OFDM认知无线电系统中分布式资源分配

针对多用户的OFDM认知无线电系统,提出了一种适合于混合业务的分布式资源分配新算法.该算法以最大化系统容量为目标,将资源分配问题建模为非凸优化问题,并通过拉格朗日对偶理论将原问题分解为若干个独立的子问题,通过对子问题的求解可以获得最优的子载波分配和功率分配.同时,根据认知用户业务分组中不同的业务类型授予其不同的权重因子,确保资源分配结果能够满足各认知用户的QoS.仿真结果表明,该算法不仅提高了系统容量,而且还保证了资源分配的公平性和用户的QoS,且算法复杂度不高.     

OFDMA协同通信系统子载波和功率联合分配算法

针对OFDMA协同通信系统资源分配仅考虑平均功率下的子载波分配,中继存在未用功率情况,研究子载波分配后中继剩余功率分配问题。提出一种既满足业务QoS需求又兼顾用户间公平性的子载波和功率联合分配算法,并设计一种基于二分法的功率注水分配方案。测试表明,该算法能在满足业务QoS需求及用户公平性的同时,提升系统容量。     

分组比例公平M-GSO优化的FBMC认知无线电资源分配算法

多用户混合业务的多载波认知无线电系统中,考虑频谱感知错误的实际情况,将分组比例公平思想融入资源分配中,提出一种改进群智能优化算法。该算法针对混合型业务,确保不同用户公平性以及基于滤波器组多载波(FBMC)技术前提下,引入状态因子,以最大化认知无线电系统吞吐量为目标,利用改进群智能算法合理分配系统资源。比较FBMC和OFDM的认知无线电系统,仿真分析表明,该算法在对授权用户通信影响很小的情况下,系统容量优于OFDM的认知无线电系统容量,且确保用户的公平,同时满足混合业务的QoS需求。     

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.     

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。     

基于背包模型的认知OFDM系统中的资源分配

本文针对多用户的OFDM认知无线电系统,提出了基于背包模型的资源分配新算法。该算法以最大化系统容量为目标,同时考虑各认知用户的QoS需求,将资源分配问题建模为多维0-1背包模型,并通过贪婪算法对其进行求解。仿真对比结果表明,该算法在性能上不仅逼近优化算法,而且具有较低的复杂度。     

OFDM-UWB系统基于用户速率的多用户动态资源分配算法

在原有动态资源分配算法基础上,提出了一种基于用户速率需求的动态资源分配算法。该算法在满足用户数据速率需求和服务质量要求(QoS)的前提下,以用户公平性为原则,分步执行子载波和比特分配来降低系统总的发射功率。首先,通过比较不同子载波对用户速率的影响,引入速率影响因子,对子载波进行分配;然后为每个用户子载波分配比特,并根据用户速率需求进行比特调整。为了进一步降低系统的复杂度,提出了一种通过子载波分组来完成子载波比特分配的方法。仿真结果表明,该算法能够降低系统功耗、误码率和系统复杂度。     

多业务MIMO-OFDMA/SDMA系统跨层调度与动态资源分配

对多业务MIMO-OFDMA/SDMA 系统下行链路跨层调度与动态资源分配问题进行了研究.首先,在满足各种约束条件的前提下,以最大化系统吞吐量为目标建立了相应的优化模型;然后,提出了一种基于业务类型和子空间距离的用户分组算法,该算法采用聚类分析的方法在每个子载波上对配置有多根接收天线的用户进行分组,从而降低了调度时所需搜索的用户空间的维数;接着,基于所提出的用户分组算法并结合不同业务的优先级提出了一种新的跨层调度和资源分配算法,该算法充分利用跨层信息为每个子载波调度相应的用户组,并为调度到的用户分配相应的系统资源,从而通过最大化每个子载波的吞吐量近似实现了系统整体吞吐量的最大化.仿真结果表明,与现有的方案相比,所提算法更好地满足了不同业务用户的QoS要求,并获得了更好的吞吐量性能.     

中继网中基于QoS保证和比例公平的资源分配

中继系统下的众多资源分配策略很少同时考虑多用户情形下的子载波配对和不同用户需求。针对这一问题,提出了一种基于QoS(服务质量)保证和比例公平的多用户子载波配对和功率分配算法,该算法既能保证QoS用户的最小速率要求,又能满足BE(尽力而为)用户之间速率比例公平的准则。该算法首先根据不同用户需求分配第二跳的子载波,然后利用匈牙利配对算法得到两跳子载波的最佳配对,最后用类似注水算法进行功率分配。仿真结果表明,所提算法在满足用户QoS保证和比例公平准则的同时有效提升了系统的吞吐量。     

正交频分多址系统中一种面向多业务应用的自适应资源分配算法

针对正交频分多址(OFDMA)系统下行链路多业务自适应调度的问题,该文首先以最大化系统吞吐量为优化目标、每种业务的服务质量(QoS)保证为约束条件,建立了一种通用的多业务自适应资源分配模型。为解决此优化问题,提出了一种具体的自适应资源调度算法。该算法对实时业务按照用户选择最好的信道的原则分配尽可能少的资源以保证其QoS,对非实时业务把尽可能多的剩余资源按照信道选择最好的用户的原则进行分配,充分利用信道资源,提升系统容量。仿真结果表明,该算法保证了下行OFDMA系统吞吐量的同时,在实时业务的延时和丢包率等方面有一定的优越性。     

Power allocation algorithm for cognitive radio energy harvesting networks based on energy cooperation

An algorithm to optimize the power allocation by maximizing the system throughput in cognitive radio energy harvesting networks was proposed.The algorithm formulated the throughput optimization model subject to the causality constraints of the harvested energy within the two secondary users and the interference constraint of the primary user.In addition,by applying the variable-substitution method and problem equivalence transformation,the joint optimization problem of power and cooperative energy was decoupled into two problems:a power allocation problem and a cooperative energy one.The former problem could be solved by iterating the two decoupled problems.As shown in the simulation results,the energy cooperation can significantly improve the system throughput when the harvested energy difference between two nodes is rather large.     

Optimal power allocation for multi-user OFDM and distributed antenna cognitive radio with RoF

In cognitive radio (CR), power allocation plays an important role in protecting primary user from disturbance of secondary user. Some existing studies about power allocation in CR utilize 'interference temperature' to achieve this protection, which might not be suitable for the OFDM-based CR. Thus in this paper, power allocation problem in multi-user orthogonal frequency division multiplexing (OFDM) and distributed antenna cognitive radio with radio over fiber (RoF) is firstly modeled as an optimization problem, where the limitation on secondary user is not 'interference temperature', but that total throughput of primary user in all the resource units (RUs) must be beyond the given threshold. Moreover, based on the theorem about maximizing the total throughput of secondary user, equal power allocation algorithm is introduced. Furthermore, as the optimization problem for power allocation is not convex, it is transformed to be a convex one with geometric programming, where the solution can be obtained using duality and Karush-Kuhn-Tucker (KKT) conditions to form the optimal power allocation algorithm. Finally, extensive simulation results illustrate the significant performance improvement of the optimal algorithm compared to the existing algorithm and equal power allocation algorithm.     

基于强化学习的合作频谱分配算法

为了解决认知无线电网络中的频谱分配问题,提出了一种基于用户体验质量的合作强化学习频谱分配算法,将认知网络中的次用户模拟为强化学习中的智能体,并在次用户间引入合作机制,新加入用户可以吸收借鉴其他用户的强化学习经验,能够以更快的速度获得最佳的频谱分配方案;并且在频谱分配过程中引入了主用户和次用户之间的价格博弈因素,允许主用...     

基于MADDPG的无人机辅助通信功率分配算法

针对多无人机(unmanned aerial vehicle, UAV)作为空中基站辅助通信的吞吐量和公平性问题,提出了一种基于多智能体深度确定性策略梯度算法(multi-agent deep deterministic policy gradient algorithms, MADDPG)的功率分配算法,该算法通过联合优化UAV基站的功率分配和用户接入以提高系统吞吐量和公平性。本文首先构建了UAV基站为地面建立通信服务的三维场景,然后通过联合功率、用户关联和UAV位置约束,构建了吞吐量和公平性最大化的问题模型。考虑到该问题的复杂性,本文将所构建的优化问题建模为马尔科夫决策过程(Markov decision process, MDP),通过引入深度确定性策略梯度算法(deep deterministic policy gradient algorithm, DDPG)解决该问题。仿真结果表明,本文提出的基于MADDPG的UAV基站功率分配算法与其他算法相比,可以有效地提升系统的吞吐量和用户的公平性,提高通信的服务质量。     

一种新的认知无线电功率分配算法（英文）

Most resource allocation algorithms are based on interference power constraint in cognitive radio networks.Instead of using conventional primary user interference constraint,we give a new criterion called allowable signal to interference plus noise ratio(SINR) loss constraint in cognitive transmission to protect primary users.Considering power allocation problem for cognitive users over flat fading channels,in order to maximize throughput of cognitive users subject to the allowable SINR loss constraint and maximum transmit power for each cognitive user,we propose a new power allocation algorithm.The comparison of computer simulation between our proposed algorithm and the algorithm based on interference power constraint is provided to show that it gets more throughput and provides stability to cognitive radio networks.     

Distributed cross‐layer optimization for wireless regional area network‐based cognitive radio networks

This paper presents a study of a cross‐layer design through joint optimization of spectrum allocation and power control for cognitive radio networks (CRNs). The spectrum of interest is divided into independent channels licensed to a set of primary users (PUs). The secondary users are activated only if the transmissions do not cause excessive interference to PUs. In particular, this paper studies the downlink channel assignment and power control in a CRN with the coexistence of PUs and secondary users. The objective was to maximize the total throughput of a CRN. A mathematical model is presented and subsequently formulated as a binary integer programming problem, which belongs to the class of non‐deterministic polynomial‐time hard problems. Subsequently, we develop a distributed algorithm to obtain sub‐optimal results with lower computational complexity. The distributed algorithm iteratively improves the network throughput, which consists of several modules including maximum power calculation, excluded channel sets recording, base station throughput estimation, base station sorting, and channel usage implementation. Through investigating the impacts of the different parameters, simulation results demonstrates that the distributed algorithm can achieve a better performance than two other schemes. Copyright © 2011 John Wiley & Sons, Ltd.     

Novel Fairness Improved Radio Resource Management Schemes for Best-Effort Service in OFDMA Systems

In this paper, we propose a complete radio resource management procedure for best-effort service in OFDMA systems, which improves the system fairness with graceful throughput degradation compared to the upper bound of the system throughput. By the proposed bandwidth and power allocation algorithms, the user in the worst channel environment has almost the same probability of accessing the system as the user in the best channel environment. Furthermore, a novel sub-channel allocation algorithm is proposed to exploit frequency selectivity and multi-user diversity gains simultaneously in OFDMA systems, which is able to achieve the highest system throughput given each user’s channel environment.     

Resource allocation based on dynamic hybrid overlay/underlay for heterogeneous services of cognitive radio networks

In cognitive radio networks (CRNs), hybrid overlay and underlay sharing transmission mode is an effective technique to improve the efficiency of radio spectrum. Unlike existing works in literatures where only one secondary user (SU) uses both overlay and underlay mode, the different transmission modes should dynamically be allocated to different SUs according to their different quality of services (QoS) to achieve the maximal efficiency of radio spectrum. However, dynamic sharing mode allocation for heterogeneous services is still a great challenge in CNRs. In this paper, we propose a new resource allocation method based on dynamic allocation hybrid sharing transmission mode of overlay and underlay (Dy-HySOU) to obtain extra spectrum resource for SUs without interfering with the primary users. We formulate the Dy-HySOU resource allocation problem as a mixed-integer programming to optimize the total system throughput with simultaneous heterogeneous QoS guarantee. To decrease the algorithm complexity, we divide the problem into two sub-problems: subchannel allocation and power allocation. Cutset is used to achieve the optimal subchannel allocation, and the optimal power allocation is obtained by Lagrangian dual function decomposition and subgradient algorithm. Simulation results show that the proposed algorithm further improves spectrum utilization with simultaneous fairness guarantee, and the achieved Dy-HySOU diversity gain is satisfying.     

使用对偶分解的MU-CoMP-JT联合资源分配

在MU-Co MP-JT(Multi-User Coordinated Multiple-Points Joint Transmission)联合资源分配问题中,传统的迫零预编码矩阵会使得每根天线发送功率互不相同,当Co MP节点发射功率仅满足总功率约束时性能损失不明显,而当Co MP节点分布在不同的地理位置时将受到单节点功率约束,这势必会降低系统功率利用率。为了进一步提升系统吞吐量,基于对偶分解理论提出了一种联合预编码优化的资源分配算法。该算法以最大化用户权重速率为目标,将原优化问题分解成若干个优化的子问题,不同子问题对应不同接收天线数的联合优化问题。当子信道的发送天线数大于接收天线数时,通过多次迭代计算得到预编码矩阵,并且预编码矩阵会随着拉格朗日因子的变化而变化。仿真结果表明所提联合预编码优化的联合资源分配算法能够明显提升系统吞吐量,且提高天线功率利用效率。     

基于双连接和回传带宽配置的系统吞吐量效用和最大化算法

针对双连接可行的异构无线网络中关于用户关联和回传带宽配置的联合优化问题,构建了一个新的网络吞吐量效用和最大化框架。将该联合优化问题建模为一个非凸的混合整数分式优化问题。为了便于求解,首先将原建模问题进行去分式化转换,然后针对转换后依旧非凸的混合整数非线性优化问题,将其分解为两个优化子问题分别求解。通过固定用户关联变量,得到了最优的回传带宽配置机制;通过固定回传带宽配置因子变量,提出一个有效的迭代算法求解双连接可行的用户关联子问题。相比固定的回传带宽配置机制,所提算法可以获得最优的回传单位带宽配置因子值,同时拥有最优的系统吞吐量和系统吞吐量效用和性能。