OFDM
Relay
Cognitive Radio
Multiple Antennas
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Full Duplex
Spectrum Sensing
Synchronization
Spectrum Sharing
Interference Cancellation
Channel Estimation
Feedback
Stochastic Geometry
Bi-directional
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Heterogeneous Networks
FBMC
HetNet
Equalization
relay networks
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TVWS
MIMO
interference
SC-FDMA
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channel capacity
interference suppression
5G
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OCBT
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body area networks
5G mobile communication
antenna arrays
NR positioning
health care
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OTDOA
hybrid
control overhead
estimated position overlapping
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amplify and forward communication
power uncertainty
CoMP
quality of service
cellular radio
Rat-dependent positioning
telecommunication traffic
Handoff
Femtocell
inter user interference
UFMC
mode selection
GFDM
QAM
Zigbee
frame structure
Vehicular communication
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non-orthogonal multiple access
QR Factorization
Spatial capacity
LTE-based V2V
Number of training blocks
Communication range
user fairness
Mode 3
resource selection
distributed mode
maximum likelihood method
Metaheuristics
cross-link interference
Dynamic TDD
Uplink SCMA system
V2X
DQN
estimated position updating
—Device-to-device (D2D)
D-TDD
dynamic HetNet
indoor positioning
spectrum partitioning
- Computation offloading
multi-access edge computing
P-NOMA
partial overlap
Subband filtering
Multi-user Receiver
reinforcement learning
RSRP weighting
smart factory
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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 57756
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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