OFDM
Relay
Cognitive Radio
Multiple Antennas
Resource Allocation
Full Duplex
Spectrum Sensing
Synchronization
Spectrum Sharing
Interference Cancellation
Channel Estimation
Feedback
Heterogeneous Networks
Energy Harvesting
Stochastic Geometry
Bi-directional
Equalization
relay networks
FBMC
HetNet
CDMA
channel capacity
SC-FDMA
interference
MIMO
Ultra Low Power
Duplex
TVWS
CLI
in-band full-duplex system
D-TDD
interference suppression
Reliability
5G
C-V2V
indoor positioning
reinforcement learning
RSRP weighting
User grouping
Rat-dependent positioning
Communication range
multi-access edge computing
hybrid
- Computation offloading
—Device-to-device (D2D)
estimated position updating
control overhead
resource block management
estimated position overlapping
Femtocell
NR positioning
frame structure
Zigbee
body area networks
channel estimation error
Handoff
CoMP
packet delay
power uncertainty
ultra-dense small cell network
GFDM
antenna arrays
5G mobile communication
UFMC
WVAN
mode selection
inter user interference
health care
Vehicular communication
cross-link interference
resource selection
FS-NOMA
Dynamic TDD
Number of training blocks
Mode 3
Uplink SCMA system
Multi-user Receiver
LTE-TDD
user fairness
smart factory
V2X
Metaheuristics
Location-based
QR Factorization
partial overlap
P-NOMA
dynamic HetNet
spectrum partitioning
Cell-free
and 5G networks.
overloading
massive connectivity
DQN
mMIMO
distributed mode
non-orthogonal multiple access
OTDOA
maximum likelihood method
Resource sharing
Power allocation
non-orthogonal multiple access (NOMA)
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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 66397
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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