The Sixth Generation (6G) Wireless Communication Network (WCN) is the successive provision to ameliorate the gain with ultra-low latency, and e xtremely high energy efficiency. The 6G WCN enables the specifications of artificial intelligence to optimize the services and capabilities. The vision of the 6G era is expected to address a seamless fusion of communication between the human, physical world, and digital world. The latest 6G WCN standard is a fundamental foundation and requires immense attention in the field of research. This paper presents the framework of 6G WCN with an illustration of its key technologies. The different technologies involved in 6G are well explained with the demonstration of the communication scenario such that the key performance indicators are improved with major differences. The primary contribution of this paper is the explanation of the 6G with the technologies that have a drastic impact on the characteristic aspects of a wireless communication network such as data rate, spectrum efficiency, energy efficiency, connection density, and reliability. All these technologies have the capability to revolutionize the subsequent WCN.

6G: Technology Evolution in Future Wireless Networks

Optical Properties Estimation of Photonic Crystal Fiber Using Gaussian Process Regression

Three-dimensional integrated optical fiber devices: emergence and applications

Abstract The combination of photonic crystal fiber (PCF) and graphene-supporting surface plasmon polaritons (SPP) presents a new approach to achieving a plasmonic sensor with adjustable properties in the terahertz (THz) frequency range. In this study, we investigate a liquid-core PCF-based graphene plasmonic sensor, where the analyte to be detected is located on both the sensing layer surface and the fiber core. As a result, the dispersion relations of both graphene plasmon (GP) and core-guide mode can be influenced by the analyte, leading to a negative refractive index (RI) wavelength sensitivity. This unique performance is attributed to the higher modulation degree of the core mode on the analyte RI (Δneff.core) compared to that of the GP mode (Δneff.GP). By reducing the graphene Fermi energy, a positive sensibility is achieved with the modulation relationship of Δneff.core &lt; Δneff.GP. Subsequently, the geometry dependence is explored to optimize the sensing capabilities. Furthermore, we demonstrate the sensor’s tunability by dynamically varying the graphene Fermi energy ( E f ). By adjusting the E f from 0.6 to 0.9 eV, the detection range can be artificially shifted from 0.554–0.574 THz to 0.686–0.724 THz, obtaining a tunability of 0.44 THz/eV and a higher sensitivity of 1.2667 THz/RIU. This design facilitates the efficient utilization of the limited bandwidth to detect various RIs and provides a flexible approach to constructing multiple sensing channels. To the best of our knowledge, this is the first report of graphene plasmonic sensing based on core-filled PCF in the THz frequency range. The novel analysis method of modulation degree and dispersion matching has the potential to be widely applied in THz plasmonic sensing and could lead to various nanoscience applications. 

Dynamic Tunable Liquid-Core Photonic Crystal Fiber Sensor Based on Graphene Plasmon

Accurate spectrum prediction in design of terahertz (THz) devices remains challenging, especially for THz fibers. In this article, we propose an approach based on artificial intelligence (AI) assisted finite element method (FEM) to achieve accurate spectrum prediction for the design of THz fiber operating in 6G communication window. The antiresonant THz fiber has been selected to verify the effectiveness of this method. Initially, the principle and physical modeling of antiresonant THz fiber are analyzed. The spectra of THz fibers with different structural parameters are designed and predicted by FEM simulation. Then, the THz fibers are fabricated by 3D printing technology and the spectra are measured by a THz time domain spectroscopy system. The spectral dataset obtained by both forms are prepared for the modeling of AI-assisted FEM methods. Different AI algorithms are induced in FEM to predict experimental spectrum, including elman neural network (Elman), support vector machines (SVM), and general regression neutral network (GRNN). The prediction performance obtained by different methods are compared and analyzed comprehensively to confirm the effectiveness of proposed methods. The AI-assisted FEM methods show great improvement of prediction accuracy in the design of THz fibers. It provides accurate data support for THz device modeling assisted by machine learning.

Artificial intelligence-assisted accurate spectrum prediction in design of terahertz fiber operating in 6G communication window

Temperature measurement with error suppression based on dual-wavelength fiber ring laser sensor

A terahertz (THz) fiber is of interest due to low loss and easy beam shaping. Graphene plays an important role in modulating the optical signal. In this paper, a graphene-coated D-shaped THz fiber modulator is proposed. The performance of the proposed modulator is investigated by the finite element method. The inference of geometric parameters of the structure on the proposed modulator is also analyzed, and an optimal structure is obtained. Simulation results show that the confinement loss and the full width at half-maximum (FWHM) can be tuned by adjusting the Fermi level of graphene. The proposed THz fiber modulator shows potential in future THz fiber communications.

https://www.frontiersin.org/articles/10.3389/fphy.2023.1202839/pdf

Graphene-coated D-shaped terahertz fiber modulator

Synthetic Aperture Radar (SAR) image despeckling is a basic task in SAR image processing. The size of noise variance affects the level of image speckle removal. Excessive variance can lead to the loss of details in the image, while conversely, it can still result in low-frequency noise in the denoised image. In this paper, we start from a sample matrix consisting of similar patches and calculate the local noise variance of each sample matrix. The PCA coefficients are contracted using the local noise variance with LMMSE. For ease of computation, we degenerate the coefficient contraction method to a kernel-paradigm minimization form using the mean of the local noise variance. Finally, the estimation bias induced by linear versus nonlinear signals is balanced by dividing the patches into homogeneous and heterogeneous categories using a region partitioning technique. The despeckling experimental results of simulated and real SAR images show good performance in both quantitative measurement and visual perception.

SAR Image Despeckling Based on Local Noise Variance

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Artificial intelligence-assisted accurate spectrum prediction in design of terahertz fiber operating in 6G communication window

Temperature measurement with error suppression based on dual-wavelength fiber ring laser sensor

Graphene-coated D-shaped terahertz fiber modulator

SAR Image Despeckling Based on Local Noise Variance