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
non-orthogonal multiple access (NOMA)
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
massive connectivity
and 5G networks.
OTDOA
estimated position updating
distributed mode
non-orthogonal multiple access
Spatial capacity
LTE-TDD
—Device-to-device (D2D)
Location-based
overloading
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Jinkyo Jeong, Ilmin Kim, and Daesik Hong, "Deep Reinforcement Learning-based Task Offloading Decision in the Time Varying Channel" ICEIC 2021
[ICEIC]
조회 18403
Status : | Presented |
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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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