基于拉格朗日对偶的认知无线电网络最优资源分配算法

针对传统机会认知无线电网络容量有限的问题,提出了基于拉格朗日对偶的认知无线电网络最优资源分配算法。首先,将一个用户分配给每个子载波;然后,使用标准的凸优化方法确定每个子载波的对应功率,仅一个用户可获得功率正值;最后,利用拉格朗日对偶分解法同时分配CR网络中的子载波和功率,最大限度地提高系统的总容量。使用长期演进真实场景参数与空间信道传播模型评估了所提算法的有效性,仿真结果表明,相比次优资源分配算法,所提算法的总容量平均分别提高了9.3%,相比基于任意输入分布的最优资源分配算法,总容量提高了28.7%,并取得了较快的收敛速率,可以很好地用于解决无线电网络资源配置中的容量问题。     

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.     

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。     

一种低复杂度的MIMO-OFDM下行链路子载波分配改进算法

针对多入多出正交频分复用( MIMO - OFDM)系统的下行链路,提出一种基于信道状态信息(CSI)反馈的次优子载波分配算法.算法从寻求容量和公平性之间的平衡出发,将提高系统容量作为资源分配的优化目标.该算法首先按照比例公平约束进行子载波初始分配,然后将现行分配给任意两个用户的子载波互换之后计算系统容量,如果系统容量增加,则将子载波在用户间进行交换,否则保持初始分配不变.将子载波在用户间进行迭代调整,直到系统总容量不再增加为止.仿真结果表明,该算法能够很好地保证比例公平约束,有效地提高系统容量,提升效率可以达到贪婪算法的40％左右,对MIMO - OFDM系统中的子载波分配具有一定的参考价值.     

OFDM系统中考虑信源编码特性的多播资源分配方案

针对OFDM系统单播以及传统多播没有充分发挥无线传播开放性优势的问题,提出了多个多播组保证组间比例公平的资源分配方案.该方案由子载波分配算法及功率分配算法构成,前者通过修正存在的单播子载波分配算法得到;后者基于理论推导的非线性方程组,得到了子载波分配给定条件下的最优功率分配.仿真结果显示,所提方案能够确保完美的多播组间公平性,且和速率容量性能明显优于传统多播方式.结合方案的低复杂度特性,它适合在实际无线系统的音/视频多播中进行应用.     

多用户OFDM系统中一种新颖的自适应功率分配方案

针对多用户正交频分复用(OFDM)系统自适应资源分配的问题,提出了一种新的自适应子载波分配方案。子载波分配中首先通过松弛用户速率比例约束条件确定每个用户的子载波数量,然后对总功率在所有子载波间均等分配的前提下,按照最小比例速率用户优先选择子载波的方式实现子载波的分配;在功率分配中提出了一种基于人工蜂群算法和模拟退火算法(ABC-SA)相结合的新功率分配方案,并且通过ABC-SA算法的全局搜索实现了在所有用户之间的功率寻优,同时利用等功率的分配方式在每个用户下进行子载波间的功率分配,最终实现系统容量的最大化。仿真结果表明,与其他方案相比,所提方案在兼顾用户公平性的同时还能有效地提高系统的吞吐量,进而证明了所提方案的有效性。     

一种新型的OFDMA比例公平资源分配算法

针对已有算法对系统容量和高公平性兼顾较差的情况,提出了一种满足公平性的系统容量最大化资源分配算法。在子载波分配中通过建立信道效率控制模型,给当前用户分配信道效率最高的子载波,将信道增益低于门限值的子载波重新分配,改进了最大化最小(max-min)用户速率模型。在功率分配中将系统模型转化成用注水线表示的数学模型,首先求解各用户的注水线,再求解各用户的功率分配,保证了用户间比例公平性。两种信噪比情形下的仿真和分析表明,整个方案计算复杂度稍低,系统容量获得较大提升,并且用户间的公平性始终为1。     

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。     

OFDM系统中一种多用户自适应资源分配算法

针对OFDMA系统中各个用户传输速率受限的情况下,最大化系统容量问题,提出了一种资源分配的实现算法.该算法首先根据每个用户发送的比特数目要求确定用户需要占用的子载波数,将最优的若干个子信道分配给用户后,通过比较各个用户内部子信道之间的差异调整被几个用户同时占用的子信道.最后用赋初值的贪心算法为各个用户分配比特、功率.仿真结果表明,该算法复杂度较低,系统容量较大,且实现了用户之间的公平性.     

认知OFDM系统中针对视频业务用户的资源分配

在多用户认知无线电OFDM系统中,针对实时视频业务用户,提出一种计算复杂度低的资源分配方案.该方案采用鱼群算法分配子载波,并提出简单功率干扰(Simple Power Interference,SPI)约束功率分配算法.目标是在满足总功率预算并且保证不干扰主用户的前提下,最大化系统的下行系统容量.仿真分析表明,在视频业务用户场景中,所提算法能有效提高下行系统速率,性能接近最优且复杂度低.     

Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints

Multiuser orthogonal frequency division multiplexing (MU-OFDM) is a promising technique for achieving high downlink capacities in future cellular and wireless local area network (LAN) systems. The sum capacity of MU-OFDM is maximized when each subchannel is assigned to the user with the best channel-to-noise ratio for that subchannel, with power subsequently distributed by water-filling. However, fairness among the users cannot generally be achieved with such a scheme. In this paper, a set of proportional fairness constraints is imposed to assure that each user can achieve a required data rate, as in a system with quality of service guarantees. Since the optimal solution to the constrained fairness problem is extremely computationally complex to obtain, a low-complexity suboptimal algorithm that separates subchannel allocation and power allocation is proposed. In the proposed algorithm, subchannel allocation is first performed by assuming an equal power distribution. An optimal power allocation algorithm then maximizes the sum capacity while maintaining proportional fairness. The proposed algorithm is shown to achieve about 95% of the optimal capacity in a two-user system, while reducing the complexity from exponential to linear in the number of subchannels. It is also shown that with the proposed resource allocation algorithm, the sum capacity is distributed more fairly and flexibly among users than the sum capacity maximization method.     

Proportional-fair energy-efficient radio resource allocation for OFDMA smallcell networks

This paper investigates the energy-efficient radio resource allocation problem of the uplink smallcell networks. Different from the existing literatures which focus on improving the energy efficiency (EE) or providing fairness measured by data rates, this paper aims to provide fairness guarantee in terms of EE and achieve EE-based proportional fairness among all users in smallcell networks. Specifically, EE-based global proportional fairness utility optimization problem is formulated, taking into account each user’s quality of service, and the cross-tier interference limitation to ensure the macrocell transmission. Instead of dealing with the problem in forms of sum of logarithms directly, the problem is transformed into a form of sum of ratios firstly. Then, a two-step scheme which solves the subchannel and power allocation separately is adopted, and the corresponding subchannel allocation algorithm and power allocation algorithm are devised, respectively. The subchannel allocation algorithm is heuristic, but can achieve close-to-optimal performance with much lower complexity. The power allocation scheme is optimal, and is derived based on a novel method which can solve the sum of ratios problems efficiently. Numerical results verify the effectiveness of the proposed algorithms, especially the capability of EE fairness provisioning. Specifically, it is suggested that the proposed algorithms can improve the fairness level among smallcell users by 150–400 % compared to the existing algorithms.     

Resource allocation with proportional rate fairness in orthogonal frequency division multiple access relay networks

We address the problem of subchannel and transmission power allocation in orthogonal frequency division multiple access relay networks with an aim to maximize the sum rate and maintain proportional rate fairness among users. Because the formulated problem is a mixed‐integer nonlinear optimization problem with an extremely high computational complexity, we propose a low‐complexity suboptimal algorithm, which is a two‐step separated subchannel and power allocation algorithm. In the first step, subchannels are allocated to each user, whereas in the second step, the optimal power allocation is carried out on the basis of the given subchannel allocation and the nonlinear interval Gauss–Seidel method. Simulation results have demonstrated that the proposed algorithm can achieve a good trade‐off between the efficiency and the fairness compared with two other existing relevant algorithms. In particular, the proposed algorithm can always achieve 100% fairness under various conditions. Copyright © 2012 John Wiley & Sons, Ltd.     

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.     

On the use of immune clonal optimization for joint subcarrier and power allocation in OFDMA with proportional fairness rate

Resource allocation in orthogonal frequency division multiple access is a constraint optimization problem. In this paper, we concentrate on maximizing both the sum capacity and proportional fairness rate among users. The optimization model of resource allocation is formulated and then an immune clonalbased algorithm is proposed for it. The resource allocation is solved by separating the subcarrier and power allocation in two steps. Suitable immune operators are designed, such as clonal, mutation, Baldwin learning, selection and so on. Experiments show that, compared with the previous methods, the proposed algorithm obtains higher sum capacity with comparable computational complexity, and keeps the proportional rate more fairly among users. Copyright © 2012 John Wiley & Sons, Ltd.     

Fairness based resource allocation for multiuser MISO-OFDMA systems with beamforming

Resource allocation problem in multiuser multiple input single output-orthogonal frequency division multiple access (MISO-OFDMA) systems with downlink beamforming for frequency selective fading channels is studied. The article aims at maximizing system throughput with the constraints of total power and bit error rate (BER) while supporting fairness among users. The downlink proportional fairness (PF) scheduling problem is reformulated as a maximization of the sum of logarithmic user data rate. From necessary conditions on optimality obtained analytically by Karush-Kuhn-Tucker (KKT) condition, an efficient user selection and resource allocation algorithm is proposed. The computer simulations reveal that the proposed algorithm achieves tradeoff between system throughput and fairness among users.     

Joint subchannel and power allocation in multiuser OFDM systems with minimal rate constraints

In this paper, we study the adaptive resource allocation in multiuser orthogonal frequency division multiplexing (OFDM) systems. We try to maximize the sum capacity of an OFDM system with given transmission power budget, while meeting users' minimal rate requirements. Unlike other resource allocation schemes, which generally separate subchannel allocation and power distribution into independent procedures, our proposed algorithm implements joint subchannel and power allocation. Given a set of subchannels, the required power to satisfy a user's minimal rate constraint is calculated by water‐filling policy. Then, the user who requires the maximum power to meet the rate requirement has a priority to obtain an additional subchannel. The procedure continues until all subchannels are consumed, by which time the consumed power to meet all users' rate requirements is also worked out. Finally, the margin power is allocated among all subchannels in an optimal manner to maximize the sum capacity of the OFDM system. Simulation results show that our proposed algorithm performs better than other existing ones. The solution produced by our proposed algorithm is close to the upper bound, while its complexity is relatively lower compared with other methods, which makes it attractive for applications. Copyright © 2012 John Wiley & Sons, Ltd.     

基于保密度的OFDMA中继网络资源分配研究

考虑到异构双向中继网络中存在窃听者的安全资源分配问题,为了提高中继安全性,该文研究了受限于子信道分配和功率约束的用户安全保密度问题模型,与传统的保密容量模型相比,安全保密度模型更侧重于反映用户本身的安全程度。基于此保密度模型,该文进一步考虑了不同用户的安全服务质量(Quality of Service, QoS)需求和网络公平性,联合优化功率分配、子信道分配、子载波配对,并分别通过约束型粒子群、二进制约束型粒子群优化算法和经典的匈牙利算法找到最优解,实现资源的最优分配,提高网络中合法用户的保密度。仿真结果验证了所提算法的有效性。     

Resource allocation for multiuser cognitive OFDM networks with proportional rate constraints

In this paper we study the resource allocation problem for the multiuser orthogonal frequency division multiplexing (OFDM)‐based cognitive radio (CR) systems with proportional rate constraints. The mutual interference introduced by primary user (PU) and cognitive radio user (also referred to secondary user, SU) makes the optimization problem of CR systems more complex. Moreover, the interference introduced to PUs must be kept under a given threshold. In this paper, the highest achievable rate of each OFDM subchannel is calculated by jointly considering the channel gain and interference level. First, a subchannel is assigned to the SU with the highest achievable rate. The remaining subchannels are always allocated to the SU that suffers the severest unjustness. Second, an efficient bit allocation algorithm is developed to maximize the sum capacity, which is again based on the highest achievable rate of each subchannel. Finally, an adjustment procedure is designed to maintain proportional fairness. Simulation results show that the proposed algorithm maximizes the sum capacity while keeping the proportional rate constraints satisfied. The algorithm exhibits a good tradeoff between sum capacity maximization and proportional fairness. Furthermore, the proposed algorithm has lower complexity compared with other algorithms, rendering it promising for practical applications. Copyright © 2011 John Wiley & Sons, Ltd.     

优化公平性的多用户OFDM系统下行链路资源分配算法

研究了优化公平性的多用户OFDM系统下行链路的资源分配算法,根据系统各用户的业务需求,在保证用户所得数据速率满足一定比例以及系统总功率限制的前提下,提高系统总数据速率。首先,根据公平性原则进行用户的子载波分配,子载波功率分配使用注水算法;子载波分配完成后,利用贪婪功率分配算法,以最大化用户数据速率和提高功率利用率为目标,对各用户内部子载波功率和比特数进行再分配。仿真结果表明,相比参考文献[10]的算法,该算法在提高系统总速率的同时,更好地保证了用户数据速率的公平性;相比参考文献[12]的算法,该算法虽然牺牲了一定的系统总速率,但能提供更高的用户数据速率公平性。