基于合作中继的OFDM蜂窝网络的QoS感知资源调度算法

针对基于中继的OFDM蜂窝网络,该文考虑具有不同QoS要求的混合业务场景,引入合作传输机制,提出了一种基于合作中继的QoS感知资源调度算法,解决了合作中继节点选取,子载波分配以及功率控制等问题。以最大化系统效用为目标,在考虑QoS业务的速率要求与基站功率约束的同时,针对中继结构引入了中继节点的功率约束。为降低计算复杂度,将原非线性组合优化问题分解为子载波分配与功率控制两个子问题。仿真结果表明,该文所提算法在能量节约、系统效用,吞吐量等性能方面都有显著优势。     

时分波分无源光网络与云无线接入网联合架构中负载平衡的用户关联与资源分配策略

在时分波分无源光网络(TWDM-PON)与云无线接入网(C-RAN)的联合架构中,由于无线域的负载不均衡问题,限制了网络整体的传输效率。为了充分利用TWDM-PON与C-RAN联合架构的网络资源,并保证用户的服务质量(QoS),该文提出一种负载平衡的用户关联与资源分配算法(LBUARA)。首先根据不同用户的服务质量需求以及分布式无线射频头端(RRH)的负载对用户的影响,构建用户收益函数。进而,在保证用户服务质量的前提下,根据网络状态建立随机博弈模型,并基于多智能体Q学习提出负载均衡的用户关联和资源分配算法,从而获得最优的用户关联与资源分配方案。仿真结果表明,所提的用户关联和资源分配策略能够实现网络的负载均衡,保证用户的服务质量,并提高网络吞吐量。     

不完整CSI下OFDMA无线中继系统资源分配算法研究

针对实际OFDMA系统中下行无线链路信道状态信息(CSI)不完善的前提下,提出一种满足不同用户QoS需求的资源分配算法。在该方案中,首先确定用户与中继站之间的CSI,然后在总功率约束条件下,通过在等功率分配降低算法复杂度的基础上进行最佳中继选择和子载波的分配,最后再对子载波进行注水的功率分配来进一步提高系统容量。仿真结果表明,该方案在保证用户QoS的同时进一步提高系统容量。     

时分波分无源光网络与云无线接入网联合架构中负载平衡的用户关联与资源分配策略

在时分波分无源光网络(TWDM-PON)与云无线接入网(C-RAN)的联合架构中,由于无线域的负载不均衡问题,限制了网络整体的传输效率.为了充分利用TWDM-PON与C-RAN联合架构的网络资源,并保证用户的服务质量(QoS),该文提出一种负载平衡的用户关联与资源分配算法(LBUARA).首先根据不同用户的服务质量需求以及分布式无线射频头端(RRH)的负载对用户的影响,构建用户收益函数.进而,在保证用户服务质量的前提下,根据网络状态建立随机博弈模型,并基于多智能体Q学习提出负载均衡的用户关联和资源分配算法,从而获得最优的用户关联与资源分配方案.仿真结果表明,所提的用户关联和资源分配策略能够实现网络的负载均衡,保证用户的服务质量,并提高网络吞吐量.     

基于OFDMA系统联合资源分配方案

针对多用户OFDMA系统,提出一种改进的比例公平资源分配算法。该算法采用分步法联合资源分配方案,分为子载波分配和功率分配两步进行,改进算法基于Wong算法,引入比例限制因子,在子载波分配环节,对剩余子载波的分配采用比例公平和最大化系统容量的算法,在用户中的功率分配环节,采用线性等式,大大降低了算法复杂度。仿真分析表明,改进算法不仅提高了系统吞吐量,而且降低了算法复杂度。     

一种低复杂度基于公平性的MIMO-OFDMA资源分配方案

该文针对MIMO-OFDMA下行链路系统,考虑在总功率和BER 以及用户数据速率成比例的约束下,以获取整个系统吞吐量极大化为准则,提出一种基于成比例公平性约束的资源分配方案。新的方案基于MIMO信道状态信息,利用特征子信道来确定子载波和功率分配,充分利用了空间域,频域以及多用户分集提高系统的频谱效率。在子载波分配时,松弛成比例约束条件,使用户数据速率近似地成比例于每个用户分配的子载波数,推导出一种线性的不需要迭代的低复杂度的功率分配方案。仿真和分析表明,整个方案在保证系统吞吐量的前提下,取得了用户间良好的速率公平性,同时又具有较低的计算复杂度。     

高铁场景下基于缓存的无线云接入预下载机制研究

云接入网（Cloud Radio Access Network,C-RAN）中有限带宽的前程链路无法有效应对高铁场景下乘客大量集中式的网络资源请求.针对这一不足,本文对C-RAN架构中的射频拉远头（Remote Radio Head,RRH）配置缓存设备使其具有存储转发资源的功能,利用列车运行信息可预知的先验条件,针对乘客数据通信提出基于缓存机制的预下载方案以提升网络系统的吞吐量.仿真结果表明,在前程链路拥挤情况下,基于缓存的预下载方案可明显提升资源的平均传输速率和高铁通信网络的吞吐量,进而提升了乘客的在途通信服务质量（Quality of Service,QoS）.     

MIMO中继系统中一种基于用户QoS的资源分配方法

针对基于OFDMA技术的MIMO中继通信系统下行链路中的动态资源分配问题,提出了一种保证最低用户服务质量(QoS)的资源分配方法.该方法采用一种低复杂度的次优算法,在假设等功率分配的前提下,根据用户的QoS定义选择优先性权衡因子,对权衡因子越大的用户越优先进行中继选择和子载波分配,在此基础上通过凸优化理论得到不同用户对应的子载波上的功率分配.仿真结果表明,本文提出的方法不但能够获得较高的系统容量,而且可以较好地保证每个用户的QoS.     

基于HPLC的物联网通信技术研究

针对宽带电力线载波通信资源分配问题,提出一种物理层资源分配算法,满足低复杂度的需求。首先,基于等功率分配方法,确定每个用户满足其最小速率要求所需的子载波集合,资源分配中的多用户问题可以拆分为单用户问题,并满足最优功率分配,然后将拉格朗日乘子法用于优化每个用户使用的子载波集的功率,以进一步提高系统吞吐量。仿真分析表明,所提算法具有更高的吞吐量,比最大吞吐量算法和Gong算法提高了13.52%和5.61%,能够满足用户的最低速率要求。     

基于两跳匹配的OFDMA中继网络联合资源分配算法

针对OFDMA中继网络的两跳特性,提出一种基于两跳匹配的中继网络联合资源分配算法。首先根据中继网络的两跳性建立两跳速率匹配模型,然后利用对偶分解理论将中继网络资源分配的主问题分解为：中继选择、子载波分配和功率分配三个主要的子问题并进行联合优化,同时基于中继网络两跳性在子载波分配的过程中考虑两跳子载波配对,以逼近系统最优解。最后为了保证算法的公平性,考虑子载波分配因子约束以优化子载波分配。仿真结果表明：所提算法将中继选择、两跳子载波配对与功率分配联合优化以进一步提升系统吞吐量,同时引入子载波分配因子约束,保证了算法的公平性。     

Cross-layer resource allocation for QoS guarantee with finite queue constraint in multirate OFDMA wireless networks

Efficient radio resource allocation is essential to provide quality of service （QoS） for wireless networks. In this article, a cross-layer resource allocation scheme is presented with the objective of maximizing system throughput, while providing guaranteed QoS for users. With the assumption of a finite queue for arrival packets, the proposed scheme dynamically a/locates radio resources based on user＇s channel characteristic and QoS metrics derived from a queuing model, which considers a packet arrival process modeled by discrete Markov modulated Poisson process （dMMPP）, and a multirate transmission scheme achieved through adaptive modulation. The cross-layer resource allocation scheme operates over two steps. Specifically, the amount of bandwidth allocated to each user is first derived from a queuing analytical model, and then the algorithm finds the best subcarrier assignment for users. Simulation results show that the proposed scheme maximizes the system throughput while guaranteeing QoS for users.     

Resource Allocation with Limited Feedback and Joint Coding Scheme for Wireless Multi-antenna Multicast System

In conventional multicast scheme (CMS), the total throughput of multicast group is constrained by the user with the worst channel quality. In order to overcome this problem of limited throughput, we introduce a resource allocation algorithm by exploiting layered coding combined with erasure correction coding for multicast services in the downlink of OFDMA-based multi-antenna system. To reduce the feedback overhead of uplink, we design a novel transmission scheme with limited feedback. Then, we formulate the joint subcarrier and power allocation problem for the data of base layer and enhancement layers, which is shown to be NP hard. Hence, in order to reduce the computational complexity, we propose a three-phase suboptimal algorithm. The algorithm is designed to maximize the system throughput while at the same time guarantee the quality of services (QoS) requirements of all multicast groups. It is composed of precoding scheme, proportional fairness subcarrier allocation algorithm and modified water-filling power allocation algorithm with QoS guarantees (MWF-Q). To further decrease the complexity of MWF-Q, a power allocation algorithm with increased fixed power allocation algorithm with QoS guarantees is introduced. Simulation results show that the proposed algorithms based on limited feedback scheme significantly outperform CMS and any other existing algorithm with full feedback. Moreover, the proposed scheme can efficiently reduce 50 % of the full feedback overhead.     

基于对偶分解的OFDMA系统资源分配算法

该文针对多业务OFDMA系统资源分配问题,建立了考虑业务服务质量、数据到达、系统约束的最优化问题。分析了不同业务的速率约束、延时约束和队列长度之间的关系,并利用对偶分解方法将原问题分解为若干独立子问题,分别得到了最优资源块与最优功率分配规则,进而提出了基于对偶分解的最优资源分配算法。仿真结果表明,该算法在业务违反概率较低、公平性较好、算法复杂度略有上升的情况下,可以实现非实时业务吞吐量最大化。     

基于深度强化学习的异构云无线接入网自适应无线资源分配算法

为了满足无线数据流量大幅增长的需求,异构云无线接入网(H-CRAN)的资源优化仍然是亟待解决的重要问题。该文在H-CRAN下行链路场景下,提出一种基于深度强化学习(DRL)的无线资源分配算法。首先,该算法以队列稳定为约束,联合优化拥塞控制、用户关联、子载波分配和功率分配,并建立网络总吞吐量最大化的随机优化模型。其次,考虑到调度问题的复杂性,DRL算法利用神经网络作为非线性近似函数,高效地解决维度灾问题。最后,针对无线网络环境的复杂性和动态多变性,引入迁移学习(TL)算法,利用TL的小样本学习特性,使得DRL算法在少量样本的情况下也能获得最优的资源分配策略。此外,TL通过迁移DRL模型的权重参数,进一步地加快了DRL算法的收敛速度。仿真结果表明,该文所提算法可以有效地增加网络吞吐量,提高网络的稳定性。     

Dynamic Resource Allocation for Downlink Multiuser MIMO-OFDMA/SDMA Systems

In this paper, new dynamic resource allocation algorithms are investigated for the downlink of multiuser multiple-input multiple-output orthogonal frequency-division multiple-access and space-division multiple-access (MU-MIMO-OFDMA/SDMA) systems. Firstly, a mathematical formulation of the optimization problem is presented with the objective of maximizing the total system throughput under the constraints of each user’s quality of service (QoS) requirement and the integer modulation orders available on each spatial subchannel. Secondly, since it is difficult to obtain the optimal solution to the joint optimization problem, the whole optimization procedure is divided into two steps, namely, the subcarrier-user scheduling and the resource allocation. In the first step, a new metric is proposed to measure the spatial compatibility of multiple users, each with multiple receive antennas, based on which a new scheduling algorithm is designed to identify the optimal sets of selected users over all subcarriers. In the second step, two dynamic resource allocation algorithms are developed to assign radio resources to the scheduled users subcarrier by subcarrier. Simulation results demonstrate that the proposed algorithms outperform the traditional allocation methods based on random scheduling scheme. Especially, the performance of the algorithm, combined with power reuse strategy, approaches closely to that of the optimal allocation method based on user selection.     

Adaptive subcarrier and bit allocation in OFDMA systems supporting heterogeneous services

Orthogonal Frequency Division Multiple Access (OFDMA) is an efficient multiple access method for the future wireless systems. This paper studies the adaptive subcarrier and bit allocation problem in OFDMA systems to support heterogeneous services. The goal of the considered resource optimization technique is to maximize the total system throughput under the overall transmit power constraint while guaranteeing the QoS requirement of realtime users and supporting proportional fairness among non-realtime users. First, we introduce a Rate Adaptive (RA) resource allocation algorithm for non-realtime users and a Margin Adaptive (MA) algorithm for realtime users. Then, based on the previous algorithms, a novel algorithm is proposed to allocate the resource to both classes of users, which makes an efficient tradeoff between the resource usage of realtime users and non-realtime users. The algorithm is locally optimal solution provided that the MA and RA algorithms are utilized. Also, to reduce the computational complexity, a suboptimal method based on the balancing of the average power per subcarrier is also introduced. Monte Carlo simulation results show that all the proposed algorithms outperform the existing counterparts. The results also show that the suboptimal method for heterogeneous services can efficiently reduce the computational complexity at the cost of very little performance degradation. This work was supported by the National Natural Science Foundation of China (Nos. 60472079, 60572115), and by the Natural Science Foundation of Zhejiang Province (No. Z104252), China.     

基于可再生能量协作的雾无线接入网络资源分配

针对雾无线接入网络(Fog Radio Access Network,F-RAN)中能耗开销巨大的问题,提出了一种基于能量收集(Energy Harvesting,EH)约束的资源分配算法,从联合模式选择与功率分配两个方面进行了研究.首先建立传输模型和能量采集模型,根据功率约束和电费支出约束建立最优化问题;再使用分枝定界法对通信模式进行选择,利用吞吐量注水法对不同传输模式下的发射功率进行分配.仿真结果表明,提出的基于可再生能量协作的F-RAN的吞吐量和电网能量效率均高于传统F-RAN,具有经济和环境双重效益.     

OFDMA系统基于QoS保证和最大最小公平性准则下的动态资源分配

正交频分多址(OFDMA)技术以其更高的频谱效率和抗多径衰落特性成为高速无线通信网络的候选标准。兼顾效率和公平性是OFDMA系统资源分配亟待解决的问题。本文研究了OFDMA系统中的无线资源分配问题,既要保证QoS用户的最小速率要求,同时“尽力而为”用户之间必须满足最小速率最大化公平性(max-min fairness)准则;该资源分配问题可以表述为一个系统总功率约束下的子载波分配和功率控制的混合离散型优化模型,这是难解的NP-hard问题,穷举搜索的代价是极其巨大的。针对该非凸模型,本文设计一个拉格朗日松弛的优化算法,该算法中采用修正的椭球算法求解对偶问题。算法具有多项式时间复杂度,且与子载波数目呈线性增长关系。仿真结果表明,该算法能近似最优地满足用户QoS及最大最小公平性要求。