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。     

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。     

无线视频通信中带宽分配策略

本文针对无线网络中多用户传输视频时带宽分配问题,提出了一种基于微观经济学中供需平衡的带宽分配策略。该方法充分考虑了运营商的收益,在运营商和用户收益同时最大且供需平衡时,所求得的频谱价格即为运营商的出售价格。同时给出了针对不同用户等级有不同的用户优先级因子,据运营商提出的带宽价格,彼此相互博弈,最终使得自身效用最大。仿真结果表明:本文提出的博弈论方法存在纳什均衡解和均衡单价;用户等级因子能按照用户的不同等级来分配带宽;系统传输质量相比以往方法有所提高。      

一种femto-macro异构网络多业务资源公平管理算法

摘要：针对毫微微蜂窝（femtocell）网络中多种业务（固定速率业务和可变速率业务）环境下如何公平分配毫微微蜂窝基站（femtocell base station, FBS）资源的问题,提出了一种基于比例公平算法的femto-macro异构网络资源分配算法。该算法以基于比例公平法则的可变速率用户的吞吐量为优化目标,并以每个用户的服务质量（quality of service, QoS）、FBS的下行传输总功率和宏蜂窝（macrocell）用户的跨层同频干扰门限值为约束组成优化问题。在上述资源分配最优化问题为凸优化问题的基础上,采用对偶分解算法进行求解。仿真结果表明,提出的算法在保证不同用户 QoS 的同时,不仅能够有效地公平分配资源给可变速率用户,而且降低了macrocell用户受到来自FBS的跨层同频干扰。     

Adaptive Subcarrier and Power Allocation with Fairness for Multi-user Space-Time Block-Coded OFDM System

This paper investigates the subcarrier and power allocation problems of multi-user space-time block coded OFDM based cellular systems. Based on the tradeoff between the number of assigned subcarriers and the amount of allocated power for users, a less complexity algorithm that separates subcarrier allocation and power allocation is proposed. Simulation results show that the proposed resource allocation algorithm can improve the capacity significantly compared with static FDMA fixed allocation algorithm and the MIMO-OFDMA scheme, and the more important thing is that it can make the capacity be distributed more fairly, very close to the ideal rate constraints, among users than the scheme which maximizes the system capacity only.     

Coalition-based downlink resource allocation for LTE system with divide-and-conquer approach

To take advantage of the multiuser diversity resulted from the variation in channel conditions among the users,it has become an interesting and challenging problem to efficiently allocate the resources such as subcarriers,bits,and power.Most of current research concentrates on solving the resource-allocation problem for all users together in a centralized way,which brings about high computational complexity and makes it impractical for real system.Therefore,a coalitional game framework for downlink multi-user resource allocation in long term evolution(LTE) system is proposed,based on the divide-and-conquer idea.The goal is to maximize the overall system data rate under the constraints of each user’s minimal rate requirement and maximal transmit power of base station while considering the fairness among users.In this framework,a coalitional formation algorithm is proposed to achieve optimal coalition formation and a two-user bargaining algorithm is designed to bargain channel assignment between two users.The total computational complexity is greatly reduced in comparison with conventional methods.The simulation results show that the proposed algorithms acquire a good tradeoff between the overall system throughout and fairness,compared to maximal rate and max-min schemes.     

Resource allocation for multiuser two-way OFDMA relay networks with user rate constraints

This paper studies the resource allocation for a multi-user two-way amplify-and-forward (AF) relay network over orthogonal frequency-division multiplexing (OFDM) technology,where all users communicate with their pre-assigned partners.Using convex optimization techniques,an optimal solution tominimize the total transmit power while satisfy each user-pair’s data rate requirements is proposed.We divide the resource allocation problem into two subproblems:(1) power optimization within user-pair and relay in each subcarrier.(2) optimal subcarrier allocation and sum power assignment among N parallel OFDM subcarriers.Closed-form expressions of the power among user-pair and relay can be obtained in subproblem (1),and so the proposed algorithm decreases the variable dimensionality of the objective function to reduce the complexity of this optimization problem.To solve it,a three-step suboptimal approach is proposed to assign the resources to user-pairs:Firstly,decompose each user-pair into two sub user-pairs which have one-way and two-way relaying transmission modes.Secondly,allocate the subcarriers to the new mode user-pairs and assign the transmit power to each carrier.Thirdly,distribute the assigned power to three nodes allocated in the subcarrier.Simulation results demonstrate the significant power is saved with the proposed solutions,as compared to a fixed subcarrier allocation.     

Power Allocation with Max–Min and Min–Max Fairness for Cognitive Radio Networks with Imperfect CSI

This paper consider the power allocation strategies in the cognitive radio (CR) system in the presence of channel estimation errors. As the user has different channel condition in CR systems, different amount of power resource is required to meets the QoS request. In order to guarantee the fairness of each CR user, ensure the interference from the primary user and other CR users meet the QoS requirement of the CR user and limit the interference that is caused by CR users on primary user within the range into the level that primary user can tolerate, we proposed some new power allocation schemes. The targets are to minimize the maximum power allocated to CR users, to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all CR users and to minimize the maximum outage probability over all CR users. The first power allocation scheme can be formulated using Geometric Programming (GP). Since GP problem is equivalent to the convex optimization problem, we can obtain the optimal solutions for the first scheme. The latter two power allocation schemes are not GP problems. We propose iterative algorithms to solve them. Simulation results show that proposed schemes can efficiently guarantee the fairness of CR users under the QoS constraint of the primary user and CR users.     

改进的多用户OFDM系统子载波和比特分配算法

针对多用户正交频分复用（OFDM）系统资源分配问题,提出了一种改进的基于边缘自适应（MA）准则的子载波和比特分配算法。在采用比例公平准则为每个用户分配子载波集合基础上,以用户速率最大者-最小者（Max-Min）子载波交换为原则进行子载波调整,使用户功率递减同时兼顾用户的公平性;通过对信道状态信息进行判断,利用贪婪算法将用户子载波分配的比特取整,以实现系统功率最小化。实验结果表明,本文提出的改进次优算法的计算复杂度较传统分步算法稍高,但仍远低于最优算法,其系统性能得以提升,且接近最优算法。     

基于社会福利最大化的柔性业务资源分配研究

对经济学方法在无线资源管理中的应用进行了研究,考虑业务、用户、资源等多个域,将无线资源分配看作生产–消费模型,兼顾用户公平性原则,针对不同业务的QoS（quality of service）要求采用不同的资源分配方法,建立了基于社会福利最大化的资源分配模型。采用基于用户柔性业务的调度算法优化所提模型,综合考虑用户效用、网络效益以及运营商收益,实现了基于社会福利最大化的柔性业务资源分配。仿真结果验证了所提算法的优越性。     

Fair QoS multi-resource allocation for uplink traffic in WLAN

In wireless local area network (WLAN), improving the quality of service (QoS) of users is often at odd with striking fairness among users. In this work, we suggest that in WLAN, multiple types of network resources should be jointly allocated to users to achieve “QoS fairness”, which is a new fairness concept targeting at balancing QoS and fairness in WLAN by allocating multiple types of network resources to users. To this end, we first transform user QoS requirements to multi-resource demands and apply the dominant resource fairness scheme to allocate network resources for each user. We prove several salient QoS-based fairness properties based on a model mapping between QoS and resources. We further discuss about more general conditions for diverse mapping models where QoS fairness properties can be satisfied. We find that the QoS fairness properties can be guaranteed as long as the mapping model meets a few practical requirements, indicating the wide applicability of our scheme. To consolidate our multi-resource allocation scheme, we design a practical protocol for WLAN. The simulation results validate that the QoS fairness can be guaranteed in practical WLAN scenario.     

基于用户分簇的认知超密集网络资源分配

针对5G时代小基站的密集部署带来的复杂干扰问题,对下行的认知无线电超密集网络下的资源分配进行了研究。为减小网络干扰,提高次用户吞吐量,提出了一种改进的基于用户分簇的资源分配算法。基于基站的覆盖范围,选出用户的强干扰基站,以用户-基站干扰关系建立用户-用户干扰图,按用户受到的平均弱干扰划分优先级对用户分簇,再为簇集群预分配频段,为每个簇分配对应频段中效用最大的信道。该资源分配算法能准确反映用户间的干扰关系,保障资源分配公平性。仿真结果表明,当用户密度与基站密度均较大时,与相同场景的已有算法相比,该改进算法有较好的抗干扰能力,能有效提高次用户的吞吐量。     

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.     

Max-utility subcarrier allocation for heterogeneous services in wireless OFDM system

In future wireless network, one user will require multiple homogeneous or heterogeneous services simultaneously. Then, the scheduling algorithm is not only responsible for assigning a resource block to different users but also sharing the assigned resource block among multiple services for one user. Most of the traditional scheduling algorithms are designed to serve one service per user, and cannot be applied directly to this scenario because of the fairness criterion. This article focuses on adaptive resource allocation for multiple services per user at the downlink of orthogonal frequency division multiplexing (OFDM) based system. This article addresses this integrative resource scheduling problem based on utility function. First, the optimal algorithm for dynamic subcarrier allocation and share is deduced for homogeneous best-effort service system. Then the algorithm is extended to heterogeneous services system by classifying the delay sensitive service according to the head-of-line packet delay. The design goal is to maximize aggregate utility function to exploit multiuser diversity gain to the greatest extent even as guaranteeing quality of service (QoS) for delay sensitive service.     

Utility-based resource allocation and scheduling in OFDM-based wireless broadband networks

This article discusses downlink resource allocation and scheduling for OFDM-based broadband wireless networks. We present a cross-layer resource management framework leveraged by utility optimization. It includes utility-based resource management and QoS architecture, resource allocation algorithms, rate-based and delay-based multichannel scheduling that exploits wireless channel and queue information, and theoretical exploration of the fundamental mechanisms in wireless resource management, such as capacity, fairness, and stability. We also provide a solution that can efficiently allocate resources for heterogeneous traffic with diverse QoS requirements.     

鱼群算法在多用户OFDM跨层资源分配中的应用

基于人工鱼群算法,利用已知信道信息对多用OFDM系统中的资源进行跨层分配.本文提出了新的目标函数,合理有效地找到了总传输速率最大化和实现用户的速率要求、比例公平要求的权衡点.为了减小资源分配的复杂度,本文提出首先对子载波进行分配,然后进行功率分配.仿真结果表明,人工鱼群算法的很好的解决了多用户OFDM系统中的跨层资源分...     

Proportional-fairness resource allocation for uplink OFDM-based cognitive radio systems

This paper has proposed a proportional-fairness resource allocation algorithm, including both subcarrier assignment algorithm and power allocation algorithm, for uplink orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems. First, to get a better performance in the proposed system model, the influence factor (a,b,c) was introduced to realize the assignment of the subcarriers. Second, the transmit power of the secondary users (SUs) was allocated to the corresponding subcarriers in order to maximize the uplink capacity of the SUs subject to both power and interference constraints. With the appropriate influence factor in the subcarrier assignment, the loss of transmitted data rate arising from the fairness was minimized. Simulation results showed that the proposed algorithm can achieve a perfect fairness among the SUs while maximizing the system capacity simultaneously, and is of a low computation complexity.     

Fair Multiuser Channel Allocation for OFDMA Networks Using Nash Bargaining Solutions and Coalitions

In this paper, a fair scheme to allocate subcarrier, rate, and power for multiuser orthogonal frequency-division multiple-access systems is proposed. The problem is to maximize the overall system rate, under each user's maximal power and minimal rate constraints, while considering the fairness among users. The approach considers a new fairness criterion, which is a generalized proportional fairness based on Nash bargaining solutions and coalitions. First, a two-user algorithm is developed to bargain subcarrier usage between two users. Then a multiuser bargaining algorithm is developed based on optimal coalition pairs among users. The simulation results show that the proposed algorithms not only provide fair resource allocation among users, but also have a comparable overall system rate with the scheme maximizing the total rate without considering fairness. They also have much higher rates than that of the scheme with max-min fairness. Moreover, the proposed iterative fast implementation has the complexity for each iteration of only$O(K^2Nlog_2 N+K^4)$, where$N$is the number of subcarriers and$K$is the number of users.     

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.     

基于非合作博弈论的多小区OFDMA系统动态资源分配算法研究

该文采用非合作博弈论的方法研究了多小区OFDMA系统中的动态资源分配问题,首先将各基站的发射功率平均分配给各子载波,然后由所有小区在每个子载波上独立地进行资源分配博弈,给出了用户调度与功率分配联合博弈框架。为了进一步简化,将用户调度和资源分配分开完成,通过将信道增益引入到定价函数中,提出了一种新的定价机制,建立了用户确定时的非合作功率分配博弈模型,分析了其纳什均衡的存在性和唯一性,并设计了具体的博弈算法。仿真结果表明,所提算法在保证吞吐量性能的同时,进一步提升了系统的公平性。