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A novel root-index based prioritized random access scheme for 5G cellular networks
김태훈,장한승,성단근 한국통신학회 2015 ICT Express Vol.1 No.3
Cellular networks will play an important role in realizing the newly emerging Internet-of-Everything (IoE). One of the challenging issues is to support the quality of service (QoS) during the access phase, while accommodating a massive number of machine nodes. In this paper, we show a new paradigm of multiple access priorities in random access (RA) procedure and propose a novel root-index based prioritized random access (RIPRA) scheme that implicitly embeds the access priority in the root index of the RA preambles. The performance evaluation shows that the proposed RIPRA scheme can successfully support differentiated performance for different access priority levels, even though there exist a massive number of machine nodes. Keywords: Internet-of-Everything; M2M; Random access; Zadoff–Chu sequence; Access priority
Asynchronous Advantage Actor Critic 기반 그룹 임의접속 제어기술
김수,장한승 한국통신학회 2023 韓國通信學會論文誌 Vol.48 No.2
본 논문은 대규모 그룹 IoT 기기들이 임의접속을 동시에 시도했을 때 발생하는 접속 과부하 문제를 해결하기위해 Asynchronous Advantage Actor Critic (A3C) 기반의 임의접속 제어 기법을 제안한다. 기존 연구에서는 강화학습 기법인 DQN을 이용하여 접속 제어기술을 구현하던 것과는 달리, 본 연구에서는 강화학습 기법 중 A3C를사용하는 접속제어 기술을 제안한다. 제안하는 A3C 기반의 그룹 임의접속 제어기술은 그룹의 모든 단말이 임의접속에 성공하는데 걸리는 시간 측면에서 최적 학습을 통해 일반적인 프리앰블 충돌 검출 방식과 빠른 프리앰블 충돌 검출 방식에 따른 이론적인 제어 성능에 도달함을 보여준다.
윤문섭,임세령,장한승 한국전자통신학회 2023 한국전자통신학회 논문지 Vol.18 No.6
The global climate crisis and the implementation of low-carbon policies have led to a growing interest in renewable energy and a growing number of related industries. Among them, solar power is attracting attention as a representative eco-friendly energy that does not deplete and does not emit pollutants or greenhouse gases. As a result, the supplement of solar power facility is increasing all over the world. However, solar power is easily affected by the environment such as geography and weather, so accurate solar power forecast is important for stable operation and efficient management. However, it is very hard to predict the exact amount of solar power using statistical methods. In addition, the conventional prediction methods have focused on only short- or long-term prediction, which causes it to take a long time to obtain various prediction models with different prediction horizons. Therefore, this study utilizes a many-to-many structure of a recurrent neural network (RNN) to integrate short-term and long-term predictions of solar power generation. We compare various RNN-based very short- and long-term prediction methods for solar power in terms of MSE and R2 values.