OFDM Relay Cognitive Radio Multiple Antennas Resource Allocation Full Duplex Spectrum Sensing Synchronization Spectrum Sharing Channel Estimation Interference Cancellation Stochastic Geometry Energy Harvesting Feedback Bi-directional Heterogeneous Networks Equalization HetNet relay networks FBMC Ultra Low Power SC-FDMA TVWS Duplex Reliability CDMA MIMO interference channel capacity in-band full-duplex system interference suppression 5G C-V2V reinforcement learning RSRP weighting and 5G networks. health care 5G mobile communication indoor positioning Vehicle-to-vehicle communication estimated position overlapping Resource sharing Power allocation multi-access edge computing control overhead hybrid Rat-dependent positioning NR positioning smart factory UFMC Handoff Femtocell QAM CoMP power uncertainty - Computation offloading amplify and forward communication Zigbee body area networks resource block management frame structure WVAN inter user interference GFDM mode selection antenna arrays partial overlap LTE-based V2V resource selection maximum likelihood method Communication range Number of training blocks Vehicular communication Uplink SCMA system Dynamic TDD QR Factorization Metaheuristics FS-NOMA cross-link interference user fairness Multi-user Receiver Mode 3 V2X P-NOMA dynamic HetNet spectrum partitioning DQN D-TDD CLI overloading non-orthogonal multiple access (NOMA) OTDOA estimated position updating distributed mode non-orthogonal multiple access Spatial capacity LTE-TDD —Device-to-device (D2D) Location-based massive connectivity
Status : Presented 
Date : 2020-12 
Title : Deep Reinforcement Learning-based Task Offloading Decision in the Time Varying Channel 
Authors : Jinkyo Jeong, Ilmin Kim, and Daesik Hong 
Conference : ICEIC 
Abstract : This paper proposes a dynamic task offloading decision control scheme to minimize the total delay to execute computation task taking into account the time-varying channel. Specifically, we consider the practical task offloading process, where executing computation task is carried out over multiple channel coherence times. In order to make an accurate decision on the task offloading process performed over multiple channel coherence times, we utilize the model-free reinforcement learning, since environment dynamics of the system, channel transition probabilities, is challenging to estimate. We formulate a problem of minimizing the total delay of executing computation task based on a Markov decision process (MDP). In order to solve the MDP problem, we develop a model-free reinforcement learning algorithm. Simulation results show that our proposed scheme outperforms the conventional scheme. 

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» [ICEIC] Jinkyo Jeong, Ilmin Kim, and Daesik Hong, "Deep Reinforcement Learning-based Task Offloading Decision in the Time Varying Channel" ICEIC 2021 Presented  2020-12