OFDM
Relay
Cognitive Radio
Multiple Antennas
Resource Allocation
Full Duplex
Spectrum Sensing
Synchronization
Spectrum Sharing
Interference Cancellation
Channel Estimation
Feedback
Heterogeneous Networks
Bi-directional
Energy Harvesting
Stochastic Geometry
HetNet
relay networks
FBMC
Equalization
channel capacity
TVWS
CDMA
interference
in-band full-duplex system
Duplex
MIMO
Ultra Low Power
C-V2V
5G
Reliability
SC-FDMA
interference suppression
D-TDD
CLI
indoor positioning
reinforcement learning
RSRP weighting
- Computation offloading
smart factory
Cell-free
multi-access edge computing
estimated position overlapping
—Device-to-device (D2D)
estimated position updating
mMIMO
control overhead
hybrid
NR positioning
Femtocell
Rat-dependent positioning
frame structure
Zigbee
body area networks
channel estimation error
Handoff
CoMP
User grouping
power uncertainty
ultra-dense small cell network
mode selection
antenna arrays
5G mobile communication
UFMC
resource block management
inter user interference
WVAN
health care
partial overlap
GFDM
Dynamic TDD
Multi-user Receiver
Number of training blocks
Uplink SCMA system
V2X
Vehicular communication
cross-link interference
LTE-TDD
FS-NOMA
Location-based
user fairness
Mode 3
QR Factorization
Metaheuristics
P-NOMA
non-orthogonal multiple access
dynamic HetNet
spectrum partitioning
and 5G networks.
massive connectivity
non-orthogonal multiple access (NOMA)
overloading
DQN
OTDOA
distributed mode
Communication range
resource selection
maximum likelihood method
Resource sharing
Power allocation
packet delay
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Sooyong Choi and Daesik Hong, "A Hybrid Structured Neural Network Receiver in Digital Communication Systems", IEEE IJCNN, July 2000
[IEEE IJCNN]
조회 64623
Status : | Presented |
---|---|
Date : | 2000-07 |
Title : | A Hybrid Structured Neural Network Receiver in Digital Communication Systems |
Authors : | Sooyong Choi and Daesik Hong |
Conference : | IEEE International Joint Conference on Neural Networks |
Abstract : | In order to reduce the complexity of a radial basis function (RBF) network as a multiuser demodulator and an equalizer, we propose a simplified hybrid neural network architecture. The proposed neural network, which is called RN, has the structure of combining a radial basis function network with multilayer perceptrons (MLPs). The RBF network yields the linear combining output of the hidden layer while the proposed hybrid neural network produces the output using nonlinear combining techniques. From computer simulation results, the RN with the reduced structure from about 50% to about 70% over the RBF network shows better than or almost equal performance to the RBF network as a multiuser demodulator and an equalizer |
URL : | http://ieeexplore.ieee.org/xpl/articleDe...ber=861499 |
Sooyong Choi; Daesik Hong; , "A hybrid structured neural network receiver in digital communication systems," Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on , vol.5, no., pp.378-383 vol.5, 2000
doi: 10.1109/IJCNN.2000.861499
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=861499&isnumber=18674
doi: 10.1109/IJCNN.2000.861499
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=861499&isnumber=18674
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