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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Daesik Hong and O. K. Ersoy, "Neural Networks Learning Paradigms Involving Nonlinear Spectral Processing", IEEE ICASSP, May 1989
[IEEE ICASSP]
조회 64208
Status : | Presented |
---|---|
Date : | 1989-05 |
Title : | Neural Networks Learning Paradigms Involving Nonlinear Spectral Processing |
Authors : | Daesik Hong and O. K. Ersoy |
Conference : | IEEE International Conference on Acoustics, Speech, and Signal Processing |
Abstract : | Two neural network architectures involving nonlinear spectral transformations are described. The first architecture involves generalization of nonlinear matched-filtering techniques, yielding a network that is very fast in learning and recall as well as highly accurate in classification. The second architecture is hierarchical with a number of stages; after each stage, error detection is carried out, followed by nonlinear spectral transformations when the error measure is above threshold |
URL : | http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=266794 |
Ersoy, O.K.; Hong, D.; , "Neural network learning paradigms involving nonlinear spectral processing,"Acoustics, Speech, and Signal Processing, 1989. ICASSP-89., 1989 International Conference on , vol., no., pp.1775-1778 vol.3, 23-26 May 1989
doi: 10.1109/ICASSP.1989.266794
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=266794&isnumber=6677
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