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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.
Partially Collective Spatial Keyword Query Processing Based on Spatial Keyword Similarity
Ah Hyun Lee, Sehwa Park, Seog Park
http://doi.org/10.5626/JOK.2021.48.10.1142
Collective spatial keyword queries return Points of Interest (POI), which are close to the query location and contain all the presented set of keywords. However, existing studies only consider a fixed number of query keywords, which is not adequate to satisfy the user. They do not care about the preference of a partial keyword set, and a flexible keyword set needs to be selected for the preference of each POI. We thus propose a new query, called Partially Collective Spatial Keyword Query, which flexibly considers keywords that fit the preference for each POI. Since this query is a combinatorial optimization problem, the query processing time increases rapidly as the number of POIs increases. Therefore, to address these problems, we propose a keyword-based search technique that reduces the overall search space. Furthermore, we propose heuristic techniques, which include the linear search-based terminal node pruning technique, approximation algorithm, and threshold-based pruning technique.
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