An Intelligent Admission Control Scheme for Dynamic Slice Handover Policy in 5G Network Slicing

5G use cases,for example enhanced mobile broadband(eMBB),massive machine-type communications(mMTC),and an ultra-reliable low latency communication(URLLC),need a network architecture capable of sustaining stringent latency and bandwidth requirements;thus,it should be extremely flexible and dynamic.Slicing enables service providers to develop various network slice architectures.As users travel from one coverage region to another area,the callmust be routed to a slice thatmeets the same or different expectations.This research aims to develop and evaluate an algorithm to make handover decisions appearing in 5G sliced networks.Rules of thumb which indicates the accuracy regarding the training data classification schemes within machine learning should be considered for validation and selection of the appropriate machine learning strategies.Therefore,this study discusses the network model’s design and implementation of self-optimization Fuzzy Qlearning of the decision-making algorithm for slice handover.The algorithm’s performance is assessed by means of connection-level metrics considering the Quality of Service(QoS),specifically the probability of the new call to be blocked and the probability of a handoff call being dropped.Hence,within the network model,the call admission control(AC)method is modeled by leveraging supervised learning algorithm as prior knowledge of additional capacity.Moreover,to mitigate high complexity,the integration of fuzzy logic as well as Fuzzy Q-Learning is used to discretize state and the corresponding action spaces.The results generated from our proposal surpass the traditional methods without the use of supervised learning and fuzzy-Q learning.

Building Semantic Communication System via Molecules:An End-to-End Training Approach

The concept of semantic communication provides a novel approach for applications in scenarios with limited communication resources.In this paper,we propose an end-to-end(E2E)semantic molecular communication system,aiming to enhance the efficiency of molecular communication systems by reducing the transmitted information.Specifically,following the joint source channel coding paradigm,the network is designed to encode the task-relevant information into the concentration of the information molecules,which is robust to the degradation of the molecular communication channel.Furthermore,we propose a channel network to enable the E2E learning over the non-differentiable molecular channel.Experimental results demonstrate the superior performance of the semantic molecular communication system over the conventional methods in classification tasks.

基于SLICE模型的东台“三医”协同发展和治理实践分析

医疗、医保、医药协同发展和治理是实现卫生健康事业高质量发展和深化医药卫生体制改革的关键。剖析总结了江苏省东台市医疗、医保、医药协同发展和治理改革在战略设计(导航仪)、组织领导(引擎机)、资源统合(燃料箱)、协同工具(润滑剂)和评估反馈(晴雨表)全闭环方面的经验举措,提炼为“三医”协同发展和治理的SLICE模型,并提出在坚持以改革创新激发“三医”协同发展和治理活力的基础上,充分发挥各级党委、政府的主导作用,建立“三医”协同发展和治理跨部门议事协调机制,吸纳多元主体参与协同治理并强化协同治理措施的系统性,以确保“三医”协同发展和治理改革行稳致远。

Strengthening network slicing for Industrial Internet with deep reinforcement learning

Industrial Internet combines the industrial system with Internet connectivity to build a new manufacturing and service system covering the entire industry chain and value chain.Its highly heterogeneous network structure and diversified application requirements call for the applying of network slicing technology.Guaranteeing robust network slicing is essential for Industrial Internet,but it faces the challenge of complex slice topologies caused by the intricate interaction relationships among Network Functions(NFs)composing the slice.Existing works have not concerned the strengthening problem of industrial network slicing regarding its complex network properties.Towards this end,we aim to study this issue by intelligently selecting a subset of most valuable NFs with the minimum cost to satisfy the strengthening requirements.State-of-the-art AlphaGo series of algorithms and the advanced graph neural network technology are combined to build the solution.Simulation results demonstrate the superior performance of our scheme compared to the benchmark schemes.

Resource Allocation for Cognitive Network Slicing in PD-SCMA System Based on Two-Way Deep Reinforcement Learning

In this paper,we propose the Two-way Deep Reinforcement Learning(DRL)-Based resource allocation algorithm,which solves the problem of resource allocation in the cognitive downlink network based on the underlay mode.Secondary users(SUs)in the cognitive network are multiplexed by a new Power Domain Sparse Code Multiple Access(PD-SCMA)scheme,and the physical resources of the cognitive base station are virtualized into two types of slices:enhanced mobile broadband(eMBB)slice and ultrareliable low latency communication(URLLC)slice.We design the Double Deep Q Network(DDQN)network output the optimal codebook assignment scheme and simultaneously use the Deep Deterministic Policy Gradient(DDPG)network output the optimal power allocation scheme.The objective is to jointly optimize the spectral efficiency of the system and the Quality of Service(QoS)of SUs.Simulation results show that the proposed algorithm outperforms the CNDDQN algorithm and modified JEERA algorithm in terms of spectral efficiency and QoS satisfaction.Additionally,compared with the Power Domain Non-orthogonal Multiple Access(PD-NOMA)slices and the Sparse Code Multiple Access(SCMA)slices,the PD-SCMA slices can dramatically enhance spectral efficiency and increase the number of accessible users.

User Scheduling and Slicing Resource Allocation in Industrial Internet of Things

Heterogeneous base station deployment enables to provide high capacity and wide area coverage.Network slicing makes it possible to allocate wireless resource for heterogeneous services on demand.These two promising technologies contribute to the unprecedented service in 5G.We establish a multiservice heterogeneous network model,which aims to raise the transmission rate under the delay constraints for active control terminals,and optimize the energy efficiency for passive network terminals.A policygradient-based deep reinforcement learning algorithm is proposed to make decisions on user association and power control in the continuous action space.Simulation results indicate the good convergence of the algorithm,and higher reward is obtained compared with other baselines.

DTHN: Dual-Transformer Head End-to-End Person Search Network

Person search mainly consists of two submissions,namely Person Detection and Person Re-identification(reID).Existing approaches are primarily based on Faster R-CNN and Convolutional Neural Network(CNN)(e.g.,ResNet).While these structures may detect high-quality bounding boxes,they seem to degrade the performance of re-ID.To address this issue,this paper proposes a Dual-Transformer Head Network(DTHN)for end-to-end person search,which contains two independent Transformer heads,a box head for detecting the bounding box and extracting efficient bounding box feature,and a re-ID head for capturing high-quality re-ID features for the re-ID task.Specifically,after the image goes through the ResNet backbone network to extract features,the Region Proposal Network(RPN)proposes possible bounding boxes.The box head then extracts more efficient features within these bounding boxes for detection.Following this,the re-ID head computes the occluded attention of the features in these bounding boxes and distinguishes them from other persons or backgrounds.Extensive experiments on two widely used benchmark datasets,CUHK-SYSU and PRW,achieve state-of-the-art performance levels,94.9 mAP and 95.3 top-1 scores on the CUHK-SYSU dataset,and 51.6 mAP and 87.6 top-1 scores on the PRW dataset,which demonstrates the advantages of this paper’s approach.The efficiency comparison also shows our method is highly efficient in both time and space.

QUATERNIONIC SLICE REGULAR FUNCTIONS AND QUATERNIONIC LAPLACE TRANSFORMS

The functions studied in the paper are the quaternion-valued functions of a quaternionic variable.It is shown that the left slice regular functions and right slice regular functions are related by a particular involution,and that the intrinsic slice regular functions play a central role in the theory of slice regular functions.The relation between left slice regular functions,right slice regular functions and intrinsic slice regular functions is revealed.As an application,the classical Laplace transform is generalized naturally to quaternions in two different ways,which transform a quaternion-valued function of a real variable to a left or right slice regular function.The usual properties of the classical Laplace transforms are generalized to quaternionic Laplace transforms.

End-to-End Joint Multi-Object Detection and Tracking for Intelligent Transportation Systems

Environment perception is one of the most critical technology of intelligent transportation systems(ITS).Motion interaction between multiple vehicles in ITS makes it important to perform multi-object tracking(MOT).However,most existing MOT algorithms follow the tracking-by-detection framework,which separates detection and tracking into two independent segments and limit the global efciency.Recently,a few algorithms have combined feature extraction into one network;however,the tracking portion continues to rely on data association,and requires com‑plex post-processing for life cycle management.Those methods do not combine detection and tracking efciently.This paper presents a novel network to realize joint multi-object detection and tracking in an end-to-end manner for ITS,named as global correlation network(GCNet).Unlike most object detection methods,GCNet introduces a global correlation layer for regression of absolute size and coordinates of bounding boxes,instead of ofsetting predictions.The pipeline of detection and tracking in GCNet is conceptually simple,and does not require compli‑cated tracking strategies such as non-maximum suppression and data association.GCNet was evaluated on a multivehicle tracking dataset,UA-DETRAC,demonstrating promising performance compared to state-of-the-art detectors and trackers.

An End-to-End Machine Learning Framework for Predicting Common Geriatric Diseases

Interdisciplinary applications between information technology and geriatrics have been accelerated in recent years by the advancement of artificial intelligence,cloud computing,and 5G technology,among others.Meanwhile,applications developed by using the above technologies make it possible to predict the risk of age-related diseases early,which can give caregivers time to intervene and reduce the risk,potentially improving the health span of the elderly.However,the popularity of these applications is still limited for several reasons.For example,many older people are unable or unwilling to use mobile applications or devices(e.g.smartphones)because they are relatively complex operations or time-consuming for older people.In this work,we design and implement an end-to-end framework and integrate it with the WeChat platform to make it easily accessible to elders.In this work,multifactorial geriatric assessment data can be collected.Then,stacked machine learning models are trained to assess and predict the incidence of common diseases in the elderly.Experimental results show that our framework can not only provide more accurate prediction(precision:0.8713,recall:0.8212)for several common elderly diseases,but also very low timeconsuming(28.6 s)within a workflow compared to some existing similar applications.

Attention-based neural network for end-to-end music separation

The end-to-end separation algorithm with superior performance in the field of speech separation has not been effectively used in music separation.Moreover,since music signals are often dual channel data with a high sampling rate,how to model longsequence data and make rational use of the relevant information between channels is also an urgent problem to be solved.In order to solve the above problems,the performance of the end-to-end music separation algorithm is enhanced by improving the network structure.Our main contributions include the following:(1)A more reasonable densely connected U-Net is designed to capture the long-term characteristics of music,such as main melody,tone and so on.(2)On this basis,the multi-head attention and dualpath transformer are introduced in the separation module.Channel attention units are applied recursively on the feature map of each layer of the network,enabling the network to perform long-sequence separation.Experimental results show that after the introduction of the channel attention,the performance of the proposed algorithm has a stable improvement compared with the baseline system.On the MUSDB18 dataset,the average score of the separated audio exceeds that of the current best-performing music separation algorithm based on the time-frequency domain(T-F domain).

Opportunistic admission and resource allocation for slicing enhanced IoT networks

Network slicing is envisioned as one of the key techniques to meet the extremely diversified service requirements of the Internet of Things(IoT)as it provides an enhanced user experience and elastic resource configuration.In the context of slicing enhanced IoT networks,both the Service Provider(SP)and Infrastructure Provider(InP)face challenges of ensuring efficient slice construction and high profit in dynamic environments.These challenges arise from randomly generated and departed slice requests from end-users,uncertain resource availability,and multidimensional resource allocation.Admission and resource allocation for distinct demands of slice requests are the key issues in addressing these challenges and should be handled effectively in dynamic environments.To this end,we propose an Opportunistic Admission and Resource allocation(OAR)policy to deal with the issues of random slicing requests,uncertain resource availability,and heterogeneous multi-resources.The key idea of OAR is to allow the SP to decide whether to accept slice requests immediately or defer them according to the load and price of resources.To cope with the random slice requests and uncertain resource availability,we formulated this issue as a Markov Decision Process(MDP)to obtain the optimal admission policy,with the aim of maximizing the system reward.Furthermore,the buyer-seller game theory approach was adopted to realize the optimal resource allocation,while motivating each SP and InP to maximize their rewards.Our numerical results show that the proposed OAR policy can make reasonable decisions effectively and steadily,and outperforms the baseline schemes in terms of the system reward.

End-to-End Auto-Encoder System for Deep Residual Shrinkage Network for AWGN Channels

With the rapid development of deep learning methods, the data-driven approach has shown powerful advantages over the model-driven one. In this paper, we propose an end-to-end autoencoder communication system based on Deep Residual Shrinkage Networks (DRSNs), where neural networks (DNNs) are used to implement the coding, decoding, modulation and demodulation functions of the communication system. Our proposed autoencoder communication system can better reduce the signal noise by adding an “attention mechanism” and “soft thresholding” modules and has better performance at various signal-to-noise ratios (SNR). Also, we have shown through comparative experiments that the system can operate at moderate block lengths and support different throughputs. It has been shown to work efficiently in the AWGN channel. Simulation results show that our model has a higher Bit-Error-Rate (BER) gain and greatly improved decoding performance compared to conventional modulation and classical autoencoder systems at various signal-to-noise ratios.

智能电网中基于二分图匹配的网络切片资源分配算法

为了解决智能电网中多类业务的服务质量需求难以同时得到满足的问题并兼顾电力终端和网络侧经济效用,提出了一种基于二分图匹配的网络切片资源分配算法。针对智能电网场景中的控制类和采集类业务,为电力终端分别制定相应的投标信息,并据此计算支付价格和效用矩阵;将网络切片与电力终端之间的资源分配建模为二分图匹配问题,根据不同业务的时延、传输速率或能耗需求,向终端分配不同的切片资源以最大化系统效用。仿真结果表明,相较于已有的双向拍卖算法和贪心算法,所提算法能够提高10%~20%的系统效用。

RedCap部署策略建议

RedCap作为轻量级5G技术,是蜂窝物联网的重要演进方向,它可有效兼顾行业对技术性能和部署成本的双重需求,有助于加速5G技术融入千行百业。分析了RedCap关键特性、基本通信功能要求、产业生态及应用场景等,提出RedCap部署策略及网络、业务规划建议,推动蜂窝物联网持续向端网协同方向发展和演进。

采用MSCT灌注成像检查评估周围型非小细胞肺癌分化程度的可行性分析

目的分析多层螺旋CT(MSCT)灌注成像检查评估周围型非小细胞肺癌(NSCLC)分化程度的可行性。方法选取本院2017年7月至2018年10月本院收治确诊的52例周围型NSCLC患者作为研究对象,比较不同分化级别患者的MSCT灌注成像参数;分析灌注参数与分化程度的相关性。结果高分化、中分化周围型NSCLC患者BF、BV、PS、MTT及PH数值均高于低分化周围型NSCLC,以高分化周围型NSCLC的BF、BV、PS、MTT及PH数值最高。各个灌注参数值,其中高分化、中分化周围型NSCLC的BF、PH与低分化周围型NSCLC比较差异显著(P<0.05),三者BV、PS及MTT数值比较,均为明显差异(P>0.05)。周围型NSCLC患者灌注参数BF、PH与其分化程度成负相关,且相关性显著(P<0.05)。结论MSCT灌注成像检查可有效反映周围型NSCLC的分化程度,其灌注参数中BF、PH对评估其分化程度有一定帮助,与周围型NSCLC分化程度具有一定相关性。

基于瓦片合并的影像快速切片技术研究

随着遥感影像质量提升和更新频率的加快,传统方式切片计算能力受限,为满足影像快速切片的需求,该文借鉴并行计算的思路,设计实现一套瓦片并行切片和基于瓦片合并的影像快速切片算法。从实验结果来看切片效率得到明显提升,有效解决单机切片效率低下的问题。

同时多层成像技术用于弥散张量成像评估脑胶质瘤

目的探讨同时多层成像(SMS)技术用于弥散张量成像(DTI)评估脑胶质瘤的价值。方法前瞻性对34例脑胶质瘤患者采集颅脑常规DTI及SMS-DTI,对比2种图像质量主观评分、信噪比(SNR)及对比度信噪比(CNR),以及基于2种图像所获全脑纤维束数及肿瘤相对各向异性分数(rFA)和平均扩散率(rMD)。结果34例中,23例为高级别、11例为低级别胶质瘤。SMS-DTI整体图像质量、显示肿瘤边缘清晰度和磁敏感伪影主观评分与常规DTI差异均无统计学意义(P均>0.05),而其SNR、CNR均低于常规DTI(P均<0.05);基于SMS-DTI所获不同病理分级脑胶质瘤患者全脑纤维束数及肿瘤rFA和rMD与常规DTI差异均无统计学意义(P均>0.05)。结论SMS技术用于DTI评估脑胶质瘤可在保证图像质量及定量分析结果准确性的前提下有效缩短采集时间。

烫漂预处理对苹果干燥过程中微观结构及质构品质的影响

为提高烫漂预处理后苹果片的热风干燥效率和品质,本研究系统评估了热水烫漂与真空蒸汽脉动烫漂两种预处理方法对苹果片的宏观干燥效果。研究结果表明,在真空度0.07 MPa、烫漂2次和烫漂时间3 min条件下真空蒸汽脉动烫漂预处理提升了苹果片的复水比和质构品质,同时缩短了干燥时间。通过石蜡切片、显微观测、图像处理等技术对两种预处理方法干燥的苹果片微观结构进行研究,对比分析在不同烫漂条件(热水烫漂:温度和时间;真空蒸汽脉动烫漂:真空度、次数和时间)下苹果片的细胞横截面面积、周长、当量直径、细胞圆度、壁面粗糙度因子和孔隙率的变化规律。结果显示,随着漂烫温度的升高、时间的延长和烫漂次数的增加,苹果片的细胞会产生使后续干燥过程中水分加速蒸发的变化,从而缩短干燥时间。此外,通过多项式拟合建立了微观结构参数与复水比之间的关系方程。研究结果可为揭示真空蒸汽脉动烫漂预处理对苹果微观结构和宏观品质影响的机理提供理论依据。

基于5G+V2X的融合组网车路协同系统

随着5G通信技术的发展,传统的基于PC5接口方式的车联网系统在时延和可靠性等方面,性能指标无法匹配车联网系统快速发展的要求。本文深入探讨了5G通信技术与V2X融合组网在车路协同系统中的应用前景,通过集成5G网络的高速率、低时延特性与V2X的广泛连接能力,提出了一种创新的融合组网架构,旨在构建更高效、安全的智慧交通生态。研究内容涵盖了系统架构设计、关键技术分析、应用场景示例、实验验证以及对未来的展望,为未来车路协同系统的部署与优化提供了全面的理论依据和技术指南。