209 research outputs found
Energy Efficient Spectrum Sensing for State Estimation over A Wireless Channel
The performance of remote estimation over wireless channel is strongly
affected by sensor data losses due to interference. Although the impact of
interference can be alleviated by performing spectrum sensing and then
transmitting only when the channel is clear, the introduction of spectrum
sensing also incurs extra energy expenditure. In this paper, we investigate the
problem of energy efficient spectrum sensing for state estimation of a general
linear dynamic system, and formulate an optimization problem which minimizes
the total sensor energy consumption while guaranteeing a desired level of
estimation performance. The optimal solution is evaluated through both
analytical and simulation results.Comment: 4 pages, 6 figures, accepted to IEEE GlobalSIP 201
Energy-Efficient Spectrum Sensing for Cognitive Radio Enabled Remote State Estimation Over Wireless Channels
The performance of remote estimation over wireless channels is strongly affected by sensor data losses due to interference. Although the impact of interference can be alleviated by applying cognitive radio technique which features in spectrum sensing and transmitting data only on clear channels, the introduction of spectrum sensing incurs extra energy expenditure. In this paper, we investigate the problem of energy-efficient spectrum sensing for remotely estimating the state of a general linear dynamic system, and formulate an optimization problem which minimizes the total sensor energy consumption while guaranteeing a desired level of estimation performance. We model the problem as a mixed integer nonlinear program and propose a simulated annealing based optimization algorithm which jointly addresses when to perform sensing, which channels to sense, in what order and how long to scan each channel. Simulation results demonstrate that the proposed algorithm well balances the sensing energy and transmission energy expenditure and can achieve the desired estimation performance
IEEE Access Special Section Editorial: Secure Modulations for Future Wireless Communications and Mobile Networks
Security has become an extremely important research topic in wireless networks over the last decade, as it is intimately related to both individual privacy and national security. Directional modulation, as a conventional type of secure modulations, transmits confidential information along the desired directions of legitimate receivers, and artificial noise in other directions, to deliberately confuse eavesdroppers in line-of-sight channels. Recently, artificial noise is also introduced into spatial modulation, leading to a secure spatial modulation strategy. In this Special Section in IEEE A CCESS, secure modulation is defined broadly as any secure modulation method, which includes, but is not limited to, secure directional modulation, secure spatial modulation, and secure index modulation
Energy-Efficient Spectrum Sensing for Cognitive Radio Enabled Remote State Estimation Over Wireless Channels
Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing
In the era of 5G mobile communication, there has been a significant surge in
research focused on unmanned aerial vehicles (UAVs) and mobile edge computing
technology. UAVs can serve as intelligent servers in edge computing
environments, optimizing their flight trajectories to maximize communication
system throughput. Deep reinforcement learning (DRL)-based trajectory
optimization algorithms may suffer from poor training performance due to
intricate terrain features and inadequate training data. To overcome this
limitation, some studies have proposed leveraging federated learning (FL) to
mitigate the data isolation problem and expedite convergence. Nevertheless, the
efficacy of global FL models can be negatively impacted by the high
heterogeneity of local data, which could potentially impede the training
process and even compromise the performance of local agents. This work proposes
a novel solution to address these challenges, namely personalized federated
deep reinforcement learning (PF-DRL), for multi-UAV trajectory optimization.
PF-DRL aims to develop individualized models for each agent to address the data
scarcity issue and mitigate the negative impact of data heterogeneity.
Simulation results demonstrate that the proposed algorithm achieves superior
training performance with faster convergence rates, and improves service
quality compared to other DRL-based approaches
Effects of Trichostatin A on Cumulus Expansion during Mouse Oocyte Maturation
This study was conducted to investigate the effects of Trichostatin A (TSA) on cumulus expansion during mouse oocyte maturation. TSA treatment inhibited cumulus expansion and significantly reduced the cumulus expansion index (CEI) (p0.05). TSA treatment blocked the activation of ERK1/2 (p0.05). Collectively, these results suggested that TSA treatment altered ECM gene expression and blocked ERK1/2 activation to inhibit cumulus expansion in the mouse
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