19 research outputs found
Two-timescale adaptive live video streaming transmission mechanism for vehicular networks
No full text
Weighted Sum-Rate Maximization for Movable Antenna-Enhanced Wireless Networks
Full text linkThis letter investigates the weighted sum rate maximization problem in
movable antenna (MA)-enhanced systems. To reduce the computational complexity,
we transform it into a more tractable weighted minimum mean square error
(WMMSE) problem well-suited for MA. We then adopt the WMMSE algorithm and
majorization-minimization algorithm to optimize the beamforming and antenna
positions, respectively. Moreover, we propose a planar movement mode, which
constrains each MA to a specified area, we obtain a low-complexity closed-form
solution. Numerical results demonstrate that the MA-enhanced system outperforms
the conventional system. Besides, the computation time for the planar movement
mode is reduced by approximately 30\% at a little performance expense.Comment: Accepted by IEEE Wireless Communications Letter
R-PMAC: A Robust Preamble Based MAC Mechanism Applied in Industrial Internet of Things
Full text linkThis paper proposes a novel media access control (MAC) mechanism, called the
robust preamble-based MAC mechanism (R-PMAC), which can be applied to power
line communication (PLC) networks in the context of the Industrial Internet of
Things (IIoT). Compared with other MAC mechanisms such as P-MAC and the MAC
layer of IEEE1901.1, R-PMAC has higher networking speed. Besides, it supports
whitelist authentication and functions properly in the presence of data frame
loss. Firstly, we outline three basic mechanisms of R-PMAC, containing precise
time difference calculation, preambles generation and short ID allocation.
Secondly, we elaborate its networking process of single layer and multiple
layers. Thirdly, we illustrate its robust mechanisms, including collision
handling and data retransmission. Moreover, a low-cost hardware platform is
established to measure the time of connecting hundreds of PLC nodes for the
R-PMAC, P-MAC, and IEEE1901.1 mechanisms in a real power line environment. The
experiment results show that R-PMAC outperforms the other mechanisms by
achieving a 50% reduction in networking time. These findings indicate that the
R-PMAC mechanism holds great potential for quickly and effectively building a
PLC network in actual industrial scenarios.Comment: This paper has been accepted by IEEE Internet of Things Journa
Linear MIMO Precoders Design for Finite Alphabet Inputs via Model-Free Training
Full text linkThis paper investigates a novel method for designing linear precoders with
finite alphabet inputs based on autoencoders (AE) without the knowledge of the
channel model. By model-free training of the autoencoder in a multiple-input
multiple-output (MIMO) system, the proposed method can effectively solve the
optimization problem to design the precoders that maximize the mutual
information between the channel inputs and outputs, when only the input-output
information of the channel can be observed. Specifically, the proposed method
regards the receiver and the precoder as two independent parameterized
functions in the AE and alternately trains them using the exact and
approximated gradient, respectively. Compared with previous precoders design
methods, it alleviates the limitation of requiring the explicit channel model
to be known. Simulation results show that the proposed method works as well as
those methods under known channel models in terms of maximizing the mutual
information and reducing the bit error rate.Comment: Accepted by GLOBECOM 202
A Super‐Hydrophobic and Super‐Oleophilic Coating Mesh Film for the Separation of Oil and Water
No full textSCSC: a novel standards-compatible semantic communication framework for image transmission
No full textJoint source-channel coding (JSCC) is a promising paradigm for next-generation communication systems, particularly in challenging transmission environments. In this paper, we propose a novel standard-compatible JSCC framework for the transmission of images over multiple-input multiple-output (MIMO) channels. Different from the existing end-to-end AI-based DeepJSCC schemes, our framework consists of learnable modules that enable communication using conventional separate source and channel codes (SSCC), which makes it amenable for easy deployment on legacy systems. Specifically, the learnable modules involve a preprocessing-empowered network (PPEN) for preserving essential semantic information, and a precoder & combiner-enhanced network (PCEN) for efficient transmission over a resource-constrained MIMO channel. We treat existing compression and channel coding modules as non-trainable blocks. Since the parameters of these modules are non-differentiable, we employ a proxy network that mimics their operations when training the learnable modules. Numerical results demonstrate that our scheme can save more than 29% of the channel bandwidth, and requires lower complexity compared to the constrained baselines. We also show its generalization capability to unseen datasets and tasks through extensive experiments
