In this paper, a simple dual-band polarization conversion metasurface(PCM) is proposed. This polarization conversion metasurface can be used in 5G applications such as satellites and radar. The polarization conversion is achieved by etching trapezoidal slots on the upper surface of the metasurface unit, resulting in a change in the distribution of the surface current. To achieve a dual-band, an additional resonant frequency is excited by loading two metal vias and two slots on either side of the metal via. In addition, the tuning of the frequency and the control of the operating bandwidth can be achieved by changing the radius size of the meta via and the slot width of the metasurface unit. The simulation results show that the line polarization(LP) conversion to line polarization(LP) is achieved at 37.6~59.5 GHz and 63~75.6 GHz with the average polarization conversion ratio of 94% and 96% in the two bands, respectively, and the lowest polarization conversion ratio of 90%.
A Q-band low-profile FSS with controllable numbers of the band is proposed in this paper. Using the complementarity principle, the branched strip lines are complemented as slots, and the FSS with controllable numbers of the band is designed. Then its working mechanism is analyzed by using the equivalent circuit method. Finally, the model is solved by incidenting Floquet mode using CST. The simulation result is fitted by the equation derived from the equivalent circuit method and shows that there are four center frequencies of the bands (or i.e. four FTZs): 36 GHz, 44 GHz, 47.5 GHz, and 48.2 GHz, respectively. By designing the two and three slots, the controllability of the numbers of the bands has been verified. By analyzing the parameters p, p, of the unit of FSS, the controllability of the position of bands has been verified. In addition, the horizon size of the unit is x=2.24 mm, y=2.02 mm, and the profile is z=0.82 mm which is a small size design. It achieves the design purpose of miniaturization and low profile.
With the development of satellite technology, satellite communication can realize reliable communication with long-distance and large-scale coverage. However, satellite resources are now relatively scarce. To address this feature, this paper focuses on designing a reasonable and efficient elastic network model and prioritizing tasks with Mobile Edge Computing (MEC) technology to maximize the use of satellite node resources while handling urgent tasks promptly. Task offloading is a key technology for MEC, and the required parameters for modeling and optimizing target designs can be obtained through the resource virtualization function in the architecture of elastic satellite network. In addition, users can make efficient offloading decisions among servers on this architecture. We focus on the optimal delay model for a multi-user multi-edge server scenario with priority, i.e., the Software-defined Multi-priority Task Offloading Model (SMTOM). The optimization objective of the scenario is then reduced to a Mixed Integer Nonlinear Programming (MINLP) problem, for which Deep Reinforcement Learning (DRL)-Based Dynamic Task Offloading (DDTO) algorithm is used to solve the task offloading problems in satellite scenarios. The DDTO algorithm improves the average delay performance by 81%, 93%, and 84% compared to the random offloading algorithm, the local offloading algorithm, and the greedy algorithm, respectively.
Facing an increasingly complex electromagnetic environment, modern communication systems must adopt certain antiinterference technology when deploying system equipment and network to ensure the normal operation of wireless communication. Currently, interference recognition is the foundation and key link of anti-interference technology. Among them, the recognition accuracy and the dependence of the algorithm model on training data are challenges that need to be solved urgently. In this paper, a CNN-RNN joint network architecture combining residual network and LSTM network is proposed to recognize the interfering signals. The joint network architecture adopts the parallel combination of residual and LSTM network, where the time-frequency image data of signals is input to the residual network branches while the real part, imaginary part, and spectral amplitude data of signals are input to the LSTM network branches. After simulation verification, the interference recognition result of the joint network is significantly improved compared with the single network. Firstly, compared with the single LSTM network, even though the single LSTM network has reached a very high recognition accuracy, the recognition accuracy of the joint network is still about 1%∼2% higher. What’s more, compared with the single network, the interference noise ratio (INR) generalization ability of the joint network is obviously improved. After training the network with different INR distributions, the recognition accuracy can be maintained. Therefore, it’s not sensitive to the INR distribution of the training data, which can adapt to different distribution conditions of training data and reduces the dependence of the algorithm on training data.
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