H-CRAN中IRS辅助的D2D系统资源分配与RCG波束成形优化

以异构云无线电接入网(heterogeneous cloud radio access network,H-CRAN)中智能反射面(intelli‐gent reflecting surface,IRS)辅助的端到端(device-to-device,D2D)通信系统为背景,研究了该系统中以和速率最大化为目标的资源分配与黎曼共轭梯度(Riemannian conjugate gradient,RCG)波束成形优化方法。以最大化系统和速率为优化目标,构造子信道复用系数、发射功率门限以及IRS反射系数模约束等多约束优化问题。对于该非线性混合整数规划问题,提出了一种基于相对信道强度的延迟接受算法,以获得信道复用系数。随后将目标优化问题分解为两个子问题进行交替优化。对于发射功率优化子问题,使用逐次凸逼近(successive convex approximation,SCA)方法进行求解。对于IRS波束成形子问题,将IRS相移约束转化为复圆流形后,采用RCG算法进行求解。仿真结果表明,当IRS反射阵源数为50、基站最大发射功率为46 dBm时,与现有信道分配方案和随机信道分配方案相比,所提信道分配方案的和速率性能分别提高了5.2 bit/(s·Hz)和14.6 bit/(s·Hz)。与无IRS通信场景相比,部署IRS的和速率性能显著提高约31.2 bit/(s·Hz)。

基于改进白鲸优化算法的D2D通信功率控制

D2D(Device-to-Device)通信作为未来移动通信网络的关键技术,为用户提供了直接通信的便利性和资源共享的高效性.然而,D2D通信的功率控制一直是影响通信质量和系统性能的关键问题.为解决这一问题,将精英反向学习、自适应权重两种策略引入到白鲸优化算法(Beluga Whale Optimization,BWO)中,并利用莱维飞行的随机步长策略来增加算法寻优的多样性,提出了基于改进白鲸优化算法的D2D通信功率控制方法.该方法利用最优解的信息引导搜索过程,可提高搜索效率和全局收敛,并能够有效提高通信效率和系统稳定性.为了验证所提出方法的有效性,开展了大量的数值仿真实验.结果显示,基于改进白鲸优化算法的D2D通信功率控制方法在增加系统吞吐量、减少干扰方面有显著的改善.同时,提出的算法相对于已有的算法有着更出色的收敛性与鲁棒性,在不同通信环境和参数设置下都能表现出更稳定的性能.

城市街道下场景IRS辅助D2D通信系统波束成形设计

针对城市街道场景下蜂窝用户和D2D通信用户共享频谱以及城市街道下无线信道特性,该文提出一种IRS辅助的联合波束成形设计方法。在D2D链路信号与干扰加噪声比的约束下,以最大化蜂窝用户容量为目标,设计了最优的波束形成向量、相移矩阵和D2D链路发射功率。引入松弛变量将非凸且变量耦合的优化问题转换为解耦后的凸优化问题和二分法搜索功率分配,采用黎曼共轭梯度算法对反射相移矩阵进行优化。仿真结果表明,所提算法收敛性较好,且与基准方案相比能有效地提升用户信道容量。

基于D2D的车联网资源分配与模式选择方案

针对当前设备到设备(D2D:Device-to-Device)通信在车联网端应用时只考虑提高D2D的复用模式的资源利用率或系统遍历容量,而未将D2D的其他模式纳入到系统内的问题,提出一种兼顾资源分配与模式选择的算法。通过优先复用模式的方式提高通信资源利用率,对不满足复用模式的D2D用户对(D-UE:Device-to-device UsErs)采用蜂窝模式,采取和蜂窝用户(C-UE:Cellular UsErs)相同的方案满足D-UE的基础需求,考虑D-UE用户与蜂窝用户和基站(BS:Base Station)距离、D-UE间的信噪比等因素对D-UE分配不同的资源接入模式。理论计算与仿真结果均表明所提资源分配与模式选择方案能有效提升系统容量及D-UE的服务质量(QoS:Quality of Service)。

D2D-MEC技术在异构车联网资源分配优化中的研究

在异构车联网中,资源分配和性能优化是关键挑战之一。D2D-MEC技术作为一种有潜力的解决方案,允许车辆之间直接通信并利用边缘计算资源。本研究旨在探讨如何有效地利用D2D-MEC技术来改进异构车联网的资源分配和性能。我们提出了一种资源分配算法,以最大程度地减少网络拥塞,降低通信时延,并提高整体系统效率。通过仿真和实验结果,我们验证了该算法在不同场景下的有效性,展示了D2D-MEC技术在异构车联网中的巨大潜力。

D2D通信复用异构蜂窝网络中分布式路由资源分配方法

由于设备到设备(Device-To-Device,D2D)通信复用异构蜂窝网络环境的复杂性和动态性,难以实现高效、均衡的路由资源利用,文章提出D2D通信复用异构蜂窝网络中分布式路由资源分配方法。采用分布式系统模型构建D2D通信复用异构蜂窝网络的拓扑结构,基于邻居节点信息交换实现D2D设备发现,并引入自适应的会话建立策略建立通信链路,采用贪婪算法为D2D用户分配子信道,实现路由资源的均衡分配。实验结果表明,设计方法在提升D2D用户之间数据传输速率方面具有较好的性能,适用于D2D通信复用异构蜂窝网络中的路由资源分配。

基于改进NSGA-Ⅲ的D2D协同MEC多目标优化研究

在当前的移动边缘计算(Mobile Edge Computing,MEC)模型中,由于任务是直接上传到MEC服务器执行,存在边缘服务器的计算压力大、空闲移动设备上的资源未得到充分利用等问题。使用边缘网络中的空闲设备进行协同计算,能够实现用户闲置资源的合理利用,增强MEC的计算能力。因此,提出了一种利用终端直通(Device-to-Device,D2D)进行协同计算的部分卸载MEC模型(D2D Collaborative MEC for Partial Offloading,DCM-PO)。在该模型中,除本地计算和MEC服务器计算外,还能将部分任务上传到空闲D2D设备进行辅助计算。首先,以最小化边缘网络的时延、能耗和费用为目标建立多目标优化问题。然后,在多染色体混合编码、自适应交叉率和变异率等方面对基于参考点的非支配排序遗传算法(Non-dominated Sorting Genetic AlgorithmⅢ,NSGA-Ⅲ)进行改进,使之适合DCM-PO模型中的多目标优化问题求解。最后,仿真结果表明,相比基准MEC模型,DCM-PO模型在多项性能指标上有明显优势。

Process,Material,and Regulatory Considerations for 3D Printed Medical Devices and Tissue Constructs

Three-dimensional(3D)printing is a highly automated platform that facilitates material deposition in a layer-by-layer approach to fabricate pre-defined 3D complex structures on demand.It is a highly promising technique for the fabrication of personalized medical devices or even patient-specific tissue constructs.Each type of 3D printing technique has its unique advantages and limitations,and the selection of a suitable 3D printing technique is highly dependent on its intended application.In this review paper,we present and highlight some of the critical processes(printing parameters,build orientation,build location,and support structures),material(batch-to-batch consistency,recycling,protein adsorption,biocompatibility,and degradation properties),and regulatory considerations(sterility and mechanical properties)for 3D printing of personalized medical devices.The goal of this review paper is to provide the readers with a good understanding of the various key considerations(process,material,and regulatory)in 3D printing,which are critical for the fabrication of improved patient-specific 3D printed medical devices and tissue constructs.

Device Discovery in D2D Communication: Scenarios and Challenges

Device to Device (D2D) communication is expected to be anessential part of 5G cellular networks. D2D communication enables closeproximitydevices to establish a direct communication session. D2D communicationoffers many advantages, such as reduced latency, high data rates,range extension, and cellular offloading. The first step to establishing a D2Dsession is device discovery;an efficient device discovery will lead to efficientD2D communication. D2D device further needs to manage its mode of communication,perform resource allocation, manage its interference and mostimportantly control its power to improve the battery life of the device. Thiswork has developed six distinct scenarios in which D2D communication canbe initiated, considering their merits, demerits, limitations, and optimizationparameters. D2D communication procedures for the considered scenarioshave been formulated, based upon the signal flow, containing device discovery,resource allocation, and session teardown. Finally, latency for each scenariohas been evaluated, based on propagation and processing delays.

无人机辅助D2D通信网络安全通信资源分配算法

针对被动窃听下的无人机辅助D2D通信网络中能量受限、频谱短缺、同频干扰严重等问题,考虑每个用户的保密速率和发射功率约束,引入网络保密能效的概念。基于物理层安全传输理论,以最大化网络保密能效为目标,研究频谱复用和功率控制策略的联合优化问题。该问题是非线性非凸优化问题,提出了一种结合Dinkelbach算法和凸近似算法的资源分配算法,通过凸近似算法将非凸优化问题转化为几何规划问题,并结合Dinkelbach算法保证原问题收敛到全局最优解。仿真结果表明,所提算法可有效缓和同频干扰,提高网络保密能效,平衡网络安全通信性能和功耗之间的关系。

多用户D2D计算卸载与资源分配算法

基于边缘服务器架构的移动计算卸载场景避免了核心网链路的网络延迟,在一定程度上提高了计算任务的处理效率,然而在移动用户密集分布的场景下,该计算卸载架构可能面临较高的蜂窝网络接入延迟。为提高移动边缘计算的用户体验,本文在移动用户密集分布的场景下研究了基于D2D通信的多用户计算卸载问题。根据是否具有任务处理需求将用户分为两类,定义拥有密集型计算任务需要处理的用户为需求用户,无任务需要处理且能够提供计算资源的用户为空闲用户。需求用户可以将任务以整体的形式卸载至空闲用户进行辅助处理。联合考虑需求用户与空闲用户在计算卸载中的收益,分别构建了各方的效用函数并形成了相应的优化问题。为了提高各方用户的效用,本文提出了基于Stackelberg博弈与遗传算法相结合的计算卸载与资源分配算法,该算法为两层结构,内层基于Stackelberg博弈对需求用户计算资源租赁单价决策以及空闲用户计算资源分配决策进行优化,并证明了各方用户的策略存在唯一纳什均衡。外层采用遗传算法对需求用户的任务卸载决策进行优化,根据内层算法的反馈结果,可以求解出能够有效提高需求用户效用的任务卸载决策。为验证算法的性能,本文进行了大量的实验仿真工作,仿真结果表明,与其他方案相比,本文算法能够有效提高需求用户与空闲用户的效用。同时,本文还对不同参数对算法有效性的影响进行了仿真分析。

2D multifunctional devices:from material preparation to device fabrication and neuromorphic applications

Neuromorphic computing systems,which mimic the operation of neurons and synapses in the human brain,are seen as an appealing next-generation computing method due to their strong and efficient computing abilities.Two-dimensional (2D) materials with dangling bond-free surfaces and atomic-level thicknesses have emerged as promising candidates for neuromorphic computing hardware.As a result,2D neuromorphic devices may provide an ideal platform for developing multifunctional neuromorphic applications.Here,we review the recent neuromorphic devices based on 2D material and their multifunctional applications.The synthesis and next micro–nano fabrication methods of 2D materials and their heterostructures are first introduced.The recent advances of neuromorphic 2D devices are discussed in detail using different operating principles.More importantly,we present a review of emerging multifunctional neuromorphic applications,including neuromorphic visual,auditory,tactile,and nociceptive systems based on 2D devices.In the end,we discuss the problems and methods for 2D neuromorphic device developments in the future.This paper will give insights into designing 2D neuromorphic devices and applying them to the future neuromorphic systems.

SWIPT-D2D通信中基于深度强化学习的资源分配

针对信道状态信息未知SWIPT-D2D(Simultaneous Wireless Information and Power Transfer Device to Device)无线通信网络环境下设备间信号干扰以及设备能量损耗问题,提出通过使用近端策略优化(Proximal Policy Optimization,PPO)算法,在满足蜂窝用户通信质量要求的前提下同时对D2D用户的资源块、发射功率以及功率分割比三部分进行联合优化。仿真结果表明,所提算法相比于其他算法能够为D2D用户制定更好的资源分配方案,在保证蜂窝用户保持较高通信速率的同时使D2D用户获得更高的能效。同时,当环境中用户数量增加时,所提算法相比于Dueling Double DQN(Deep Q-Network)以及DQN算法,D2D能效分别平均提高了15.95%和23.59%,当通信网络规模变大时所提算法具有更强的鲁棒性。

基于深度强化学习的蜂窝网络中D2D通信资源分配

设备到设备(Device to Device,D2D)通信可以提升频谱利用率和系统吞吐量,但由于D2D通信存在干扰问题,资源分配难度较大。近年来,深度强化学习(Deep Reinforcement Learning,DRL)被广泛应用于蜂窝通信的资源分配。因此,提出了一种基于优势演员-评论员(Advantage Actor-Critic,A2C)的资源分配算法,该算法可以根据环境状态选择最佳的D2D资源分配策略。通过仿真实验验证了该算法在网络性能上的优越性,并与其他算法进行了对比,结果表明,所提算法在提高系统吞吐率方面效果最好。因此,该算法为蜂窝网络中D2D通信资源分配问题提供了一种新的解决方案,具有广泛的应用前景。

基于干扰辅助MMSE检测的去蜂窝大规模MIMO中D2D通信干扰协调算法

针对D2D通信复用去蜂窝大规模MIMO网络资源所带来的干扰问题,研究了与D2D通信共存的去蜂窝大规模多输入多输出系统的联合导频分配和功率优化问题。首先,为了抑制D2D用户(DUEs)对去蜂窝用户(CFUEs)的干扰,提出了一种干扰辅助最小均方误差(MMSE)检测器,并推导了DUE和基于干扰辅助MMSE检测的CFUEs在不完美信道状态条件下的上行链路闭合频谱效率表达式。其次,为了减少导频污染,针对导频分配问题,提出了以最小化系统归一化信道估计误差(NMSE)之和为优化目标的导频复用方式给DUEs分配导频,并结合基于禁忌搜索的粒子群算法给CFUEs分配导频的方案。针对数据功率优化问题,提出了在保证DUEs服务质量下最大化CFUEs中最小频谱效率的方案,并采用二分法进行求解。仿真结果表明,所提出的基于干扰辅助MMSE检测的联合导频分配和功率控制方案有效提高了系统总和频谱效率,且导频开销不会随着D2D数量增加而增加。

Light-based 3D printing of stimulus-responsive hydrogels forminiature devices:recent progress and perspective

Miniature devices comprising stimulus-responsive hydrogels with high environmental adaptability are now considered competitive candidates in the fields of biomedicine,precise sensors,and tunable optics.Reliable and advanced fabricationmethods are critical formaximizing the application capabilities ofminiature devices.Light-based three-dimensional(3D)printing technology offers the advantages of a wide range of applicable materials,high processing accuracy,and strong 3D fabrication capability,which is suitable for the development of miniature devices with various functions.This paper summarizes and highlights the recent advances in light-based 3D-printed miniaturized devices,with a focus on the latest breakthroughs in lightbased fabrication technologies,smart stimulus-responsive hydrogels,and tunable miniature devices for the fields of miniature cargo manipulation,targeted drug and cell delivery,active scaffolds,environmental sensing,and optical imaging.Finally,the challenges in the transition of tunable miniaturized devices from the laboratory to practical engineering applications are presented.Future opportunities that will promote the development of tunable microdevices are elaborated,contributing to their improved understanding of these miniature devices and further realizing their practical applications in various fields.

Resource allocation for D2D-assisted haptic communications

Haptic communications is recognized as a promising enabler of extensive services by enabling real-time haptic control and feedback in remote environments,e.g.,teleoperation and autonomous driving.Considering the strict transmission requirements on reliability and latency,Device-to-Device(D2D)communications is introduced to assist haptic communications.In particular,the teleoperators with poor channel quality are assisted by auxiliaries,and each auxiliary and its corresponding teleoperator constitute a D2D pair.However,the haptic interaction and the scarcity of radio resources pose severe challenges to the resource allocation,especially facing the sporadic packet arrivals.First,the contentionbased access scheme is applied to achieve low-latency transmission,where the resource scheduling latency is omitted and users can directly access available resources.In this context,we derive the reliability index of D2D pairs under the contention-based access scheme,i.e.,closed-loop packet error probability.Then,the reliability performance is guaranteed by bidirectional power control,which aims to minimize the sum packet error probability of all D2D pairs.Potential game theory is introduced to solve the problem with low complexity.Accordingly,a distributed power control algorithm based on synchronous log-linear learning is proposed to converge to the optimal Nash Equilibrium.Experimental results demonstrate the superiority of the proposed learning algorithm.

基于D2D技术的V2V中断模式的信息传输机制

针对车联网中节点高速移动性特点,以及信息传递低时延与可靠性高的需求,多网络融合已成为部署车联网架构的必然趋势。由于车联网的信息传递往往只需在一定范围内的车辆之间发生,将D2D技术的通信模式引入车联网中进行消息传输,在车辆的覆盖范围内联合完全自组网与混合车联网两种通信模式,提出了一种中断模式的信息传输机制,只有当车辆的行驶状态发生改变时才向其他车辆发送,其他时间只需一定时间间隔向其他车辆发送一个确认信息,以此来判断车辆是否仍处于保护圈内或链路是否通畅。仿真数据显示:该中断传输机制可以有效地提高互传信息的车辆数目,节省了信令开销从而避免网络堵塞,也提高了覆盖圈内所有节点的信道容量。

基于增强学习的D2D用户和蜂窝用户传输功率的联合优化

针对D2D用户与蜂窝用户间的干扰问题,提出基于深度增强学习的传输功率优化算法(DTPO)。DTPO算法通过调整传输设备的传输功率,缓解干扰。先将功率分配问题构建成基于线性约束的联合优化问题,再利用深度增强算法求解,获取D2D用户和蜂窝用户的传输功率,进而最大化和速率。仿真结果表明,DTPO算法的性能逼近于穷尽搜索算法的性能。

Graph neural network-based scheduling for multi-UAV-enabled communications in D2D networks

In this paper,we jointly design the power control and position dispatch for Multi-Unmanned Aerial Vehicle(UAV)-enabled communication in Device-to-Device(D2D)networks.Our objective is to maximize the total transmission rate of Downlink Users(DUs).Meanwhile,the Quality of Service(QoS)of all D2D users must be satisfied.We comprehensively considered the interference among D2D communications and downlink transmissions.The original problem is strongly non-convex,which requires high computational complexity for traditional optimization methods.And to make matters worse,the results are not necessarily globally optimal.In this paper,we propose a novel Graph Neural Networks(GNN)based approach that can map the considered system into a specific graph structure and achieve the optimal solution in a low complexity manner.Particularly,we first construct a GNN-based model for the proposed network,in which the transmission links and interference links are formulated as vertexes and edges,respectively.Then,by taking the channel state information and the coordinates of ground users as the inputs,as well as the location of UAVs and the transmission power of all transmitters as outputs,we obtain the mapping from inputs to outputs through training the parameters of GNN.Simulation results verified that the way to maximize the total transmission rate of DUs can be extracted effectively via the training on samples.Moreover,it also shows that the performance of proposed GNN-based method is better than that of traditional means.