No abstract

No abstract

No abstract

No abstract

In next-generation communications, massive machine-type communications (mMTC) induce severe burden on base stations. To address such an issue, automatic modulation classification (AMC) can help to reduce signaling overhead by blindly recognizing the modulation types without handshaking. Thus, it plays an important role in future intelligent modems. The emerging deep learning (DL) technique stores intelligence in the network, resulting in superior performance over traditional approaches. However, conventional DL-based approaches suffer from heavy training overhead, memory overhead, and computational complexity, which severely hinder practical applications for resource-limited scenarios, such as Vehicle-to-Everything (V2X) applications. Furthermore, the overhead of online retraining under time-varying fading channels has not been studied in the prior arts. In this work, an accumulated polar feature-based DL with a channel compensation mechanism is proposed to cope with the aforementioned issues. Firstly, the simulation results show that learning features from the polar domain with historical data information can approach nearoptimal performance while reducing training overhead by 99.8 times. Secondly, the proposed neural network-based channel estimator (NN-CE) can learn the channel response and compensate for the distorted channel with 13% improvement. Moreover, in applying this lightweight NN-CE in a time-varying fading channel, two efficient mechanisms of online retraining are proposed, which can reduce transmission overhead and retraining overhead by 90% and 76%, respectively. Finally, the performance of the proposed approach is evaluated and compared with prior arts on a public dataset to demonstrate its great efficiency and lightness. The lightweight and efficient learning features of the proposed mechanism will be very attractive for future resourceconstrained/aware IoT/V2X applications.

To develop intelligent receivers, automatic modulation classification (AMC) plays an important role for better spectrum utilization. The emerging deep learning (DL) technique has received much attention in AMC due to its superior performance in classifying data with deep structure. In this work, a novel polar-based deep learning architecture with channel compensation network (CCN) is proposed. Our test results show that learning features from polar domain ( -) can improve recognition accuracy by 5% and reduce training overhead by 48%. Besides, the proposed CCN is also robust to channel fading, such as amplitude and phase offsets, and can improve the recognition accuracy by 14% under practical channel environments.

No abstract

The cerebrospinal fluid (CSF) of C57BL/6 mice infected with Angiostrongylus cantonensis was examined for kinetic changes in oxidative stress parameters, including reactive oxygen species (ROS), superoxide dismutase (SOD), catalase, malondialdehyde (MDA), 8-isoprostane, and 8-hydroxy-2'-deoxyguanosine (8-OHdG). The ROS increased gradually in the early stage of infection. During days 12-30 post-infection, the infected mice revealed ROS levels significantly higher than that in uninfected controls (P < 0.001). The ROS levels peaked at day 24 and then returned to that observed in uninfected controls at day 45 post-infection. The kinetics of MDA, 8-isoprostane, and 8-OHdG concentration changes observed in the CSF of the infected mice corresponded with kinetic changes in ROS levels. Thus, the excess ROS caused lipid peroxidation and DNA damage to cells in the central nervous system (CNS) of mice infected with A. cantonensis despite the increased antioxidant SOD and catalase enzyme activities during post-infection days 12-30. The oxidative stress in the CNS of C57BL/6 mice was apparently increased by diseases associated with A. cantonensis infection.