Reconfigurable Intelligent Surface(RIS),as one of the potential key technologies for 5G-Advanced and 6G,boasts ad-vantages such as quasi-passivity,continuous aperture,ease of deployment,low power consumption,software programmability,broad-band response,and low thermal noise.It has the capability to reshape the electromagnetic environment and enable a variety of func-tions.This study conducts a statistical analysis on the trends in application volume,regional distribution,and key players in technolog-ical innovation related to 6G intelligent surface technology patents.Furthermore,it examines the technological evolution paths of three main branches:RIS device,beamforming,channel estimation,and Integrated Sensing and Communication(ISAC).By relying on these patent technology evolution paths,the study uncovers the latest development trends and research hotspots in 6G intelligent surface technology.Through tracking and analyzing global patents focused on 6G intelligent surfaces,the technical information embedded in these patents is fully explored,highlighting the leading role of patent information in domestic technological development.
Radio Frequency Identification (RFID) is one of the key technologies of the Internet of Things. However, during its application, it faces a huge challenge of co-frequency interference cancellation, that is, the tag collision problem. The multi-tag anti-collision problem is modeled as a Blind Source Separation (BSS) problem from the perspective of system communication transmission layer signal processing. In order to reduce the cost of the reader antenna, this paper uses the boundedness of the tag communication signal to propose an underdetermined RFID tag anti-collision method based on Bounded Component Analysis (BCA). This algorithm converts the underdetermined tag into the signal collision model is combined with the BCA mechanism. Verification analysis was conducted using simulation data. The experimental results show that compared with the nonnegative matrix factorization (NMF) algorithm based on minimum correlation and minimum volume constraints, the bounded component analysis method proposed in this article can perform better. Solving the underdetermined collision problem greatly improves the effect of eliminating co-channel interference of tag signals, improves the system bit error rate performance, and reduces the complexity of the underdetermined model system.
Facial age recognition has been widely used in real-world applications. Most of current facial age recognition methods use deep learning to extract facial features to identify age. However, due to the high dimension features of faces, deep learning methods might extract a lot of redundant features, which is not beneficial for facial age recognition. To improve facial age recognition effectively, this paper proposed the deep manifold learning (DML), a combination of deep learning and manifold learning. In DML, deep learning was used to extract high-dimensional facial features, and manifold learning selected age-related features from these high-dimensional facial features for facial age recognition. Finally, we validated the DML on Multivariate Observations of Reactions and Physical Health (MORPH) and Face and Gesture Recognition Network (FG-NET) datasets. The results indicated that the mean absolute error (MAE) of MORPH is 1.60 and that of FG-NET is 2.48. Moreover, compared with the state of the art facial age recognition methods, the accuracy of DML has been greatly improved.
Terahertz (THz) communication is one of the key technologies of the sixth-generation mobile communication sys-tem (6G) and is receiving more and more attention for its large bandwidth and ultra-high data rate features. To support the performance evaluation of THz wireless communication devices, it is critical to model wireless channels that can characterize the channel properties. In this paper, a THz channel model is proposed based on the scattering characterization. The channel model is then applied to generate channel coefficients and power delay profiles (PDPs) in an indoor scenario. The evaluation results indicate that the proposed channel model can appropriately characterize THz scattering properties and support THz communication system design. Finally, we summarize the advantages of this model and point out the next research content.
In this paper, the minimum mean square error (MMSE) channel estimation for intelligent reflecting surface (IRS) assisted wireless communication systems is investigated. In the considered setting, each row vector of the equivalent channel matrix from the base station (BS) to the users is shown to be Bessel K distributed, and all these row vectors are independent of each other. By introducing a Gaussian scale mixture model, we obtain a closed-form expression for the MMSE estimate of the equivalent channel, and determine analytical upper and lower bounds on the mean square error. Using the central limit theorem, we conduct an asymptotic analysis of the MMSE estimate, and show that the upper bound on the mean square error of the MMSE estimate is equal to the asymptotic mean square error of the MMSE estimation when the number of reflecting elements at the IRS tends to infinity. Numerical simulations show that the gap between the upper and lower bounds are very small, and they almost overlap with each other at medium signal-to-noise ratio (SNR) levels and moderate number of elements at the IRS.
