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Deep Reinforcement Learning based MCS Decision Model
A-Hyun Lee, Hyeongho Bae, Young-Ky Kim, Chong-kwon Kim
http://doi.org/10.5626/JOK.2022.49.8.663
In wireless mobile communication systems, link adaptation techniques are used to increase channel throughput and frequency efficiency to adaptively adjust transmission parameters according to the changes in the channel state. Adaptive modulation and coding is a link adaptation technique that determines predefined modulation and coding scheme depending on the channel condition and performed based on the reported CQI from UE and HARQ feedback on packet transmission. In this paper, we propose an MCS decision model that applies deep reinforcement learning to adaptive modulation and coding. The proposed model adaptively determines the MCS level in a dynamically changing network, thereby increasing the transmission efficiency of UEs. We evaluated our proposed model through UE log-based simulations and demonstrated that our model performs much better than the existing outer loop rate control method.
Study and Application of RSSI-based Wi-Fi Channel Detection Using CNN and Frequency Band Characteristics
Junhyun Park, Hyungho Byun, Chong-Kwon Kim
http://doi.org/10.5626/JOK.2020.47.3.335
For mobile devices, Wi-Fi channel scanning is essential to initiating an internet connection, which enables access to a variety of services, and maintaining a stable link quality by periodic monitoring. However, inefficient Wi-Fi operation, where all channels are scanned regardless of whether or not an access point (AP) exists, wastes resources and leads to performance degradation. In this paper, we present a fast and accurate Wi-Fi channel detection method that learns the dynamic frequency band characteristics of signal strengths collected via a low power antenna using a convolution neural network (CNN). Experiments were conducted to demonstrate the channel detection accuracy for different AP combination scenarios. Furthermore, we analyzed the expected performance gain if the suggested method were to assist the scanning operation of the legacy Wi-Fi.
Opinion Classification in Professional Sports Fan Sites using Topic Keyword-Based Sentiment Analysis
Hyungho Byun, Sihyun Jeong, Chong-kwon Kim
http://doi.org/10.5626/JOK.2018.45.4.390
In this study, we propose the classification method using topic keyword-based sentiment analysis through the posts of professional sports fan sites in Korea. We studied ways to take into account the use of special communication methods or vocabulary in the community and defined keywords based on the characteristics of the topic or frequency of the community"s words. In addition, we presented a new sentiment analysis approach that utilizes the use of keyword pools and the proximity relation to keywords. Through three years of actual community dataset, sentiment analysis based on the topic keyword is more effective than the existing method and reflects the community environment.
Rank Correlation Coefficient of Energy Data for Identification of Abnormal Sensors in Buildings
Naeon Kim, Sihyun Jeong, Boyeon Jang, Chong-Kwon Kim
Anomaly detection is the identification of data that do not conform to a normal pattern or behavior model in a dataset. It can be utilized for detecting errors among data generated by devices or user behavior change in a social network data set. In this study, we proposed a new approach using rank correlation coefficient to efficiently detect abnormal data in devices of a building. With the increased push for energy conservation, many energy efficiency solutions have been proposed over the years. HVAC (Heating, Ventilating and Air Conditioning) system monitors and manages thousands of sensors such as thermostats, air conditioners, and lighting in large buildings. Currently, operators use the building’s HVAC system for controlling efficient energy consumption. By using the proposed approach, it is possible to observe changes of ranking relationship between the devices in HVAC system and identify abnormal behavior in social network.
Performance Evaluation of Review Spam Detection for a Domestic Shopping Site Application
As the number of customers who write fake reviews is increasing, online shopping sites have difficulty in providing reliable reviews. Fake reviews are called review spam, and they are written to promote or defame the product. They directly affect sales volume of the product; therefore, it is important to detect review spam. Review spam detection methods suggested in prior researches were only based on an international site even though review spam is a widespread problem in domestic shopping sites. In this paper, we have presented new review features of the domestic shopping site NAVER, and we have applied the formerly introduced method to this site for performing an evaluation.
A Re-configuration Scheme for Social Network Based Large-scale SMS Spam
Sihyun Jeong, Giseop Noh, Hayoung Oh, Chong-Kwon Kim
The Short Message Service (SMS) is one of the most popular communication tools in the world. As the cost of SMS decreases, SMS spam has been growing largely. Even though there are many existing studies on SMS spam detection, researchers commonly have limitation collecting users" private SMS contents. They need to gather the information related to social network as well as personal SMS due to the intelligent spammers being aware of the social networks. Therefore, this paper proposes the Social network Building Scheme for SMS spam detection (SBSS) algorithm that builds synthetic social network dataset realistically, without the collection of private information. Also, we analyze and categorize the attack types of SMS spam to build more complete and realistic social network dataset including SMS spam.
Designing an Algorithm for the Priority Deciding and Recommending of the Logistic Support with Stationary Distribution
Giseop Noh, Sihyun Jeong, Chong-Kwon Kim, Hayoung Oh
One of the important roles used to ensure victory in a war is to maximize the overall military forces and to make sure that the capability of the military forces can be sustained as much as possible. Although several researchers have proposed various possible methodologies for logistics support, no research trials have been undertaken to investigate logistics support that considers all relevant elements of such. Unlike previous in trials that consider and analyze the system fault ratio as the main methodology, we propose an approach that simultaneously decides and recommends logistic priority by reflecting and combining item costs, transportation, fault-ratio, and system complexity. Also, we designed an algorithm that can recommend optimized logistics support priority using stationary distribution.
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