49,573 research outputs found
Deep Learning Framework for Wireless Systems: Applications to Optical Wireless Communications
Optical wireless communication (OWC) is a promising technology for future
wireless communications owing to its potentials for cost-effective network
deployment and high data rate. There are several implementation issues in the
OWC which have not been encountered in radio frequency wireless communications.
First, practical OWC transmitters need an illumination control on color,
intensity, and luminance, etc., which poses complicated modulation design
challenges. Furthermore, signal-dependent properties of optical channels raise
non-trivial challenges both in modulation and demodulation of the optical
signals. To tackle such difficulties, deep learning (DL) technologies can be
applied for optical wireless transceiver design. This article addresses recent
efforts on DL-based OWC system designs. A DL framework for emerging image
sensor communication is proposed and its feasibility is verified by simulation.
Finally, technical challenges and implementation issues for the DL-based
optical wireless technology are discussed.Comment: To appear in IEEE Communications Magazine, Special Issue on
Applications of Artificial Intelligence in Wireless Communication
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
Signal-Aligned Network Coding in K-User MIMO Interference Channels with Limited Receiver Cooperation
In this paper, we propose a signal-aligned network coding (SNC) scheme for
K-user time-varying multiple-input multiple-output (MIMO) interference channels
with limited receiver cooperation. We assume that the receivers are connected
to a central processor via wired cooperation links with individual limited
capacities. Our SNC scheme determines the precoding matrices of the
transmitters so that the transmitted signals are aligned at each receiver. The
aligned signals are then decoded into noiseless integer combinations of
messages, also known as network-coded messages, by physical-layer network
coding. The key idea of our scheme is to ensure that independent integer
combinations of messages can be decoded at the receivers. Hence the central
processor can recover the original messages of the transmitters by solving the
linearly independent equations. We prove that our SNC scheme achieves full
degrees of freedom (DoF) by utilizing signal alignment and physical-layer
network coding. Simulation results show that our SNC scheme outperforms the
compute-and-forward scheme in the finite SNR regime of the two-user and the
three-user cases. The performance improvement of our SNC scheme mainly comes
from efficient utilization of the signal subspaces for conveying independent
linear equations of messages to the central processor.Comment: 12 pages, 4 figures, submitted to the IEEE Transactions on Vehicular
Technolog
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