Energy-Efficient Resource Allocation for Heterogeneous Network with Grouping D2D

In this paper,a new communication model is built named grouping D2D(GD2D).Different from the traditional D2D coordination,we proposed GD2D communication in licensed and unlicensed spectrum simultaneously.We formulate a resource allocation problem,which aims at maximizing the energy efficiency(EE)of the system while guaranteeing the quality-of-service(Qos)of users.To efficiently solve this problem,the non-convex optimization problem is first transformed into a convex optimization problem.By transforming the fractional-form problem into an equivalent subtractive-form problem,an iterative power allocation algorithm is proposed to maximize the system EE.Moreover,the optimal closedform power allocation expressions are derived by the Lagrangian approach.Simulation results show that our algorithm achieves higher EE performance than the traditional D2D communication scheme.

Grouping pilot allocation scheme based on matching algorithm in massive MIMO system

Pilot allocation is one of the effective means to reduce pilot pollution in massive multiple-input multiple-output(MIMO)systems.The goal of this paper is to improve the uplink achievable sum rates of strong users,and ensure the quality of service(QoS)requirements of weak users at the same time,so that the sum rates of system can be improved.Combining with the technical advantage of pilot grouping,a low complexity pilot allocation scheme based on matching algorithm is proposed,which divides the users in the target cell into weak user group and strong user group,and adopts the minimum-maximum matching method to allocate pilots in weak user group.Through the introduction of Hungarian algorithm,a pilot allocation method is designed to ensure the fairness of the strong users.The simulation results show that,compared with the smart pilot allocation scheme,the pilot allocation scheme based on Hungarian algorithm,the pilot allocation scheme based on user grouping and the random pilot allocation scheme,the system performance of the proposed scheme has been effectively improved.

Implementation of an open-source customizable minimization program for allocation of patients to parallel groups in clinical trials

Current minimization programs do not permit full control over different aspects of minimization algorithm such as distance or probability measures and may not allow for unequal allocation ratios. This article describes the implementation of “MinimPy” an open-source minimization program in Python programming language, which provides full customizetion of minimization features. MinimPy supports naive and biased coin minimization together with various new and classic distance measures. Data syncing is provided to facilitate minimization of multicenter trial over the network. MinimPy can easily be modified to fit special needs of clinical trials and in particular change it to a pure web application, though it currently supports network syncing of data in multi-center trials using network repositories.

An Iterative Power Allocation Algorithm for Group-wise Space-Time Block Coding Systems

An iterative transmit power allocation (PA) algorithm was proposed for group-wise space-time block coding (G-STBC) systems with group-wise successive interference cancellation (GSIC) receivers. Group-wise interference suppression (GIS) filters are employed to separate each group's transmit signals from other interferences and noise. While the total power on all transmit symbols is constrained, all transmit PA coefficients are updated jointly according to the channel information at each iteration. Through PA, each detection symbol has the same post-detection signal to interference-and-noise ratio (SINR). The simulation results verify that the proposed PA algorithm converges at the equilibrium quickly after few iterations, and it achieves much lower bit error rates than the previous single symbol SIC PA and the fixed ratio PA algorithms for G-STBC systems with GSIC receivers .

Joint resource allocation for hybrid NOMA-assisted MEC in 6G networks

Multi-access Edge Computing(MEC)is an essential technology for expanding computing power of mobile devices,which can combine the Non-Orthogonal Multiple Access(NOMA)in the power domain to multiplex signals to improve spectral efficiency.We study the integration of the MEC with the NOMA to improve the computation service for the Beyond Fifth-Generation(B5G)and the Sixth-Generation(6G)wireless networks.This paper aims to minimize the energy consumption of a hybrid NOMA-assisted MEC system.In a hybrid NOMA system,a user can offload its task during a time slot shared with another user by the NOMA,and then upload the remaining data during an exclusive time duration served by Orthogonal Multiple Access(OMA).The original energy minimization problem is non-convex.To efficiently solve it,we first assume that the user grouping is given,and focuses on the one group case.Then,a multilevel programming method is proposed to solve the non-convex problem by decomposing it into three subproblems,i.e.,power allocation,time slot scheduling,and offloading task assignment,which are solved optimally by carefully studying their convexity and monotonicity.The derived solution is optimal to the original problem by substituting the closed expressions obtained from those decomposed subproblems.Furthermore,we investigate the multi-user case,in which a close-to-optimal algorithm with lowcomplexity is proposed to form users into different groups with unique time slots.The simulation results verify the superior performance of the proposed scheme compared with some benchmarks,such as OMA and pure NOMA.

Resource allocation for sum-rate maximization in NOMA-based generalized spatial modulation

The Multiple-Input Multiple-Output(MIMO)Non-Orthogonal Multiple Access(NOMA)based on Spatial Modulation(SM-MIMO-NOMA)system has been proposed to achieve better spectral efficiency with reduced radio frequency chains comparing to the traditional MIMO-NOMA system.To improve the performance of SM-MIMO-NOMA systems,we extend them to generalized spatial modulation scenarios while maintaining moderate complexity and fairness.In this paper,system spectral efficiency and transmission quality improvements are proposed by investigating a sum-rate maximization resource allocation problem that is subject to the total transmitted power,user grouping,and resource block constraints.To solve this non-convex and difficult problem,a graph-based user grouping strategy is proposed initially to maximize the mutual gains of intragroup users.An auxiliary-variable approach is then adopted to transform the power allocation subproblem into a convex one.Simulation results demonstrate that the proposed algorithm has better performance in terms of bit error rate and sum rates.

A pilot allocation method for multi-cell multi-user massive MIMO system

Pilot contamination can spoil the accuracy of channel estimation and then has become one of the key problems influencing the performance of massive multiple input multiple output(MIMO)systems.This paper proposes a method based on cell classification and users grouping to mitigate the pilot contamination in multi-cell massive MIMO systems and improve the spectral efficiency.The pilots of the terminals are allocated onebit orthogonal identifier to diminish the cell categories by the operation of exclusive OR(XOR).At the same time,the users are divided into edge user groups and central user groups according to the large-scale fading coefficients by the clustering algorithm,and different pilot sequences are assigned to different groups.The simulation results show that the proposed method can effectively improve the spectral efficiency of multi-cell massive MIMO systems.

Resource Allocation Algorithm Based on PSO-GA for Multi-User OFDM System

In order to minimize the transmitted power in the multi-user orthogonal frequency division multiplexing（OFDM） system, a scheme combining the improved particle swarm optimization（POS） algorithm with genetic algorithm（GA） is proposed to optimize the sub-carriers and bits allocation. In the algorithm, a random velocity between the maximum and minimum particle velocity is used as the updating velocity instead of maximum or minimum velocity when the updated particle velocity is higher than the maximum particle velocity or lower than the minimum particle velocity. Then, the convergence population is used as the initial population of the genetic algorithm to optimize the subcarriers and bits allocation further. Simulation results show that the transmitted power of the proposed algorithm is about 2 d B to 10 d B lower than that of the genetic algorithm, particle swarm optimization algorithm, and Zhang＇s algorithm.

THREE-DIMENSIONAL VARIABLES ALLOCATION IN MESOSCALE MODELS

Forecasts and simulations are varied owing to different allocation of 3-dimensional variables in mesoscale models. No attempts have been made to address the issue of optimizing the simulation with a 3-dimensional variables distribution that should come to be. On the basis of linear nonhydrostatic anelastic equations, the paper hereby compares, mainly graphically, the computational dispersion with analytical solutions for four kinds of 3-dimensional meshes commonly found in mesoscale models, in terms of frequency, horizontal and vertical group velocities. The result indicates that the 3-D mesh C/CP has the best computational dispersion, followed by Z/LZ and Z/LY, with the C/L having the worst performance. It is then known that the C/CP mesh is the most desirable allocation in the design of nonhydrostatic baroclinic models. The mesh has, however, larger errors when dealing with shorter horizontal wavelengths. For the simulation of smaller horizontal scales, the horizontal grid intervals have to be shortened to reduce the errors. Additionally, in view of the dominant use of C/CP mesh in finite-difference models, it should be used in conjunction with the Z/LZ or Z/LY mesh if variables are allocated in spectral models.

A joint optimization scheme of resource allocation in downlink NOMA with statistical channel state information

In a real communication scenario,it is very difficult to obtain the real-time channel state infor-mation(CSI)accurately,so the non-orthogonal multiple access(NOMA)system with statistical CSI has been researched.Aiming at the problem that the maximization of system sum rate cannot be solved directly,a step-by-step resource allocation optimization scheme based on machine learning is proposed.First,in order to achieve a trade-off between the system sum rate and user fairness,the system throughput formula is derived.Then,according to the combinatorial characteristics of the system throughput maximization problem,the original optimization problem is divided into two sub-problems,that are power allocation and user grouping.Finally,genetic algorithm is introduced to solve the sub-problem of power allocation,and hungarian algorithm is introduced to solve the sub-problem of user grouping.By comparing the ergodic data rate of NOMA users with statistical CSI and perfect CSI,the effectiveness of the statistical CSI sorting is verified.Compared with the orthogonal multiple access(OMA)scheme,the NOMA scheme with the fixed user grouping scheme and the random user grouping scheme,the system throughput performance of the proposed scheme is signifi-cantly improved.

无人机辅助MEC系统中基于最优SIC顺序的能耗优化方案

在基于上行非正交多址接入(NOMA)的无人机(UAV)辅助移动边缘计算(MEC)系统中,NOMA的连续干扰消除(SIC)顺序已成为限制上行任务卸载链路传输性能的瓶颈,为降低系统能耗,对SIC顺序进行了讨论,提出了联合信道增益与任务时延约束的最优SIC顺序。在满足设备给定任务时延、设备最大发射功率约束以及UAV轨迹的约束下,基于最优SIC顺序提出了最小化系统能耗的问题。由于该问题是个复杂的非凸问题,采取交替优化的方法求解该优化问题,以实现功率分配和UAV轨迹的优化;利用匹配理论,提出了低复杂度算法来得到不同时隙的最优设备分组。仿真结果表明,与其他SIC顺序相比,最优SIC顺序能够在相同的任务时延约束下实现更小的系统能耗;所提的低复杂度设备分组算法能够得到最优设备分组。

多无人机辅助NOMA网络的联合无线资源分配算法

为了满足高容量需求和通信质量,提出了一种多无人机(Unmanned Aerial Vehicle, UAV)辅助下行非正交多址(Non-Orthogonal Multiple Access, NOMA)网络的联合定位、用户调度、用户配对和功率分配优化算法。在基站位置固定的网络场景下,推导出通用最优用户配对和功率分配策略闭式解。针对多小区场景,将无人机的位置优化问题转化为一个局部合作博弈问题,并证明所提博弈是一个精确的潜在博弈,它至少有一个纯策略纳什均衡(Pure Strategy Nash Equilibrium, PNE),且最优的纳什均衡(Nash Equilibrium, NE)能够最大化网络和速率。设计了一种集中分布式学习算法寻找最优PNE。仿真结果表明,该算法优于现有方案,显著提高了网络效用。

面向用户多样化业务需求的多波束卫星系统动态资源分配算法

多波束卫星通信系统由于其高吞吐量和高资源利用率而受到广泛关注.已有研究主要考虑多波束卫星通信系统的信道或功率分配问题,但较少考虑用户分组和动态资源分配策略的联合优化设计,导致系统性能受限.此外,现有研究往往假设固定的波束覆盖半径,忽略了波束覆盖半径可变性对波束覆盖性能提升的影响.本文研究了多波束卫星通信系统中的用户分组和资源分配问题,提出了一种两阶段资源管理方案.针对动态和多样化的用户服务需求,首先设计一种基于Voronoi图的迭代用户分组算法以实现分组之间的负载均衡,然后将子信道和功率分配问题建模为系统平均效用函数最大化问题.为解决该问题,将每个波束视为一个智能体,采用一种基于多智能体深度Q网络(Deep Q Network,DQN)的算法来确定子信道和功率分配策略.仿真结果表明,与K-均值用户分组方案相比,本文所提出的基于Voronoi图的迭代用户分组算法对应的用户组负载差异值可降低49.2%,体现了本文所提算法在实现用户组间负载均衡方面的优势.此外,本文所提两阶段资源管理方案与现有文献中所提算法相比,系统所提供容量与用户需求差值可降低83.43%,体现了本文所提算法在实现系统资源高效利用及用户服务需求保障方面的性能优势.

考虑碳排放的地下洞室群施工资源动态均衡优化

在中国“双碳”目标的背景下,持续推进地下洞室群等大型工程施工快速向低碳、绿色方向转型是当前面临的焦点问题。为了保证地下洞室群施工资源合理配置,实现绿色低碳施工,提出了一种衡量施工期碳排放均匀程度的指标“碳熵”,结合“资源熵”作为资源均衡的度量,建立了考虑碳排放和资源配套施工的资源均衡优化模型,并设计了一种内点算法进行求解;在此基础上构建了考虑施工进度延误条件下的资源动态优化流程,以确保发生风险事件后施工资源配置能得到及时优化与调整。乌东德水电站工程实例应用表明:该模型准确有效,可以提高资源总体均衡程度,降低施工期的资源投入峰值和碳排放峰值,从而提高资源配置效率。

高效低碰撞的太赫兹无线定向MAC协议

针对网络中节点在争用访问时期发送消息碰撞、时隙分配机制低效和空闲时隙浪费等问题,提出了一种高效低碰撞的太赫兹无线定向MAC协议.文中提出了3种新机制,分别是基于缓存权重的分组机制、复用竞争机制以及时隙分配机制.首先,在微网控制器(Piconet Coordinator,PNC)中生成缓存映射表记录节点的缓存情况;然后,根据缓存映射表为节点分组,节点在分组对应的时隙中进行时隙请求,PNC根据节点缓存为节点分配时隙;最后,未申请到时隙的节点利用剩余时隙进行复用竞争操作.仿真结果表明,所提出的方案有效地提高了网络的吞吐量,减少了消息碰撞率并降低了平均时延.

无人化仓库下异构机器人混合任务分配研究

在具有若干个障碍区的码头无人化仓库中,为解决异构机器人系统对混合任务的有效分配问题,文中提出一种模糊聚类融合的扩展一致性束算法。在采用模糊聚类划分任务子区域和利用时间窗设置混合任务执行顺序的基础上,提出机器人分组资源配置策略,以任务分配收益最大、运行时间最短为目标,建立异构机器人系统任务分配的数学模型,通过扩展一致性束算法,在各个子区域内完成任务分配。结果表明:扩展一致性束算法缩短了任务分配时间,得到各子区域内机器人的执行任务序列并且每架机器人所分配到的任务数保持均衡。

具有无线携能技术的MIMO-NOMA系统

用户干扰降低了毫米波大规模多输入多输出(MIMO)非正交多址接入(NOMA)系统的频谱效率和能量效率,针对这一问题,提出将无线携能技术(SWIPT)引入基于混合预编码(HP)的MIMO-NOMA系统,并设计了用户分组、混合预编码以及功率分配方案。首先,提出了簇头选择算法,以设计模拟预编码,基于等效信道相关性进行用户分组,并最大化等效信道增益设计数字预编码;其次,为追求用户间的公平性,提出一种基于二分的功率分配方案,以实现最小可达速率最大化。理论分析和仿真结果表明,信噪比(SNR)为5dB时,相较于现有的具有无限携能的正交多址方案,所提方案在全连接架构下频谱效率提升约24.8%,在子连接架构下能量效率提升约24.6%。

基于DRGs的外科病区护理人力资源模型的构建与验证

目的探索DRGs权重与护理工作量的关系,利用DRGs相关指标构建外科病区护理人力资源配置模型。方法采用实地调研收集患者住院期间的总护理工作量及DRGs相关信息进行多元回归分析形成基于DRGs的外科病区护理人力资源配置模型。结果共收集450例患者(建模组)住院期间的护理工作量,年龄、入院自理能力评分、入院护理级别、麻醉方式以及DRGs权重是护理时间的影响因素,能够解释55.52%的变量。继续收集184例患者(验证组)信息带入护理人力资源配置模型,两组总工时比较差异无统计学意义(P>0.05)。结论形成的基于DRGs的外科病区护理人力资源配置模型较为科学、客观,能够将代表病种资源消耗与服务能力的DRGs权重与护理工作量进行联系,可为今后科学配置护理人力提供依据。

变通讯条件下弹群火力分配方法

多导弹-多目标的空地战斗中,通过最优火力分配实现打击毁伤最大化,是取得战斗胜利的重要因素。传统火力分配方法默认弹载通讯条件稳定且不受干扰,将火力分配问题转化为最优求解问题。但实际战斗中,弹群受到来自敌方的固定及机动电子干扰,通讯条件受到极大挑战,考虑攻击前的通讯变化成为智能弹群发展的趋势。针对变通讯条件下的弹群火力分配问题,提出一种基于知识引导和遗传算法相融合的火力分配方法,实现变通讯条件下最优火力分配方案的稳定求解,仿真实验结果表明,该算法可以有效解决变通信条件下弹群针对目标的火力分配问题,为多导弹-多目标空地作战的火力分配决策提供支持。

多波束低轨卫星用户分组与资源分配算法

与地面网络设施相比,卫星的机载资源十分有限,面对用户地理位置及流量需求分布的不均匀,传统的固定分组和固定资源分配方式缺乏灵活性,限制了系统容量。基于地理位置的用户分组,利用相控阵天线产生的具有灵活方向和波束宽度的动态波束和跳波的时间分片技术,可以实现对卫星资源的合理分配和高效利用。在低轨多波束卫星场景下,利用P中心问题模拟用户分组问题算法对卫星用户进行分组,实现用户平均信息速率的最大化。基于分组结果提出一套适用于用户分组方案的灵活资源分配算法,并在最后加入一个组间资源调配的过程,以避免空闲资源的浪费。经过实验数据模拟和性能评估,所提算法比传统的用户分组和资源分配算法更能够有效提升系统吞吐量,降低用户平均排队时延。