111 research outputs found
Blind Demixing for Low-Latency Communication
In the next generation wireless networks, lowlatency communication is
critical to support emerging diversified applications, e.g., Tactile Internet
and Virtual Reality. In this paper, a novel blind demixing approach is
developed to reduce the channel signaling overhead, thereby supporting
low-latency communication. Specifically, we develop a low-rank approach to
recover the original information only based on a single observed vector without
any channel estimation. Unfortunately, this problem turns out to be a highly
intractable non-convex optimization problem due to the multiple non-convex
rankone constraints. To address the unique challenges, the quotient manifold
geometry of product of complex asymmetric rankone matrices is exploited by
equivalently reformulating original complex asymmetric matrices to the
Hermitian positive semidefinite matrices. We further generalize the geometric
concepts of the complex product manifolds via element-wise extension of the
geometric concepts of the individual manifolds. A scalable Riemannian
trust-region algorithm is then developed to solve the blind demixing problem
efficiently with fast convergence rates and low iteration cost. Numerical
results will demonstrate the algorithmic advantages and admirable performance
of the proposed algorithm compared with the state-of-art methods.Comment: 14 pages, accepted by IEEE Transaction on Wireless Communicatio
Sparse Signal Processing Concepts for Efficient 5G System Design
As it becomes increasingly apparent that 4G will not be able to meet the
emerging demands of future mobile communication systems, the question what
could make up a 5G system, what are the crucial challenges and what are the key
drivers is part of intensive, ongoing discussions. Partly due to the advent of
compressive sensing, methods that can optimally exploit sparsity in signals
have received tremendous attention in recent years. In this paper we will
describe a variety of scenarios in which signal sparsity arises naturally in 5G
wireless systems. Signal sparsity and the associated rich collection of tools
and algorithms will thus be a viable source for innovation in 5G wireless
system design. We will discribe applications of this sparse signal processing
paradigm in MIMO random access, cloud radio access networks, compressive
channel-source network coding, and embedded security. We will also emphasize
important open problem that may arise in 5G system design, for which sparsity
will potentially play a key role in their solution.Comment: 18 pages, 5 figures, accepted for publication in IEEE Acces
A system-on-chip microwave photonic processor solves dynamic RF interference in real time with picosecond latency
Radio-frequency interference is a growing concern as wireless technology
advances, with potentially life-threatening consequences like interference
between radar altimeters and 5G cellular networks. Mobile transceivers mix
signals with varying ratios over time, posing challenges for conventional
digital signal processing (DSP) due to its high latency. These challenges will
worsen as future wireless technologies adopt higher carrier frequencies and
data rates. However, conventional DSPs, already on the brink of their clock
frequency limit, are expected to offer only marginal speed advancements. This
paper introduces a photonic processor to address dynamic interference through
blind source separation (BSS). Our system-on-chip processor employs a fully
integrated photonic signal pathway in the analogue domain, enabling rapid
demixing of received mixtures and recovering the signal-of-interest in under 15
picoseconds. This reduction in latency surpasses electronic counterparts by
more than three orders of magnitude. To complement the photonic processor,
electronic peripherals based on field-programmable gate array (FPGA) assess the
effectiveness of demixing and continuously update demixing weights at a rate of
up to 305 Hz. This compact setup features precise dithering weight control,
impedance-controlled circuit board and optical fibre packaging, suitable for
handheld and mobile scenarios. We experimentally demonstrate the processor's
ability to suppress transmission errors and maintain signal-to-noise ratios in
two scenarios, radar altimeters and mobile communications. This work pioneers
the real-time adaptability of integrated silicon photonics, enabling online
learning and weight adjustments, and showcasing practical operational
applications for photonic processing
Temporal and Spatial Features of Single-Trial EEG for Brain-Computer Interface
Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain
rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very useful to produce
subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of
feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data
indicate that independent residual analysis (IRA) method can provide efficient temporal features. Combining IRA
features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best
classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based
brain-computer interface
Detection of movement related cortical potentials from EEG using constrained ICA for brain-computer interface applications
The movement related cortical potential (MRCP), a slow cortical potential from the scalp electroencephalogram (EEG), has been used in real-time brain-computer-interface (BCI) systems designed for neurorehabilitation. Detecting MPCPs in real time with high accuracy and low latency is essential in these applications. In this study, we propose a new MRCP detection method based on constrained independent component analysis (cICA). The method was tested for MRCP detection during executed and imagined ankle dorsiflexion of 24 healthy participants, and compared with four commonly used spatial filters for MRCP detection in an offline experiment. The effect of cICA and the compared spatial filters on the morphology of the extracted MRCP was evaluated by two indices quantifying the signal-to-noise ratio and variability of the extracted MRCP. The performance of the filters for detection was then directly compared for accuracy and latency. The latency obtained with cICA (-34 ± 29 ms motor execution (ME) and 28 ± 16 ms for motor imagery (MI) dataset) was significantly smaller than with all other spatial filters. Moreover, cICA resulted in greater true positive rates (87.11 ± 11.73 for ME and 86.66 ± 6.96 for MI dataset) and lower false positive rates (20.69 ± 13.68 for ME and 19.31 ± 12.60 for MI dataset) compared to the other methods. These results confirm the superiority of cICA in MRCP detection with respect to previously proposed EEG filtering approaches
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