1,665 research outputs found
Jointly Sparse Support Recovery via Deep Auto-encoder with Applications in MIMO-based Grant-Free Random Access for mMTC
In this paper, a data-driven approach is proposed to jointly design the
common sensing (measurement) matrix and jointly support recovery method for
complex signals, using a standard deep auto-encoder for real numbers. The
auto-encoder in the proposed approach includes an encoder that mimics the noisy
linear measurement process for jointly sparse signals with a common sensing
matrix, and a decoder that approximately performs jointly sparse support
recovery based on the empirical covariance matrix of noisy linear measurements.
The proposed approach can effectively utilize the feature of common support and
properties of sparsity patterns to achieve high recovery accuracy, and has
significantly shorter computation time than existing methods. We also study an
application example, i.e., device activity detection in Multiple-Input
Multiple-Output (MIMO)-based grant-free random access for massive machine type
communications (mMTC). The numerical results show that the proposed approach
can provide pilot sequences and device activity detection with better detection
accuracy and substantially shorter computation time than well-known recovery
methods.Comment: 5 pages, 8 figures, to be publised in IEEE SPAWC 2020. arXiv admin
note: text overlap with arXiv:2002.0262
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Reconstructing Human Pose from Inertial Measurements: A Generative Model-based Compressive Sensing Approach
The ability to sense, localize, and estimate the 3D position and orientation
of the human body is critical in virtual reality (VR) and extended reality (XR)
applications. This becomes more important and challenging with the deployment
of VR/XR applications over the next generation of wireless systems such as 5G
and beyond. In this paper, we propose a novel framework that can reconstruct
the 3D human body pose of the user given sparse measurements from Inertial
Measurement Unit (IMU) sensors over a noisy wireless environment. Specifically,
our framework enables reliable transmission of compressed IMU signals through
noisy wireless channels and effective recovery of such signals at the receiver,
e.g., an edge server. This task is very challenging due to the constraints of
transmit power, recovery accuracy, and recovery latency. To address these
challenges, we first develop a deep generative model at the receiver to recover
the data from linear measurements of IMU signals. The linear measurements of
the IMU signals are obtained by a linear projection with a measurement matrix
based on the compressive sensing theory. The key to the success of our
framework lies in the novel design of the measurement matrix at the
transmitter, which can not only satisfy power constraints for the IMU devices
but also obtain a highly accurate recovery for the IMU signals at the receiver.
This can be achieved by extending the set-restricted eigenvalue condition of
the measurement matrix and combining it with an upper bound for the power
transmission constraint. Our framework can achieve robust performance for
recovering 3D human poses from noisy compressed IMU signals. Additionally, our
pre-trained deep generative model achieves signal reconstruction accuracy
comparable to an optimization-based approach, i.e., Lasso, but is an order of
magnitude faster
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