一种基于平均时延最小化的跨层自适应算法

该文研究了OFDM系统的跨层自适应问题。针对现有资源分配算法不能有效地兼顾频谱效率、公平性和QoS的问题,提出了一种综合考虑CSI和排队信息的基于平均时延最小化的跨层自适应资源分配算法。该算法利用经济学中的效用理论,将平均等待时间作为优化权重,充分保证了不同业务的QoS,通过效用函数保证了不同等待时间或者不同QoS的公平性。仿真结果显示,该算法的时延性能略好于LWDF算法,大大好于PF算法和EXP算法,并且很好地保证了时延的公平性,非常适合对时延敏感的通信。     

分层认知无线电网络中基于稳定匹配的资源分配算法

频谱资源的合理分配是认知无线电技术追求的目标之一,随着认知无线电网络中的次用户(SUs)数量不断增加,频谱资源的精确、实时分配与管控越来越难以实现。针对此问题,该文提出一种分层的认知无线电网络(CRN)架构,多个管理实体专注于为各层用户提供频谱服务;并在该架构下,提出一种基于稳定匹配的资源分配算法,用户通过自主协商形成分配结果,不仅保证了主用户(PUs)对次用户的功率限制,还充分考虑了各自的效用。仿真结果表明,所提算法的性能接近于最优方案,并降低了计算复杂度和系统时延。     

密集异构网络中基于多目标优化的资源分配策略

密集异构网络(Dense Heterogeneous Network, DHN)通过部署小基站可以提升网络容量和用户速率,但小基站的密集部署会产生巨大的能耗和严重的干扰,进而影响系统的能量效率(Energy Efficiency, EE)和频谱效率(Spectral Efficiency, SE)。在保证用户服务质量(Quality of Service, QoS)需求的前提下,为了联合优化系统的能量效率和频谱效率,研究了密集异构网络中下行链路的资源分配(Resource Allocation, RA)问题。首先,将频谱和小基站发射功率分配问题建模为联合优化系统能量效率和频谱效率的多目标优化问题;其次,提出了基于单策略多目标强化学习(Single-strategy Multi-objective Reinforcement Learning, SMRL)的资源分配算法求解所建立的多目标优化问题。仿真结果表明,与基于单目标强化学习的资源分配算法相比,所提算法可以实现系统能量效率和频谱效率的联合优化,与基于群体智能算法的资源分配算法相比,所提算法的系统能量效率提高了1%～1.5%,频谱效率...     

基于区分业务的OFDM跨层动态资源分配算法

跨层设计是目前比较流行的、有效的无线系统设计方法.本文针对多业务(实时业务和非实时业务)正交频分复用(OFDM)系统,提出了一种跨层结构的动态资源分配算法.该算法不仅在物理层上考虑信道状态信息和功率限制条件,而且在MAC层上针对不同类型业务采取不同的调度策略:(1)对于高优先级的实时业务,采用最大化容量的OFDMA策略,在频域上最大限度地利用多用户分集提高频谱效率,在时域上通过时隙的分配来保证时延边界;(2)对于低优先级的非实时业务,在频域上采用基于比例公平的OFDMA策略,在时域上尽力而为地分配时隙.仿真结果表明,与传统的单层资源分配算法相比,所提算法能够在保证不同业务服务质量(QoS)的前提下,大幅度地提高系统性能.     

GEO卫星LTE跨层资源调度算法

地球同步轨道(GEO)卫星通信系统具有通信时延长的特点,适用于GEO卫星长期演进(LTE)通信的资源调度算法非常重要。为提升星上资源分配的高效性,基于可变最大加权时延优先(M-LWDF)算法,提出了一种综合考虑媒体接入控制(MAC)层参数和应用层参数的跨层调度算法。该算法在MAC层从数学角度推导,提出权重更大的时延判决因子及在应用层根据业务优先级不同引入Q因子。仿真结果表明,与M-LWDF算法相比,不同业务情况下,跨层资源调度算法减小了通信时延,提高了系统吞吐量,但公平性能略有下降并增加了复杂度。     

双参数跨层自适应资源分配算法研究

针对多用户OFDM系统,该文提出了一种适合于混合业务的双参数跨层自适应资源分配方案,它只需调整时延补偿因子和吞吐量补偿因子两个参数,就可将现有的调度算法灵活高效地应用于系统中.仿真结果表明,该方案可以灵活地在系统功率效率和用户服务质量满意度之间取得折衷,并保证不同类型业务用户间的公平性.     

多用户MIMO-OFDM系统基于QoE效用函数的跨层资源分配

对多用户MIMO-OFDM系统下行链路的资源分配问题进行了研究.依据实时(RT,real-time)和非实时(NRT,non real-time)业务的不同特点分别设计了QoE效用函数,并提出一种基于RT &NRT QoE效用函数的跨层资源分配算法.该算法利用所设计的效用函数计算用户分得资源所贡献的QoE增量,并根据由QoE增量确定的用户优先级完成时频资源块的分配,进而通过“拟注水”方式对功率分配进行优化.仿真结果表明,该算法相比已有算法在用户平均QoE、实时业务平均分组时延、非实时业务平均分组丢失率以及系统和吞吐量方面的性能均得到了明显改善.     

OFDMA毫微微小区双层网络中基于分组的资源分配

毫微微小区(Femtocell)网络能够增强室内覆盖,提高系统容量,但是在频谱共享的正交频分多址(OFDMA)毫微微小区网络中,毫微微小区之间的同层干扰以及毫微微小区与宏小区(Macrocell)之间的跨层干扰严重限制了系统的性能。针对这两种干扰,该文提出一种基于分组的资源分配算法。该算法包括两部分:一部分是宏基站先利用改进的匈牙利算法为宏小区用户分配信道,再用注水算法分配功率,保证宏小区用户的正常传输;另一部分是在避免干扰宏小区用户的基础上,先采用模拟退火算法对毫微微小区进行分组,再进行信道和功率分配,满足毫微微小区用户的数据速率需求,最大化频谱效率。仿真结果表明,该算法有效地抑制了这两种干扰,既能保证用户的数据速率需求,又能有效提升网络频谱效率。     

基于OFDMA/TDM系统的跨层资源分配算法

研究了OFDMA/TDM系统的资源分配问题,基于最差信道用户优先原则,提出了一种新的跨层资源分配算法CRRA(Cross-layer Radio Resource Allocation)及其简化算法S-CRRA(Simplified CRRA).该算法利用用户数据队列长度动态变化的特性和多用户分集策略以实现在满足用户服务质量(Quality of Service,QoS)的基础上最大化系统吞吐量,并且将频谱和功率分配分开进行以降低计算复杂度.仿真结果表明,CRRA算法和S-CRRA算法在系统吞吐量上基本一致,两者较已有的OFDMA/TDM系统资源分配算法能提高10%-24%的系统吞吐量.     

基于拍卖算法的TD-LTE系统上行资源分配算法

在LTE上行系统中,用户设备必须在连续的子载波上发送数据,而且易受到邻扇区同频干扰,有效的资源分配是提高系统性能的关键。对上行资源分配相关文献进行分析综述,提出一种基于拍卖的TD-LTE上行系统单扇区及多扇区资源分配算法,并进行了TD-LTE系统级平台仿真验证。仿真结果表明,与传统方法相比,提出的方法可以更为有效地提高系统频谱效率和边缘用户频谱效率,提升了用户Qo S。     

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.     

QoS约束下多层异构蜂窝网中基于分层休眠的节能机制研究

针对多层异构蜂窝网中资源分配和节能问题,本文基于随机几何模型利用分布式配对算法实现了用户连接和资源分配的联合优化,并利用分层休眠机制提升了系统的整体能效。首先,采用随机几何工具对不同类型的基站进行建模,在建立模型的基础上提出用户QoS约束下的最小化系统总功耗的联合优化方案,然后将该方案分解简化为用户连接资源分配和基站休眠两个子问题,并分别利用稳定配对算法和基于投票法的分层休眠机制加以解决。仿真和分析结果表明本文方法可以显著提升系统能效,与已有的休眠策略相比具有更低的用户平均中断比。      

一种改进的多用户OFDM系统跨层分配优化算法

提出了一种多用户正交频分复用系统的跨层资源分配模型,结合了物理层中信道状态和媒体接入控制层中用户的队列信息,能更好地满足用户的服务质量要求。改进的混合优化算法结合了遗传算法与禁忌搜索算法的优点,并且对遗传算法的交叉因子进行改进,提高了全局搜索能力,能够更好地收敛于全局最优值,这样能更好地解决跨层资源分配问题。仿真结果表明,在此模型下利用改进的混合算法能有效地提高系统吞吐量,减小用户的平均时延,提高服务质量。     

Maximum Achievement Rate Allocation Algorithm for Downlink Multi-User OFDMA Systems

In multi-user OFDMA systems, adaptive resource allocation has been identified as one of the key technologies to have more flexibility and higher efficiency. Several adaptive subcarrier allocation algorithms with the objective to maximize spectral efficiency or fairness have been proposed. However, quality of service (QoS) requirement of each user may not be supported. Some algorithms considering user’s QoS requirement have been introduced, but they do not consider the case that every user’s QoS requirement cannot be guaranteed with limited resources. In this paper, we propose a maximum achievement rate allocation (MARA) algorithm as a new adaptive resource allocation algorithm. The proposed MARA algorithm has a goal to improve overall throughput while maximizing achievement rate, i.e., maximize the number of users meeting QoS requirements. In addition, we investigate that MARA is more effective when fractional frequency reuse (FFR) is adopted as a frequency partitioning scheme. Simulation results show that the MARA algorithm improves the achievement rate as well as overall throughput. Moreover, further performance gains are achieved when FFR is adopted.     

基于资源分配和功率控制的D2D中继选择

为优化蜂窝用户通信与设备直传(D2D)中继通信共存下的同频干扰问题,满足蜂窝用户容量要求,提出了一种基于能效的联合资源分配和功率控制的D2 D中继选择算法.该算法首先对等效D2 D中继链路进行资源分配,减小算法复杂度的同时使得D2 D链路对蜂窝链路产生的干扰最小;然后以资源分配结果和功率控制算法为依据进行中继选择.该方案不仅考虑了D2 D中继链路的能效问题,而且还同时考虑到了对蜂窝链路的干扰问题.通过仿真验证,所提算法不仅能有效提升D2 D中继链路的能效值,同时降低了对蜂窝用户的干扰.     

A resource allocation algorithm for throughput maximization with fairness increase based on virtual PRB in MIMO-OFDMA systems

This paper deals with a new resource allocation algorithm in downlink MIMO-OFDMA systems. The objective is to maximize the system throughput with respect to fairness criteria since some users may experience bad channel conditions for a long time. Known to be NP-hard, the original optimization problem is divided into two sub-problems where radio resource allocation and power allocation are performed separately. Firstly, a recursive PRB allocation algorithm is performed aiming at maximizing the system throughput. In LTE systems, 41% of sub-carriers are considered unused which introduces spectral efficiency loss. As solution, the eNodeB aggregates the unused sub-carriers by each user to construct a “virtual” PRB to be allocated to seldom served user for fairness and throughput increase. Secondly, power allocation is performed to select a more appropriate MCS.

     

Scheduling and Resource Allocation Strategy for OFDMA Systems Over Time-Varying Channels

A cross-layer scheduling and resource allocation (SRA) strategy for an adaptive modulation and coding (AMC) based orthogonal frequency multiple access (OFDMA) system is proposed. The objective of this paper is to maximize the system throughput as a function of the bit error rate (BER) and the spectral efficiency based on the selected modulation and coding schemes (MCSs). The proposed strategy contains two main algorithms. Firstly, the scheduling algorithm that aims to maximize the average system throughput by arranging the users in distinct queues according to their priorities and selecting the best user of each queue individually in order to guarantee a fair user service amongst different priority levels. Secondly, the resource allocation algorithm that allocates the user, bit and power based on the channel conditions of the scheduling users and the transmission power constraints. The transmitter of the investigated AMC-OFDMA system at the assigned base station (BS) divides the transmitted OFDMA frame into sub-channels and assigns each sub-channel to a scheduled user. In this paper, we compare the performance of the proposed SRA with the conventional first in first out (FIFO) queuing based scheduling and resource allocation strategies used for an AMC-OFDMA system. The simulation results show that the investigated AMC-OFDMA system based on the proposed SRA strategy outperforms the conventional approaches.     

System utility based resource allocation for D2D multicast communication in software-defined cellular networks

Device-to-device (D2D) multicast communication is a useful way to improve the communication efficiency of local services. This study considers a scenario of D2D multicast communication in software defined cellular network and investigates the frequency resource allocation problem. Firstly, we build the system model and formulate the optimization problem. Secondly, a hierarchical scheme to achieve a suboptimal solution is proposed. To select appropriate user equipments (UEs) as potential D2D transmitters (PDTs), a social aware PDT selection method is proposed. Then, a resource allocation algorithm considering users’ priorities is proposed. Furthermore, to study the resource allocation for general system that UEs without priorities, a non-priority considered allocation algorithm is proposed also. Numerical simulation results show that the proposed schemes are effective in improving the system utility and reducing the resource consuming for D2D communications.     

Joint energy efficiency and spectral efficiency optimization algorithm for UDN under the restriction of interference threshold and backhaul capacity

Aiming at the scenarios which consider the constraint of backhaul capacity restriction and interference threshold in ultra-dense networks (UDN),an integer linear programming (ILP) and Lagrangian dual decomposition (LDD) based joint optimization algorithm of energy efficiency and spectrum efficiency was proposed.In the proposed algorithms,the user association problem with the constraint of limited backhaul capacity was modelled as an ILP problem and then finished the connection between the user and the base station of microcell by solving this problem with dynamic programming method.Therefor,Lagrangian dual decomposition (LDD) was applied in an iteration algorithm for spectrum resource allocation and power allocation.The simulation results show that compared with traditional schemes,the proposed algorithm can significantly improve the energy efficiency and spectrum efficiency of system and use the microcell’s load capacity more efficiently.     

多天线OFDM系统中的自适应子信道分配和天线选择

针对多用户MIMO-OFDM系统,提出自适应子信道分配算法.首先将相邻的子载波分块成若干子信道,在分配子信道的时候优先考虑平均信道容量小的用户,并且将相对利用度大的子信道分配给该用户,从而在频谱利用率和公平性上具有较好的性能.为了降低发送端的硬件复杂度以及非线性因素的影响,将自适应信道分配和发送天线选择相结合,提出联合自适应子信道分配和天线选择算法.仿真结果表明,本文提出的联合算法具有较高的频谱效率和较低的中断概率.