensembled-based learning model for Hepatitis has been developed for Hepatitis C stage classification. 1385 samples were used using Egyptian-dataset obtained from UCI repository. The comparative evaluation confirms decreased in execution time and an increase in model performance accuracy from 56% to 63%.

• Journal of Digital Convergence
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• v.17 no.2
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• pp.163-170
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• 2019

Over the past decade, the development of the Web explosively increased the data. Feature selection step is an important step in extracting valuable data from a large amount of data. This study proposes a novel opinion mining model based on combining feature selection (FS) methods with Word embedding to vector (Word2vec) and BOW (Bag-of-words). FS methods adopted for this study are CFS (Correlation based FS) and IG (Information Gain). To select an optimal FS method, a number of classifiers ranging from LR (logistic regression), NN (neural network), NBN (naive Bayesian network) to RF (random forest), RS (random subspace), ST (stacking). Empirical results with electronics and kitchen datasets showed that LR and ST classifiers combined with IG applied to BOW features yield best performance in opinion mining. Results with laptop and restaurant datasets revealed that the RF classifier using IG applied to Word2vec features represents best performance in opinion mining.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.5A
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• pp.511-520
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• 2011

In this paper, we introduce a multiuser (MU) scheduling method for multiuser amplify-and-forward relay systems, which selects both the transmission mode, i.e., either one- or two-hop transmission, and the desired user via two steps. A closed-form expression for the average achievable rate of the proposed scheduling is derived under two transmission modes with MU scheduling, and its asymptotic solution is also analyzed in the limit of large number of mobile stations. Based on the analysis, we perform our two-step scheduling algorithm: the transmission mode selection followed by the user selection that needs partial feedback for instantaneous signal-to-noise ratios (SNRs) to the base station. We also analyze the average SNR condition such that the MU diversity gain is fully exploited. In addition, it is examined how to further reduce a quantity of feedback under certain conditions. The proposed algorithm shows the comparable achievable rate to that of the optimal one using full feedback information, while its required feedback overhead is reduced below half of the optimal one.

• Journal of Power System Engineering
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• v.21 no.3
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• pp.60-65
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• 2017

A GA is one of the best method to find optimal value in searching area. A GA is driven by probabilistic selection that based on the survival of the fittest. So this algorithm need a huge solving time even if it can be used lots of optimizing problem such as structural design, machine learning, system's identification and so on. This GA's characteristic constrain the program to drive offline. Some studies try to use this algorithm on online or reduce the GA's running time with parallel GA or micro GA. Unfortunately these studies still didn't reduce amount of fitness solving. If the chromosome was imported to the system, it affected system's stability. And when the control system uses online GA, it also doesn't have enough learning time. In this study, try to find stability criterion to reduce the chromosome's affection and find the characteristic of the number of population and generation when GA was driven into the wide searching area.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.44 no.2
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• pp.6-14
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• 2007

In this paper, we propose and observe the Adaptive Modulation system with optimal Turbo Coded V-BLAST (Vertical-Bell-lab Layered Space-Time) technique that is applied the extrinsic information from MAP (Maximum A Posteriori) Decoder in decoding Algorithm of V-BLAST: ordering and slicing. The extrinsic information is used by a priori probability and the system decoding process is composed of the Main Iteration and the Sub Iteration. And comparing the proposed system with the Adaptive Modulation system using conventional Turbo Coded V-BLAST technique that is simply combined V-BLAST with Turbo Coding scheme, we observe how much throughput performance has been improved. In addition, we observe the proposed system using STD (Selection Transmit Diversity) scheme. As a result of simulation, Comparing with the conventional Turbo Coded V-BLAST technique with the Adaptive Modulation systems, the optimal Turbo Coded V-BLAST technique with the Adaptive Modulation systems has better throughput gain that is about 350 Kbps in 11 dB SNR range. Especially, comparing with the conventional Turbo Coded V-BLAST technique using 2 transmit and 2 receive antennas, the proposed system with STD (Selection Transmit Diversity) scheme show that the improvement of maximum throughput is about 1.77 Mbps in the same SNR range.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.27-33
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• 2009

Selection of the Lagrangian multiplier is a key factor to determine the performance of Rate-Distortion Optimization (RDO) in video coding. JM, reference S/W of H.264, employs only one RDO model for all macroblock. However, since the characteristics of macroblocks are different, RDO model adaptive to their characteristics can give some performance improvement. In this paper, we propose an RDO algorithm adaptive to characteristics of macroblocks. We empirically obtain the optimal Lagrangian multipliers considering characteristics of macroblocks. For performance evaluation, the proposed method is applied to JM10.2 and, as a result, we have PSNR gain of 0.2dB on average.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.3
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• pp.891-910
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• 2021

Previous QoS-aware service composition methods mainly focus on how to generate composite service with the optimal QoS efficiently for a single request. However, in the real application scenarios, there are multiple service requests and multiple service providers. It is more important to compose services with suboptimal QoS and maintain the load balance between services. To solve this problem, in this paper, we propose a service composition method, named as dynamically change and balancing composition method (DCBC). It assumes that the QoS of service is not static, and the services can adjust the value of QoS to gain more opportunities to be selected for composition. The method mainly includes two steps, which are the preprocessing step and the service selection step. In the preprocessing step, a backward global best QoS calculation is performed which regarding the static and dynamic QoS respectively; then guided by the global QoS, the feasible services can be selected efficiently in the service selection step. The experiments show that the DCBC method can not only improve the overall quality of composite services but also guarantee the fulfill ratio of requests and the load balance of services.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1559-1565
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• 2021

In this paper, an antenna selection scheme is proposed in massive multi-user multiple input multiple output (MU-MIMO) systems. The proposed antenna selection scheme can achieve almost the same performance as a conventional scheme while significantly reducing the overhead of feedback by using deep reinforcement learning (DRL). Each user compares the channel gains of massive antennas in base station (BS) to the L-largest channel gain, converts them to one-bit binary numbers, and feed them back to BS. Thus, the feedback overhead can be significantly reduced. In the proposed scheme, DRL is adopted to prevent the performance loss that might be caused by the reduced feedback information. We carried out extensive Monte-Carlo simulations to analyze the performance of the proposed scheme and it was shown that the proposed scheme can achieve almost the same average sum-rates as a conventional scheme that is almost optimal.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.9A
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• pp.758-766
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• 2011

Cooperative communications are adopted as a promising solution to achieve high data rate over large areas in the future 40 wireless system and the relay station (RS) is the key concept in cooperative communications. However, most existing works in this area focus only on optimal RS selections. In addition, there are only few works consider another crucial issue: how many relay stations we need to place. Only when the number of relay stations is defined, the relay station selection can be performed well. In this paper we derive a formula which describes the impact of varying number of RS on end-to-end link throughput assuming a clustering scheme which is based on Voronoi tessellation. In addition to mathematical analysis on the feasibility of the formula, we also examine its performance through a set of simulations under the Erceg path loss model. Simulation results verify that the link throughput gain of our proposed scheme is promising.