基于比例速率保证的信道误差OFDMA中继系统资源分配

针对存在有信道估计误差的正交频分多址( OFDMA)中继系统,在考虑用户传输中断概率的同时,提出了满足不同用户最小服务质量( QoS)需求和比例公平性约束条件下的中继选择、子载波分配和功率分配的联合优化问题,建立了以最大化系统总容量为目标的优化模型。在此基础上以速率最大化为目标进行最佳中继选择,并通过动态子载波分配来满足用户的最小QoS需求和比例公平性,最后采用拉格朗日乘子法来得到最优功率分配方案。仿真结果表明,此算法在降低用户中断概率的同时,提高了系统吞吐量并保证了用户速率的比例公平性。     

一种基于比例公平的多跳OFDMA系统资源分配算法

为解决多跳OFDMA系统的资源分配问题,本文提出一种基于比例公平的资源分配算法,该算法将一个联合优化的问题分解为两个步骤,首先在等功率分配的条件下求出最优的子载波分配以及中继节点选择方案,然后再根据注水定理进行功率分配,以提高系统的性能.仿真结果表明,该算法能够在保障不同用户间公平性的条件下,有效地提高系统的容量.     

基于速率约束的OFDM中继链路能效最优资源分配策略

该文研究解码转发(DF)模式的OFDM中继链路的能效最大化资源分配问题。与现有典型的固定速率最小化发射功率或无约束最大化能效算法不同,该文考虑电路功率消耗的前提下,将问题建模为以最大化系统能效为目标,同时考虑用户最小速率需求、源节点S和中继节点R各自总发射功率约束下的联合子载波配对和最优功率分配问题。证明了速率和功率联合约束条件下中继链路全局能效最优解的唯一性,在此基础上提出一种低复杂度联合最优资源分配策略。仿真结果表明,该文所提方案能够在最小速率和S/R节点最大发射功率约束下自适应分配功率资源,实现系统能效最优,并能够降低链路的中断概率。     

OFDMA中继网络变时域节能资源分配策略

现有正交频分多址接入(OFDMA)中继网络资源分配的研究均采用固定时域配置下的频域分配和功率分配,不能很好适应用户业务在时域上的变化。该文针对OFDMA中继网络提出一种可最优节能的资源分配策略和一种简化的节能资源分配策略,为资源分配问题建立一般化的模型,即动态分配时域资源、频域资源和功率资源,所建模型具有很强的灵活性和适应性,不仅适用于固定时域分配系统,也适用于非固定时域分配系统。此外针对非满负荷业务,在保证用户服务质量的情况下,以节能为目标对无线非协作中继网络的资源分配进行能效最大化建模,使用拉格朗日乘数法对模型求解。考虑到算法复杂度,应用指派问题中的匈牙利算法设计出一种简化的资源分配策略。理论和仿真结果表明,最优节能资源分配算法能够得到能效的最大化,而简化节能资源分配算法与最优节能资源分配算法在能效上的差距不足5%,但算法复杂度得到了显著降低。而且,动态分配时域资源比固定时域分配对用户分布不均或链路分布不均有更强的适应性。     

比例速率约束下OFDMA系统近似最优的资源分配算法

中继协作OFDMA系统以及非中继OFDMA系统,在比例速率公平性约束下的无线资源分配问题,是含非线性等式约束的混合离散型优化问题,很难得到满足实时性要求的近似最优算法。该文分别基于这两种系统在速率公平性约束下的等价优化模型,提出拉格朗日松弛的联合优化算法。算法的关键在于OFDMA系统的渐进强对偶性,这个性质使得多项式时间算法的设计成为可能。该文算法的复杂度与系统载波数目成线性关系;仿真结果表明,算法的性能极好地逼近最优分配方案性能。     

AF中继下行链路系统的能效资源分配方案

为最大化放大转发中继系统的下行链路总能效,结合系统的电路功率,提出了一种基于能效的中继选择和功率分配联合方案.为降低复杂度,采用分步式次优化方案,利用虚拟直传信道增益得到中继选择方法,并将中继系统转换为单跳系统;然后利用凸规划得到最优功率分配.此外,为适应不同的用户分布,提出联合小区呼吸机制的分配方案.仿真结果表明,所提次优联合方案逼近最优值,联合小区呼吸的方案可适应不同的用户分布并进一步提升能效.     

认知无线电下行链路中的OFDMA资源分配算法

摘 要： 本文提出一种适用于下行认知无线电系统的正交频分多址接入资源分配算法,在总发射功率、误比特率和对授权用户的干扰受限的条件下最大化系统信息传输速率.本算法分两步实现：首先通过比较各认知用户在各子载波上单位信号发送条件下接收信号的信干噪比实现子载波分配;然后利用凸优化理论求解非负实数域内的比特数和功率值的最优解,并将其调整为符合实际系统需要的比特数和功率值,实现比特和功率分配.仿真结果表明,相比传统的基于频谱空洞的资源分配算法,本算法可以提供显著的系统信息传输速率增益.     

一种分布式的OFDMA系统资源分配算法

本文研究OFDMA系统的资源分配问题,把该问题建模为一个在基站的总发射功率一定的条件下,使系统中各个用户的权重速率之和最大化的数学模型。并提出一种基于对偶分解的分布式资源分配算法,将该问题分解为一个关于基站的主问题以及若干个关于用户的子问题。各个用户可以通过对子问题的求解获得各自的子载波以及功率的分配方案;而基站通过对主问题的求解使得满足子载波与功率的分配能够满足约束条件的要求,实现各用户权重速率和最大化的优化目标。所提算法能够把一个复杂的优化问题分解为若干个独立的子问题进行并行求解,因此可以有效地降低计算的复杂度以及基站的运算量。仿真结果表明,该算法能够在较少的迭代步数内得到一个近似最优解。      

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。     

协作系统中基于比例公平的资源分配方法

针对多用户协作中继系统中的资源分配问题,提出了一种在满足用户速率比例公平约束条件下的新算法。该算法先将由2个时隙组成的中继用户传输链路转换为一个等效信道链路,将涉及子载波分配、中继选择和功率分配的组合优化问题转化为分步的次优化问题。该算法在等功率分配情况下,根据各用户速率比例公平系数进行初步子载波数目分配;以瞬时信道增益最佳原则,进行剩余子载波数目分配及具体子载波分配,同时完成中继选择;在速率比例公平约束条件下推导出次优化功率分配的闭式表达式,从而完成各子载波上的功率分配。仿真结果表明,该算法在有效提高系统容量的同时,保证了各用户速率之间的比例公平性。     

OFDMA中继通信系统中非对称的资源分配

传统的OFDMA协作通信系统,中继经过一一对应的两跳子载波接收和发送数据信息,这一对称的资源分配模式限制了系统吞吐量的提高;本文放宽对称约束,研究了非对称模式下的资源分配问题。自由度的增加,增大了系统资源分配的复杂度,这是一个结合中继选择、子载波分配和功率控制等多维自由度的混合离散型优化问题,很难得到满足实时性要求的近似最优算法。针对该模型,本文提出基于拉格朗日松弛的联合优化算法,算法的关键在于OFDMA系统的渐进强对偶性,这个性质使得多项式时间算法的设计成为可能。算法的复杂度与子载波数目成线性关系。仿真结果表明,该算法的性能是近似最优的,而且与对称模式相比,非对称模式下的资源分配进一步提升了系统的吞吐量性能。      

速率比例公平下多用户OFDM系统的自适应资源分配

在多用户正交频分复用(MU-OFDM)系统中,考虑各个用户之间具有比例数据传输速率限制条件下的一种公平的自适应资源分配方案的最优算法计算量巨大,为此,提出了一种将子信道分配和功率分配相分离的次优算法.首先,在假设相同功率分配的情况下进行子信道的分配,然后在保持一定比例公平条件下使总容量最大时进行最优功率分配.对该算法的仿真表明,在用户数为2、子信道数为10的系统中,所提算法的容量性能接近最优算法,而计算量由指数增长变为线性增长.所提资源分配算法的总容量比以前的算法在用户间的分配更公平也更灵活.     

Dynamic Resource Allocation in OFDMA-Based DF Cooperative Relay Networks

This paper investigates the Resource allocation problem in OFDMA-based decode-and-forward cooperative communication systems. The objective is to maximize the sum throughput under the constraints of joint total transmission power and subchannels occupation, while maintaining the maximum fairness among multiple relay nodes. Since the optimal solution to this combinatorial problem is extremely computationally complex to obtain, we propose a low-complexity suboptimal algorithm that allocates subchannel and power separately. In the proposed algorithm, subchannel allocation over the relay nodes is first performed under the assumption of equal power distribution. Then, an optimal power allocation algorithm named multi-level water-filling is used to maximize the sum rate. The simulation results show that the performance of the proposed algorithm can approach asymptotically to that of the optimal algorithm while enhancing the fairness among all relay nodes and reducing computational complexity from exponential to linear with the number of subchannels. It is also shown that the proposed equal power distribution algorithm with subchannel permutation (SP) outperforms the one without SP.     

A Novel Genetic Algorithm for Adaptive Resource Allocation in MIMO-OFDM Systems with Proportional Rate Constraint

This paper considers base station allocation of subcarriers and power to each user to maximize the sum of user data rates, subject to constraints on total power, bit error rate, and proportionality among user data rates in Multiple Input Multiple Output Orthogonal Frequency Division Multiple access (MIMO-OFDMA) system. Previous allocation methods have been iterative nonlinear methods suitable for offline optimization. The subcarrier allocation is tackled using a novel algorithm which combines the aspects of both deterministic and Genetic Algorithms (GA). This modified GA gave very encouraging results as can be seen from the simulation results shown. The simulation results show a marked improvement in the performance of the algorithm as the number of users increase. The capacity attained from the subcarrier allocation scheme generated by our algorithm is found to be comparable to that attained by previous algorithms.     

Power-Minimizing Rate Allocation in Cooperative Uplink Systems

The rate-allocation problem, which has been aimed at minimizing the total transmit power in cooperative uplink systems, is investigated. Each user transmits over an orthogonal frequency band using cooperative broadcasting. The broadcasting nature of the wireless channel is exploited by allowing users to act as relays for one another. All users operate in the decode-and-forward mode. Depending on the number of relays that was selected by a user, we suggest two schemes: 1) the flow-optimized cooperative scheme (FCS) and 2) the single-relay cooperative scheme (SCS). We develop rate-allocation algorithms for them. In our simulation, we compare the outage performance of our schemes with two other schemes: 1) the orthogonal cooperative scheme and 2) direct transmission. Results indicate that our schemes achieve full diversity order and outperform other compared schemes. The algorithm for FCS achieves the optimality, whereas the algorithm for SCS is near optimal. In addition, our algorithms have fast convergence performance. SCS has a lower complexity than FCS, but it requires a higher total transmit power. However, the difference in total transmit power between FCS and SCS is not large under practical rate requirements. In addition to the total transmit power, we consider the improvement in the individual transmit powers of the users.      

LTE系统上行链路无线资源分配算法

LTE系统上行链路中采用SC-FDMA技术,要求为用户分配的资源块（RU）在频域上连续分布,同时位于扇区边缘的用户因为收到较为严重的外扇区干扰性能有很大恶化。文中主要针对这两个问题,在传统算法的基础上,分别提出一种改进的PF频域分组调度算法和一种改进的部分功率控制算法。仿真结果表明,二者结合能较好地改善系统性能,特别是边缘用户的性能。     

Resource Allocation in Multi-channel Multi-user Relay System with Fairness Constraints

In this paper, we derive optimal joint power allocation, subchannel pairing and scheduling strategies in multiple orthogonal channels multiple users wireless networks in the presence of a single regenerative relay node. Two models with users’ data rate request (fairness) constraint in different time domains are considered. The first one is called deterministic model in which each user’s data rate request has to be satisfied in each time slot t (named short term fairness constraint) and the second one is called stochastic model in which users have average data rate request (named long term fairness). In these two models the optimization problems of maximizing system capacity with total transmit power constraint and fairness constraint are formulated. The Lagrangian dual method is used to derive the optimal solution for deterministic model and in the stochastic model stochastic approximation and dual method are employed to find out the optimal algorithm. Both algorithms have polynomial times complexity, which is reduced significantly compared with the Exhaustive Search Method (ESM). Since Lagrangian dual method is utilized in both schemes, the dual gap is also analyzed. Furthermore, through the analysis and simulation, we see that the optimal resource allocation algorithm in stochastic model has better performance than that in the deterministic model for its ability to exploit temporal diversity.     

Optimal Resource Allocation for Multiuser MIMO-OFDM Systems With User Rate Constraints

With the proliferation of wireless services, personal connectivity is quickly becoming ubiquitous. As the user population demands greater multimedia interactivity, data rate requirements are set to soar. Future wireless systems, e.g., multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM), need to cater to not only a burgeoning subscriber pool but also to a higher throughput per user. Furthermore, resource allocation for multiuser MIMO-OFDM systems is vital for the optimization of the subcarrier and power allocations to improve overall system performance. Using convex optimization techniques, this paper proposes an efficient solution to minimize the total transmit power subject to each user's data rate requirement. Using a Lagrangian dual decomposition, the complexity is reduced from one that is exponential in the number of subcarriers $M$ to one that is only linear in $M$. To keep the complexity low, linear beamforming is incorporated at both the transmitter and the receiver. Although frequency-flat fading has been known to plague OFDM resource allocation systems, a modification, i.e., dual proportional fairness, seamlessly handles flat or partially frequency-selective fading. Due to the nonconvexity of the optimization problem, the proposed solution is not guaranteed to be optimal. However, for a realistic number of subcarriers, the duality gap is practically zero, and optimal resource allocation can be evaluated efficiently. Simulation results show large performance gains over a fixed subcarrier allocation.