A Superdirective Beamforming Approach with Impedance Coupling and Field Coupling for Compact Antenna Arrays

In most multiple-input multiple-output (MIMO) communication systems, the antenna spacing is generally no less than half a wavelength. It helps to reduce the mutual coupling and therefore facilitate the system design. The maximum array gain equals the number of antennas in this settings. However, when the antenna spacing is made very small, the array gain of a compact array can be proportional to the square of the number of antennas - a value much larger than the traditional array. To achieve this so-called ``superdirectivity" however, the calculation of the excitation coefficients (beamforming vector) is known to be a challenging problem. In this paper, we address this problem with a novel double coupling-based superdirective beamforming method. In particular, we categorize the antenna coupling effects to impedance coupling and field coupling. By characterizing these two coupling in model, we derive the beamforming vector for superdirective arrays. In order to obtain the field coupling matrix, we propose a spherical wave expansion approach, which is effective in both simulations and realistic scenarios. Moreover, a prototype of the independently controlled superdirective antenna array is developed. Full-wave electromagnetic simulations and real-world experiments validate the effectiveness of our proposed approaches, and superdirectivity is achieved in reality by a compact array with 4 and 5 dipole antennas.Comment: arXiv admin note: text overlap with arXiv:2204.1154

Research Progress on the Effect of New Electrophysical Processing on Multiscale Protein Structure

Protein is an important nutrient required by the human body. The change of protein structure during processing will lead to changes in its functional properties, in turn affecting the quality of foods. There are many physical methods available to alter the structure of proteins to expand their application in the food industry. The new electrophysical processing technology has become a hot spot in the field of food processing due to its advantages of high efficiency, low energy consumption, and slight loss of nutrients. Therefore, this paper reviews the effects of electric field technology (ohmic heating and electrostatic field) and electromagnetic field technology (microwave, radio frequency and magnetic field) on the change of protein structure at multiscales (macroscopic, molecular and microscopic levels), in order provide a theoretical basis for the development and utilization of electromagnetic field processed protein products

Wireless Virtual Network Embedding Algorithm Based on Deep Reinforcement Learning

Wireless network virtualization is widely used to solve the ossification problem of networks, such as 5G and the Internet of Things. The most crucial method of wireless network virtualization is virtual network embedding, which allows virtual networks to share the substrate network resources. However, in wireless networks, link interference is an inherent problem while mapping virtual networks because of the characteristics of wireless channels. To distribute resources efficiently and address the problem of interference, a dynamic embedding algorithm with deep reinforcement learning is proposed. During the training stage, we take resource use and interference from substrate networks as observations to train the agent, and then the agent generates a resource allocation strategy. Aiming at realizing load balance, we reshape the reward function considering the execution ratio and residual ratio of substrate network resources as well as the cost consumed by current virtual network request. Numerical tests show that our embedding approach increases the acceptance ratio and maintains better robustness. Moreover, the results also illustrate that our algorithm maintains a high acceptance ratio while producing less interference and lower cost

Wireless Virtual Network Embedding Algorithm Based on Deep Reinforcement Learning

Wireless network virtualization is widely used to solve the ossification problem of networks, such as 5G and the Internet of Things. The most crucial method of wireless network virtualization is virtual network embedding, which allows virtual networks to share the substrate network resources. However, in wireless networks, link interference is an inherent problem while mapping virtual networks because of the characteristics of wireless channels. To distribute resources efficiently and address the problem of interference, a dynamic embedding algorithm with deep reinforcement learning is proposed. During the training stage, we take resource use and interference from substrate networks as observations to train the agent, and then the agent generates a resource allocation strategy. Aiming at realizing load balance, we reshape the reward function considering the execution ratio and residual ratio of substrate network resources as well as the cost consumed by current virtual network request. Numerical tests show that our embedding approach increases the acceptance ratio and maintains better robustness. Moreover, the results also illustrate that our algorithm maintains a high acceptance ratio while producing less interference and lower cost

Analysis on anti-wear mechanism of bionic non-smooth surface based on discrete phase model

In order to study the lubrication and anti-wear mechanism of the pit-type bionic non-smooth surface used in the low-speed and high-torque seawater hydraulic motor valve plate pair, the discrete phase models of the four pits are simulated under different working conditions. In this study, the trajectories of different diameters particles in the hemispherical pits are analysed, which can reflect the movement of different sizes and masses wear debris in the pits. The discrete phase concentration distributions of the four-kind pits, hemispherical pits, cylindrical pits, four-prism pits and tri-prism pits, are simulation under the same working conditions, which reflects the effect of pit geometry on the movement of wear debris. The discrete phase concentration distributions of four pits moving at different rotation speeds and different rotation radii are calculated, which indicates that the rotation speed of the motor and the distribution of pits on the valve plate will affect the ability of the pit to store wear debris