Full Duplex
HetNet
FBMC
relay networks
Channel Estimation
Interference Cancellation
interference
channel capacity
MIMO
CLI
synchronization
interference suppression
in-band full-duplex system
D-TDD
5G
Multiple Antennas
inter user interference
OFDM
mode selection
WVAN
channel estimation error
UFMC
5G mobile communication
timing misalignment
ultra-dense small cell network
GFDM
reinforcement learning
multi-access edge computing
health care
RSRP weighting
- Computation offloading
—Device-to-device (D2D)
antenna arrays
power uncertainty
resource block management
frame structure
QAM
Poisson arrival
Short burst transmission
mMTC
User association
Traffic
Capacity
OCBT
Waveforms
Time-division duplex
self interference cancellation
body area networks
cellular radio
quality of service
amplify and forward communication
Cognitive radio
telecommunication traffic
intercarrier interference
Zigbee
indoor positioning
intersymbol interference
spectrum sharing
Cell-free
Uplink SCMA system
Number of training blocks
Reliability
Communication range
Mode 3
resource allocation
P-NOMA
partial overlap
Vehicular communication
resource selection
maximum likelihood method
V2X
user fairness
cross-link interference
Dynamic TDD
LTE-TDD
Metaheuristics
QR Factorization
Multi-user Receiver
FS-NOMA
non-orthogonal multiple access
C-V2V
OTDOA
mMIMO
User grouping
packet delay
estimated position updating
Resource sharing
Rat-dependent positioning
NR positioning
estimated position overlapping
non-orthogonal multiple access (NOMA)
overloading
Power allocation
Location-based
distributed mode
DQN
spectrum partitioning
massive connectivity
and 5G networks.
dynamic HetNet
smart factory
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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 37793
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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