Rate-Splitting Multiple Access for 6G Networks: Ten Promising Scenarios and Applications

In the upcoming 6G era, multiple access (MA) will play an essential role in achieving high throughput performances required in a wide range of wireless applications. Since MA and interference management are closely related issues, the conventional MA techniques are limited in that they cannot provide near-optimal performance in universal interference regimes. Recently, rate-splitting multiple access (RSMA) has been gaining much attention. RSMA splits an individual message into two parts: a common part, decodable by every user, and a private part, decodable only by the intended user. Each user first decodes the common message and then decodes its private message by applying successive interference cancellation (SIC). By doing so, RSMA not only embraces the existing MA techniques as special cases but also provides significant performance gains by efficiently mitigating inter-user interference in a broad range of interference regimes. In this article, we first present the theoretical foundation of RSMA. Subsequently, we put forth four key benefits of RSMA: spectral efficiency, robustness, scalability, and flexibility. Upon this, we describe how RSMA can enable ten promising scenarios and applications along with future research directions to pave the way for 6G.Comment: 17 pages, 6 figures, submitted to IEEE Network Magazin

A Lightweight Convolutional Neural Network (CNN) Architecture for Traffic Sign Recognition in Urban Road Networks

Recognizing and classifying traffic signs is a challenging task that can significantly improve road safety. Deep neural networks have achieved impressive results in various applications, including object identification and automatic recognition of traffic signs. These deep neural network-based traffic sign recognition systems may have limitations in practical applications due to their computational requirements and resource consumption. To address this issue, this paper presents a lightweight neural network for traffic sign recognition that achieves high accuracy and precision with fewer trainable parameters. The proposed model is trained on the German Traffic Sign Recognition Benchmark (GTSRB) and Belgium Traffic Sign (BelgiumTS) datasets. Experimental results demonstrate that the proposed model has achieved 98.41% and 92.06% accuracy on GTSRB and BelgiumTS datasets, respectively, outperforming several state-of-the-art models such as GoogleNet, AlexNet, VGG16, VGG19, MobileNetv2, and ResNetv2. Furthermore, the proposed model outperformed these methods by margins ranging from 0.1 to 4.20 percentage point on the GTSRB dataset and by margins ranging from 9.33 to 33.18 percentage point on the BelgiumTS dataset

A pipeline for neuron reconstruction based on spatial sliding volume filter seeding.

Neuron’s shape and dendritic architecture are important for biosignal transduction in neuron networks. And the anatomy architecture reconstruction of neuron cell is one of the foremost challenges and important issues in neuroscience. Accurate reconstruction results can facilitate the subsequent neuron system simulation. With the development of confocal microscopy technology, researchers can scan neurons at submicron resolution for experiments. These make the reconstruction of complex dendritic trees become more feasible; however, it is still a tedious, time consuming, and labor intensity task. For decades, computer aided methods have been playing an important role in this task, but none of the prevalent algorithms can reconstruct full anatomy structure automatically. All of these make it essential for developing new method for reconstruction. This paper proposes a pipeline with a novel seeding method for reconstructing neuron structures from 3D microscopy images stacks. The pipeline is initialized with a set of seeds detected by sliding volume filter (SVF), and then the open curve snake is applied to the detected seeds for reconstructing the full structure of neuron cells. The experimental results demonstrate that the proposed pipeline exhibits excellent performance in terms of accuracy compared with traditional method, which is clearly a benefit for 3D neuron detection and reconstruction

Additional file 1: of Neuron anatomy structure reconstruction based on a sliding filter

This document includes additional figures not included in the paper. Some other tracing results are shown in the supplementary material (12 figures). S1-S7 are some tracing results of BigNeuron datasets. S8-S9 are some other tracing results of NC datasets. S10-S12 are some other tracing results of OP datasets. (PDF 1439 kb

A Facile Route for Patterned Growth of MetalInsulator Carbon Lateral Junction through One-Pot Synthesis

Precise graphene patterning is of critical importance for tailor-made and sophisticated two-dimensional nanoelectronic and optical devices. However, graphene-based heterostructures have been grown by delicate multistep chemical vapor deposition methods, limiting preparation of versatile heterostructures. Here, we report one-pot synthesis of graphene/amorphous carbon (a-C) heterostructures from a solid source of polystyrene via selective photo-cross-linking process. Graphene is successfully grown from neat polystyrene regions, while patterned cross-linked polystyrene regions turn into a-C because of a large difference in their thermal stability. Since the electrical resistance of a-C is at least 2 orders of magnitude higher than that for graphene, the charge transport in graphene/a-C heterostructure occurs through the graphene region. Measurement of the quantum Hall effect in graphene/a-C lateral heterostructures clearly confirms the reliable quality of graphene and well-defined graphene/a-C interface. The direct synthesis of patterned graphene from polymer pattern could be further exploited to prepare versatile heterostructures.open0