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
Daesik Hong and O. K. Ersoy, "Neural Networks Learning Paradigms Involving Nonlinear Spectral Processing", IEEE ICASSP, May 1989
[IEEE ICASSP]
조회 64189
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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