Energy-Efficient Resource Allocation for Small-Cell Networks：A Stable Queue Perspective

The small-cell technology is promising for spectral-efficiency enhancement. However, it usually requires a huge amount of energy consumption. In this paper, queue state information and channel state information are jointly utilized to minimize the time average of overall energy consumption for a multi-carrier small-cell network, where the inter-cell interference is an intractable problem. Based on the Lyapunov optimization theory, the problem could be solved by dynamically optimizing the problem of user assignment, carrier allocation and power allocation in each time slot. As the optimization problem is NP-hard, we propose a heuristic iteration algorithm to solve it. Numerical results verify that the heuristic algorithm offers an approximate performance as the brute-force algorithm. Moreover, it could bring down the overall energy consumption to different degrees according to the variation of traffic load. Meanwhile, it could achieve the same sum rate as the algorithm which focuses on maximizing system sum rate.

Resource allocation for cooperative diversity systems based on quadrature modulation

To improve the error performance and the resource utilization of cooperative systems, the optimum resource allocation, i.e., power allocation and partner choice, for an adaptive decode-and-forward （DF） cooperative diversity system based on quadrature modulation is investigated. The closed-form expression of the bit error rate （BER） system performance is derived and an optimal power allocation （OPA） algorithm is proposed to optimize the power allocation between the local and relayed signals under the minimum BER criterion. Based on the OPA algorithm, a partner choice strategy is proposed to determine the partner locations specified by various cooperation gains. Simulation results show that the proposed resource optimization algorithms are superior to the unoptimized algorithms by significantly reducing the BER and improving the cooperative gain, which is useful to simplify the practical partner choice process.

Adaptive Resource Allocation Algorithm for Internet of Things with Bandwidth Constraint

In order to improve the transmission accuracy and efficiency of sensing and actuating signals in Internet of Things （loT） and ensure the system stability, an adaptive resource allocation algorithm is proposed, which dynami- cally assigns the network bandwidth and priority among components according to their signals＇ frequency domain characteristics. A remote sensed and controlled unmanned ground vehicle （UGV） path tracking test-bed was devel- oped and multiple UGV＇s tracking error signals were measured in the simulation for performance evaluation. Results show that with the same network bandwidth constraints, the proposed algorithm can reduce,, the accumulated and maximum errors of UGV path tracking by over 60% compared with the conventional static algorithm.

Secure Resource Allocation in Device-to-Device Communications Underlaying Cellular Networks

With increasing the demand for transmitting secure information in wireless networks,deviceto-device(D2D)communication has great potential to improve system performance.As a well-known security risk is eavesdropping in D2D communication,ensuring information security is quite challenging.In this paper,we first obtain the closed-forms of the secrecy outage probability(SOP)and the secrecy ergodic capacity(SEC)for direct and decodeand-forward(DF)relay modes.Numerical results are presented to verify the theoretical results,and these results show the cases that the DF relay mode improves security performance compared to the direct mode at long distances between the transmitter and receiver nodes.Further,we look into the optimization problems of secure resource allocation in D2D communication to maximize the SEC and to minimize the SOP by considering the strictly positive secrecy capacity constraint as a mixed-integer non-linear programming(MINLP)problem.In the continue,we convert the MINLP to convex optimization.Finally,we solve this program with a dual method and obtain an optimal solution in the direct and DF relay modes.

Comprehensive Utility Function for Resource Allocation in Mobile Edge Computing

In mobile edge computing(MEC),one of the important challenges is how much resources of which mobile edge server(MES)should be allocated to which user equipment(UE).The existing resource allocation schemes only consider CPU as the requested resource and assume utility for MESs as either a random variable or dependent on the requested CPU only.This paper presents a novel comprehensive utility function for resource allocation in MEC.The utility function considers the heterogeneous nature of applications that a UE offloads to MES.The proposed utility function considers all important parameters,including CPU,RAM,hard disk space,required time,and distance,to calculate a more realistic utility value for MESs.Moreover,we improve upon some general algorithms,used for resource allocation in MEC and cloud computing,by considering our proposed utility function.We name the improved versions of these resource allocation schemes as comprehensive resource allocation schemes.The UE requests are modeled to represent the amount of resources requested by the UE as well as the time for which the UE has requested these resources.The utility function depends upon the UE requests and the distance between UEs and MES,and serves as a realistic means of comparison between different types of UE requests.Choosing(or selecting)an optimal MES with the optimal amount of resources to be allocated to each UE request is a challenging task.We show that MES resource allocation is sub-optimal if CPU is the only resource considered.By taking into account the other resources,i.e.,RAM,disk space,request time,and distance in the utility function,we demonstrate improvement in the resource allocation algorithms in terms of service rate,utility,and MES energy consumption.

A Resource Allocation Algorithm of Physical-Layer Security for OFDMA System under Non-ideal Condition

In this paper, a resource allocation scheme based on physical layer security under non-ideal condition for OFDMA system is introduced. Firstly, the program uses the information security constructing an OFDMA system Wiretap Channel Model under non-ideal condition. Based on this model, arti?cial noise is generated for secure communications combatting passive multiple eavesdroppers. In order to maximize the average secrecy outage capacity without channel state information of eavesdroppers, we use dual decomposition method to implement subcarriers and power allocation in joint optimization. Simulation results show that the average secrecy outage capacity can achieve 7.81 bit/s/Hz while secrecy outage probability is 0.05 with 50 dB mtransmitpower and 64 sub-carrier for 8 authorized users.

Cooperative multiple access channels: Achievable rates and optimal resource allocation

We investigate the multiple access channels （MAC） where sources can cooperate via half-duplex relaying and refer to it as cooperative MAC channels （CMAC）. Assuming perfect channel state information （CSI） at the transmitters and the receivers, we determine the bounds on the achievable rate region of a Gaussian CMAC channel and an inner bound on the outage capacity region of a fading CMAC channel. Based on superposition modulation, a half-duplex cooperative relay scheme with optimal resource allocation is proposed to achieve the bounds of capacity region. Analytical results and simulation results show that the achievable rate region of a Gaussian CMAC channel is larger than that of a Gaussian MAC channel with direct transmission （DT） schemes. But they have the same achievable sum rate. Moreover, the proposed scheme can provide higher outage capacity region than DT schemes in a fading MAC channel due to the fact that sources can share the resources with each other to reduce outages.

Multicast resource allocation with min-rate requirements in OFDM systems

This article addresses the multicast resource allocation problem with min-rate requirement constraints in orthogonal frequency division multiplexing （OFDM） systems. Due to the prohibitively high complexity for nonlinear and combinatorial optimization, the original problem is relaxed and reformulated to form a standard optimization problem. By theoretical derivation according to the Karush-Kuhn-Tucker （KKT） conditions, two propositions are presented as the necessary criteria for optimality. Furthermore, a two-step resource allocation scheme, including subcarrier assignment and power allocation, is proposed on a basis of the propositions for practical implementation. With the rain-rate based multicast group order, subcarriers are assigned in a greedy fashion to maximize the capacity. When subcarrier assignment is determined, the proposed power allocation can achieve the optimal performance for the rain-rate constrained capacity maximization with an acceptable complexity. Simulation results indicate that the proposed scheme approximates to optimal resource allocation obtained by exhaustive search with a negligible capacity gap, and considerably outperforms equal power distribution. Meanwhile, multicast is remarkably beneficial to resource utilization in OFDM systems.

Resource allocation optimization for SWIPT full-duplex relaying networks with practical energy harvester

In this paper,the resource allocation optimization for the simultaneous wireless information and power transfer(SWIPT)full-duplex(FD)relaying networks is investigated,in which the power-constrained relay scavenges energy from the source signal and assists information transmission by FD operation.Taking into account non-linear energy harvesting(EH)hardware circuit characteristics of the relay,the information rate maximization problem is developed by jointly optimizing the time-switching(TS)factor and transmission powers of the source in two different phases.However,the formulated optimization is highly non-convex and difficult to solve.To cope with this problem,the primary problem is decomposed into two sub-problems with respect to the TS factor and transmission powers.After solving these two sub-problems,the final sub-optimal solutions can be obtained by alternating search.Simulation results prove that the optimization of both TS factor and transmission powers can effectively enhance the information rate for the considered networks.

The allocation between egg size and clutch size depends on local nest survival rate in a mean of bet-hedging in a shorebird

Background:The allocation of resources between offspring size and number is a central question of life-history theory.Although several studies have tested the existence of this trade-off,few studies have investigated how environmental variation influences the allocation of resources to offspring size and offspring number.Additionally,the relationship between population dynamics and the offspring size and number allocation is far less understood.Methods:We investigate whether resource allocation between egg size and clutch size is influenced by the ambient temperature and whether it may be related to apparent nest survival rate.We measured 1548 eggs from 541 nests of two closely related shorebird species,the Kentish Plover(Charadrius alexandrinus)and the White-faced Plover(C.dealbatus)in China,in four populations that exhibit contrasting ambient environments.We weighed females,monitored nest survival,and calculated the variance of ambient temperature.Results:Although we found that egg size and clutch size were all different between the four breeding populations,the reproductive investment(i.e.total clutch volume)was similar between populations.We also found that populations with a high survival rate had relatively larger eggs and a smaller clutch than populations with a low nest survival rate.The latter result is in line with a conservative/diversified bet-hedging strategy.Conclusions:Our findings suggest that plovers may increasing fitness by investing fewer,larger or many,small according local nest survival rate to make a similar investment in reproduction,and thereby may have an impact on population demography.

企业资源配置战略与债券信用评级

企业战略作为企业全局性、长期性资源配置的重要规划,对企业经营活动具有重要影响,也必然会影响企业的融资行为。本文从企业战略视角出发,以2008—2020年中国A股上市公司为研究对象,运用固定效应模型探究了企业资源配置战略对债券信用评级的影响。研究发现:相对于经营主导型企业,投资主导型企业的债券信用评级更低,并且该结论在处理内生性问题和进行一系列稳健性检验后依然成立。异质性分析发现,企业资源配置战略对债券信用评级的影响在非国有企业和分析师跟踪人数多的企业中更明显。中介效应检验结果表明,企业资源配置战略通过作用于企业违约风险而影响债券信用评级。本文的研究结论对发债企业、信用评级机构和债券市场投资者具有重要启示意义。

低出生率背景下省域学前教育资源供需关系与动态调配——基于对中部J省人口预测的分析

低出生率背景下研判省域学前教育资源供需关系的变化,对优化学前教育资源配置、实现学前教育优质均衡发展具有指导意义。本研究选择省域学前教育资源配置中低供给高需求类型的中部J省作为研究对象,运用PADIS-INT软件预测分析了J省2023—2035年低、中、高三种方案下城乡在园幼儿人数的变化,调查了J省学前教育人力资源与物力资源供给现状,进而预测分析了J省学前教育资源在供给动态变化与静态不变情况下的供需关系。结果发现,随着在园幼儿人数减少,学前教育资源需求将明显减少;现有人力资源供给存在结构性短缺,未来会出现城乡配置失衡;物力资源配备已达到高水平,未来会严重过剩。根据研究结果,建议地方政府根据资源供需关系的动态变化做好学前教育长期规划,控增量、调存量、疏余量;从结构与质量入手优化人力资源配置,稳定乡村教师队伍;谨慎增加办园条件投入,采用多种形式提升物力资源使用效益。

汇率变动与出口产品加成率:影响机制和资源配置效应

利用2000—2013年中国工业企业数据和海关进出口数据,基于样本期间人民币汇率持续升值的事实,考察人民币汇率变动对中国出口产品加成率的影响。研究结果显示:人民币实际有效汇率的上升显著提高了企业出口产品加成率,并且该影响效应在异质性产品、竞争型行业、外资企业以及存在进口行为的企业中更大。机制分析表明,人民币升值通过产品质量效应、边际成本效应、价格竞争效应和市场选择效应影响出口产品加成率,其中产品质量升级和进口成本降低造成的影响占据主导,这是人民币升值显著提高出口产品加成率的重要原因。进一步研究显示,人民币升值的市场选择效应降低了产品内企业间的加成率离散度,提升了企业间的资源配置效率,但在市场选择效应和企业内加成率调整的共同影响下,其对企业内的资源配置效率没有显著影响。

有源RIS辅助速率分割多址系统的资源分配方案

针对无源智能反射面(Passive Reconfigurable Intelligent Surface,PRIS)所带来的“双衰落”效应及传统多址接入方案在干扰管理上的缺陷,提出了一种有源智能反射面(Active Reconfigurable Intelligent Surface,ARIS)辅助速率分割多址(Rate Splitting Multiple Access,RSMA)的系统方案。该方案中,构建了一个以最大化用户最小速率为目标的优化问题。该问题是一个多变量耦合的非凸问题,为此提出了一种具有公平性的资源分配算法,在ARIS硬件约束、公共信息速率约束等条件下,将原优化问题拆分为基站波束赋形矢量和速率分割因子的联合优化、ARIS的预编码矩阵优化两个子问题,随后使用逐次凸逼近、半正定松弛和凸差函数对非凸约束进行转换,并利用交替优化方法迭代求解。理论分析和仿真结果表明,在系统总功率为30 dBm的情况下,所提方案的用户最小速率相比传统PRIS辅助RSMA方案提升了18.6%。

数字经济对全要素生产率的影响研究——来自国家级大数据综合试验区的证据

基于国家级大数据综合试验区的准自然实验,运用2014—2019年280个地级市的面板数据,采用多期双重差分法实证检验数字经济对全要素生产率的影响。研究结果表明:数字经济显著提升了全要素生产率,这一结论在经过多种稳健性检验后仍然成立;中介效应检验表明,数字经济通过提高资源配置效率和促进技术创新来提升全要素生产率;异质性分析表明,数字经济对低资源禀赋城市、低数字经济发展水平城市以及智慧城市的全要素生产率具有促进作用,抑制高资源禀赋、高数字经济城市的全要素生产率,而对非智慧城市的作用则不明显。因此,应基于大数据综合试验区深度挖掘数字经济的直接作用与间接作用,同时采取差异化发展战略,培育壮大全要素生产率增长新动能。

人口出生率下降对学前教育格局的影响

随着我国人口出生率不断下降,3~5岁学龄前儿童数量开始逐年减少,学前教育规模整体进入负增长阶段。城市学前教育处在不断增长阶段,农村学前教育已全面进入负增长阶段;公办园需求增长,民办园需求萎缩。学龄前儿童减少缓解学前教育资源配置不足的压力,师资需求呈下降趋势,城乡差距导致普惠性学前教育资源结构短缺,引发学前教育发展模式转变,对学前教育质量保障提出新要求。未来学前教育发展要合理布局配置幼儿园、合理配置师资、缩小城乡差距、改进监督与质量评估标准、拓宽服务对象。

CR-RSMA网络接入控制策略研究

针对多个次用户的速率分割多址接入技术辅助的认知无线电(CR-RSMA,cognitive radio-inspired rate splittingmultipleaccess)网络设计了一种接入控制策略,在主用户实现与正交多址接入(OMA,orthogonal multiple access)相同的中断性能并满足次用户通信服务质量(QoS,quality of service)需求的前提下,让更多的次用户接入主用户频段,并在此基础上最大化次用户吞吐量之和。该策略考虑次用户QoS约束,联合优化次用户发射功率分配因子和接入次用户集合,推导出最优功率分配因子的闭合表达式,进而求得次用户最大通信速率和最小通信时间,在此基础上利用贪婪搜索算法确定最终接入的次用户,最后通过优化时间资源实现所有接入次用户吞吐量之和的最大化。仿真结果表明,该策略相比于非正交多址接入技术辅助的认知无线电(CR-NOMA,cognitive radio-inspired non-orthogonal multiple access)网络在接入用户数和吞吐量方面均表现出更优越的性能。

无人机辅助无线通信网络资源自适应协同分配

为保证边缘用户可正常使用通信网络资源,研究了无人机辅助的无线通信网络资源自适应协同分配技术。构建无人机辅助无线通信网络模型,并设置自适应协同分配的目标函数,从而进行网络资源最大化的资源分配。引入排斥、吸引方法改进果蝇算法,提取最优、最差味道浓度与坐标信息。输出资源的最优传输速率、最优无人机运行轨迹的分配解,改进求解资源分配目标函数,完成通信网络资源自适应协同分配。试验结果表明,不同工况中,无人机能够结合用户分布状态飞行。在用户最小速率需求标准依次是3 bit/s、6 bit/s、9 bit/s时,经过6次迭代,算法便可收敛到纳什均衡点,无线通信任务时延便可控制为最小值1.5 s。该技术能够自适应协同分配无人机辅助无线通信网络资源。协同分配结果满足用户需求。

基于DEABC算法的EH-CRAIoT的资源分配算法研究

针对具有无线能量收集功能的认知农业物联网中上行链路资源分配的优化问题,提出一种基于差分蜂群算法的资源分配算法。首先建立了认知农业物联网资源分配的系统模型,以系统的平均传输速率最大化为优化目标,通过在传统的人工蜂群算法中引入差分算法的更新策略,提升人工蜂群算法的收敛速度和搜索能力;再利用改进后的差分蜂群算法求解得出最优分配方案。仿真实验表明,差分蜂群算法具有良好的寻优效率和收敛性能,通过调整传感器节点的发射功率和感知时隙,能够有效在传感器节点进行频谱感知、信息传输的能量需求的前提下提升系统的平均传输速率。

STAR-RIS辅助多载波通信感知一体化系统的资源优化

针对频谱资源短缺和传统的智能反射面无法实现全空间覆盖的问题,研究了同时透射和反射的可重构智能表面(simultaneously transmitting and reflecting reconfigurable intelligent surface, STAR-RIS)辅助通信感知一体化系统。通过分配功率和子载波资源并优化STAR-RIS系数矩阵,使得在保证感知功能的前提下,最大化通信和速率。设计了一种交替优化算法,将多变量之间相互耦合的非凸问题解耦转化为3个凸的子问题。首先,松弛子载波选择变量后使用惩罚函数和逐次凸逼近算法;然后,直接求解功率分配变量的凸子问题;最后,使用半正定规划并把秩一约束改写为核范数与谱范数之差,将其作为惩罚函数,使用逐次凸逼近以优化STAR-RIS系数矩阵。仿真结果表明,与传统RIS相比,所提方案的性能取得了显著提升。