共查询到18条相似文献,搜索用时 78 毫秒
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大数据分析兴起使得系统可以预测用户的移动轨迹和业务需求等信息,从而可以根据预测信息对资源进行预先分配,在满足用户需求的同时降低网络的资源消耗。相比于无干扰网络,在基站密集部署的网络中,干扰的存在使得用户数据率预测与资源分配耦合,增加了干扰网络中进行预测资源分配的复杂性。本文研究了在保证用户业务需求情况下如何最小化系统资源消耗的问题,提出了一种能够有效协调网络干扰的预测资源分配方法。仿真结果表明,本方法基于可预测的大尺度信道信息进行预测资源分配,能够在相同的用户需求下提高网络成功传输率,降低系统能量资源消耗,提高资源的频谱效率。 相似文献
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在超密集网络中,小区间干扰严重制约了小区边缘用户的性能体验以及网络吞吐量。无线大数据分析的飞速发展,使得人们有可能通过预测未来的信道状态来分配资源,在无干扰网络中可达到很大的性能增益。但是在干扰网络中如何利用预测信息,在分配资源的同时有效协调干扰还是一个尚未研究的问题。本文分析了干扰网络中预测资源分配的设计难点和存在的问题,针对该问题提出了相应的解决方法,将资源分配建模成一个凸优化问题,通过求解优化问题得到最优的资源分配方法。仿真表明,与未知预测信息的最大化网络吞吐量方法相比,所提方法能够有效提高用户的成功传输率、平均传输进度和网络的吞吐量。当用户数据需求较大时,所提方法可以提供较大的网络性能增益。 相似文献
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作为5G的关键技术,超密集组网(UDN)可以大幅度提高网络容量和用户体验,但其性能严重受限于小区间干扰。针对超密集异构蜂窝网络的无线资源高效管理问题,提出了一种多维无线资源联合分配算法。为了在保障用户服务质量的前提下,最大化系统能效,将时频资源与功率资源的最优分配问题建模为有约束的组合优化问题。由于这是一个NP-hard问题,求解比较困难。因此,采用分步优化+迭代搜索策略:首先基于模拟退火算法进行时频资源优化分配,并引入粒子删减和回火升温过程以提高搜索速度和避免落入局部最优陷阱;然后采用拉格朗日乘子法进行功率最优分配;最后,通过多次迭代,逼近全局最优解。仿真结果表明,提出的联合资源分配算法能够保障用户间的公平性并且有效提高系统能效与网络吞吐量,同时具有更快的收敛速度和更高的收敛精确度。 相似文献
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近年来,随着移动互联网的蓬勃发展和智能终端的快速普及,对无缝网络覆盖和高速数据传输要求日趋增强,超密集异构蜂窝无线网络通过减小小区半径,增加低功率节点数量进行超密集组网,提高无线资源利用率和频谱效率,因而引起了国内外研究学者的广泛关注。然而,超密集异构蜂窝无线网络中愈发密集的网络部署也带来了各种严峻的问题,包括超密集异构蜂窝无线网络中的系统干扰问题、无线信息的安全传输问题、以及网络运营的能量供给问题等。为此,本文首先简单介绍了超密集异构蜂窝无线网络架构,接着从干扰管理、物理层安全和能量效率等方面,深入分析了超密集异构蜂窝无线网络中的关键技术和当前挑战,最后,进一步展望了超密集异构蜂窝无线网络的未来发展。 相似文献
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超密集组网是5G的关键技术,通过在单位区域中大量布置基站,提升网络容量,满足用户在各个场景下的多元化需求。然而,基站部署的不规律性和异构网络的不兼容性,使超密集组网中的干扰问题愈加突出,对无线通信质量造成了很大影响,若不加以管理,会适得其反。为此,重点结合干扰协调技术,对如何降低甚至消除干扰进行研究。 相似文献
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在引入休眠机制的超密集异构无线网络中,针对网络动态性增强,导致切换性能下降的问题,该文提出一种基于改进深度Q学习的网络选择算法。首先,根据网络的动态性分析,构建深度Q学习选网模型;其次,将深度Q学习选网模型中线下训练模块的训练样本与权值,通过迁移学习,将其迁移到线上决策模块中;最后,利用迁移的训练样本及权值加速训练神经网络,得到最佳选网策略。实验结果表明,该文算法显著改善了因休眠机制导致的高动态性网络切换性能下降问题,同时降低了传统深度Q学习算法在线上选网过程中的时间复杂度。 相似文献
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Wireless ultra-dense network (UDN) is one of the important technologies to solve the burst of throughput demand in the forthcoming fifth generation (5G) cellular networks. Reusing spectrum resource for the backhaul of small base stations (SBSs) is a hotspot research because of lower cost and rapid implementation with macro base stations (MBSs) in recent years. In heterogeneous UDN, the problem of spectrum allocation for wireless backhaul is investigated. In particular, two different spectrum resource reusing strategies for wireless backhaul are proposed in heterogeneous UDN with the limited bandwidth condition. Using a stochastic geometry-based heterogeneous UDN model, the success probabilities that mobile users communicate with SBSs or MBSs are derived under two different spectrum resource reusing strategies. In addition, the network throughput’s analytical expressions and the optimal ratio of spectrum allocation are derived. Numeral results are provided to evaluate the performance of the proposed strategies at throughput. Thus, the effectiveness of the strategy that mobile users can only communicate with SBSs is validated. 相似文献
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Shang‐Juh Kao Kuo‐Chan Hsiao Fu‐Min Chang 《International Journal of Communication Systems》2019,32(15)
Radio resource management to enable user association and resource block (RB) allocation is crucial for enhancing the performance of heterogeneous networks (HetNets), which are required for fifth generation (5G) mobile networks. This paper proposed a resource allocation technique based on a genetic algorithm (GA) for use in HetNets. We aimed to optimize user association and RB allocation simultaneously to fulfill multiple objectives, such as throughput and fairness measure. In addition to the four primary phases used in GA process, namely initialization, crossover, mutation, and selection, a further operator was provided for managing illegal offspring generated during a GA process. We performed a simulation to compare the proposed GA‐based approach with best channel quality indicator (CQI) algorithm and integer linear programming (ILP) approach in terms of total throughput and fairness measure. The simulation results revealed that the total throughput obtained using the proposed approach is 32.7% and 37.6% better than that obtained using the ILP and best CQI approaches, respectively. Moreover, the fairness measure obtained using the proposed GA‐based approach was 31.8% and 33.2% higher than that obtained using ILP and best CQI approaches, respectively. 相似文献
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为了实现超密集网络中的绿色通信,提出一种基于能效最优的资源分配算法。首先,在考虑用户服务质量(quality of service,QoS)需求和干扰容限的情况下,建立最大化网络能效的优化问题。其次,为了降低求解原问题的计算复杂度,采用柯西不等式将原优化问题进行松弛,从而转化为非合作博弈问题。进而,在满足最大最小公平(max-min fairness,MMF)准则的情况下,提出一种分布式能效最优算法(distributed EE maximization algorithm,DEMA)。仿真结果表明,所提算法较传统算法可以更好地兼顾系统的能效和吞吐量性能。 相似文献
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为了提高移动边缘计算(mobile edge computing,MEC)网络的频谱效率,满足大量用户的服务需求,建立了基于非正交多址接入(non-orthogonal multiple access,NOMA)的超密集MEC系统模型。为了解决多个用户同时卸载带来的严重通信干扰等问题,以高效利用边缘服务器资源,提出了一种联合任务卸载和资源分配的优化方案,在满足用户服务质量的前提下最小化系统总能耗。该方案联合考虑了卸载决策、功率控制、计算资源和子信道资源分配。仿真结果表明,与其他卸载方案相比,所提方案可以在满足用户服务质量的前提下有效降低系统能耗。 相似文献
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由于在超密集网络中小基站密集的部署,用户数据量空前增加,对数据速率的要求不断提高,所以在有限的资源下如何高效地将资源分配给用户尤为重要。提出了一种小区分簇算法,根据小区簇的通信业务、通信负载量等条件将各个小区分为不同的优先级,引入二分图,以小区簇的优先级为依据建立频谱资源与小区簇之间的匹配关系,并提出一种低复杂度的贪婪算法。仿真结果表明所提算法能够有效提高系统性能,并且有效完成频谱资源的分配。 相似文献
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为了降低超密集网络中基站管理算法的计算复杂度并提升基站的能源使用效率,根据用户密度、网络负载量等信息,提出了一种基于分簇的动态管理基站算法.该算法首先根据用户测量报告计算出理论最小需求基站数,然后对基站进行合理的网络分簇,最终通过粒子群优化算法确定基站休眠组合.仿真结果表明,与未进行分簇的基站管理算法相比,该算法可以降低约60%的计算复杂度,并能有效降低基站能源消耗. 相似文献
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As a promising technology to improve spectrum efficiency and transmission coverage, Heterogeneous Network (HetNet) has attracted the attention of many scholars in recent years. Additionally, with the introduction of the Non-Orthogonal Multiple Access (NOMA) technology, the NOMA-assisted HetNet cannot only improve the system capacity but also allow more users to utilize the same frequency band resource, which makes the NOMA-assisted HetNet a hot topic. However, traditional resource allocation schemes assume that base stations can exactly estimate direct link gains and cross-tier link gains, which is impractical for practical HetNets due to the impact of channel delays and random perturbation. To further improve energy utilization and system robustness, in this paper, we investigate a robust resource allocation problem to maximize the total Energy Efficiency (EE) of Small-Cell Users (SCUs) in NOMA-assisted HetNets under imperfect channel state information. By considering bounded channel uncertainties, the robust resource optimization problem is formulated as a mixed-integer and nonlinear programming problem under the constraints of the cross-tier interference power of macrocell users, the maximum transmit power of small base station, the Resource Block (RB) assignment, and the quality of service requirement of each SCU. The original problem is converted into an equivalent convex optimization problem by using Dinkelbach's method and the successive convex approximation method. A robust Dinkelbach-based iteration algorithm is designed by jointly optimizing the transmit power and the RB allocation. Simulation results verify that the proposed algorithm has better EE and robustness than the existing algorithms. 相似文献