17,607 research outputs found
Detection for 5G-NOMA: An Online Adaptive Machine Learning Approach
Non-orthogonal multiple access (NOMA) has emerged as a promising radio access
technique for enabling the performance enhancements promised by the
fifth-generation (5G) networks in terms of connectivity, low latency, and high
spectrum efficiency. In the NOMA uplink, successive interference cancellation
(SIC) based detection with device clustering has been suggested. In the case of
multiple receive antennas, SIC can be combined with the minimum mean-squared
error (MMSE) beamforming. However, there exists a tradeoff between the NOMA
cluster size and the incurred SIC error. Larger clusters lead to larger errors
but they are desirable from the spectrum efficiency and connectivity point of
view. We propose a novel online learning based detection for the NOMA uplink.
In particular, we design an online adaptive filter in the sum space of linear
and Gaussian reproducing kernel Hilbert spaces (RKHSs). Such a sum space design
is robust against variations of a dynamic wireless network that can deteriorate
the performance of a purely nonlinear adaptive filter. We demonstrate by
simulations that the proposed method outperforms the MMSE-SIC based detection
for large cluster sizes.Comment: Accepted at ICC 201
Generalized Kernel-based Visual Tracking
In this work we generalize the plain MS trackers and attempt to overcome
standard mean shift trackers' two limitations.
It is well known that modeling and maintaining a representation of a target
object is an important component of a successful visual tracker.
However, little work has been done on building a robust template model for
kernel-based MS tracking. In contrast to building a template from a single
frame, we train a robust object representation model from a large amount of
data. Tracking is viewed as a binary classification problem, and a
discriminative classification rule is learned to distinguish between the object
and background. We adopt a support vector machine (SVM) for training. The
tracker is then implemented by maximizing the classification score. An
iterative optimization scheme very similar to MS is derived for this purpose.Comment: 12 page
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