Adaptive power allocation with quality-of-service guarantee in cognitive radio networks

This paper proposes a novel power control policy for a cognitive radio network as an effort to maximize throughput under the average interference power constraint. The underlined policy ensures delay-related quality of service (QoS) requirements with reduced interference to the primary user. In this work we also take into account the peak and average transmit power constraints for the secondary user. An optimization problem associated with the power control policy is formulated based on a cross-layer framework, where the queue on data link layer is serviced by the power control policy at the physical layer. A recursive algorithm under the power constraints is developed to solve for the optimal solution. It is shown that the reduction of average interference to the primary user is related closely to the QoS requirements. The analysis derives the average interference power limits to the primary user in fading channels with guaranteed QoS requirements for the secondary user. The numerical results show the effectiveness of the proposed power control policy.     

一种公平的认知无线电系统下行频谱分配策略

目前关于认知无线电频谱分配的研究大多以最大化系统容量为目标,很少考虑认知用户的QoS需求,频谱分配方案设计缺乏公平性。针对主用户干扰限制和保障认知用户QoS需求,结合OFDM技术,研究了认知无线电场景中下行多用户分配算法。该算法构建不等式约束下的目标函数以最大化系统容量,通过拉格朗日(Lagrange)对偶优化法给出近似最优解。仿真结果表明,所提算法在牺牲系统和容量的前提下充分保证了次用户的QoS需求,提高了系统的公平性。     

Cross-layer optimization policy for QoS scheduling in computational grid

This paper presents a cross-layer quality of service (QoS) optimization policy for computational grid. Efficient QoS management is critical for computational grid to meet heterogeneity and dynamics of resources and users’ requirements. There are different QoS metrics at different layers of computational grid. To improve perceived QoS by end users over computational grid, QoS supports can be addressed in different layers, including application layer, collective layer, fabric layer and so forth. The paper tackles cross-layer grid QoS optimization as optimization decomposition, each layer corresponds to a decomposed subproblem. The proposed policy produces an optimal set of grid resources, service compositions and user's payments at the fabric layer, collective layer and application layer respectively to maximize global grid QoS. The cross-layer optimization problem decomposes into three subproblems: grid resource allocation problem, service composing and user satisfaction degree maximization problem, all of which interact through the optimal variables for capacities of grid resources and service demand. In order to coordinate the subproblems, cross-layer QoS feedback mechanism is established to ensure different layer interactions. The simulations are conducted to validate the efficiency of the proposed policy.     

Failure-aware resource provisioning for hybrid Cloud infrastructure

Hybrid Cloud computing is receiving increasing attention in recent days. In order to realize the full potential of the hybrid Cloud platform, an architectural framework for efficiently coupling public and private Clouds is necessary. As resource failures due to the increasing functionality and complexity of hybrid Cloud computing are inevitable, a failure-aware resource provisioning algorithm that is capable of attending to the end-users quality of service (QoS) requirements is paramount. In this paper, we propose a scalable hybrid Cloud infrastructure as well as resource provisioning policies to assure QoS targets of the users. The proposed policies take into account the workload model and the failure correlations to redirect users’ requests to the appropriate Cloud providers. Using real failure traces and a workload model, we evaluate the proposed resource provisioning policies to demonstrate their performance, cost as well as performance–cost efficiency. Simulation results reveal that in a realistic working condition while adopting user estimates for the requests in the provisioning policies, we are able to improve the users’ QoS about 32% in terms of deadline violation rate and 57% in terms of slowdown with a limited cost on a public Cloud.     

Resource management policies for fixed relays in cellular networks

Mobile stations in the cell boundary experience poor spectral efficiency due to the path loss and interference from adjacent cells. Therefore, satisfying QoS requirements of each MS at the cell boundary has been an important issue. To resolve this spectral efficiency problem at the cell boundary, deploying fixed relay stations has been actively considered. In this paper, we consider radio resource management policies concerned with fixed relays that include path selection rules, frequency reuse pattern matching, and frame transmission pattern matching among cells. We evaluate performance of each policy by varying parameter values such as relay station’s position and frequency reuse factor. Through Monte Carlo simulations and mathematical analysis, we suggest some optimal parameter values for each policy and discuss some implementation issues that need to be considered in practical deployment of relay stations.     

基于认知无线电的QoS路由算法

一般无线网络的路由协议不适合直接应用于认知无线电网络,需要提出一个新的路由算法以满足认知无线电网络端到端的服务质量(QoS)性能。认知无线电网络中的节点可以自主地进行信道选择和频谱切换,从而最大限度地满足流量需求和避免流内竞争。结合按需路由的基本流程,在充分考虑信道容量和流内竞争所带来的干扰对路由选择的影响下,提出了适用于认知无线电网络的基于容量和干扰的路由算法。仿真结果表明,基于容量和干扰的路由算法比另两种路由算法具有更好的端到端QoS性能。     

Spectrally efficient emission mask shaping for OFDM cognitive radios

Orthogonal Frequency Division Multiplexing has been widely adopted in recent years due to its inherent spectral efficiency and robustness to impulsive noise and fading. For cognitive radio applications in particular, it can enable flexible and agile spectrum allocation, yet suffers from spectral leakage in the form of large side lobes, leading to inter-channel interference unless mitigated carefully. Hence, recent OFDM-based standards such as IEEE 802.11p for vehicular communication and IEEE 802.11af for TV whitespace reuse impose strict spectrum emission mask limits to combat adjacent channel interference. Stricter masks allow channels to operate closer together, improving spectral efficiency at the cost of implementation difficulty. Meeting these strict limits is a significant challenge for implementing both 802.11p and 802.11af, yet remains an important requirement for enabling cost-effective systems. This paper proposes a novel method that embeds baseband filtering within a cognitive radio architecture to meet the specification for the most stringent 802.11p and 802.11af masks, while allowing up to ten additional active 802.11af sub-carriers to occupy a single basic channel without violating SEM specifications. The proposed method, performed at baseband, relaxes otherwise strict RF filter requirements, allowing the RF subsystem to be implemented using much less stringent 802.11a designs, resulting in cost reductions.     

基于非合作博弈的改进型认知无线电功控算法

在认知无线电网络中,当认知用户（CU）与主用户（PU）共享频谱带宽时,需要对认知用户的发射功率进行控制,以确保认知用户在满足自己的QoS且不影响主用户的前提下与主用户共享频谱带宽。利用博弈论的方法,设计了一种基于链路增益和干扰温度的代价函数,并据此提出了一种改进型非合作博弈功率控制算法（IPC-NG）。通过数学理论推导分析,证明了新的净效用函数存在纳什均衡且均衡点唯一,同时还分析了该算法的收敛速度。仿真结果表明,该算法不仅避免了对主用户的影响,也保证了每个CU的QoS需求,同时还提高了认知系统的吞吐量和净效用。     

Game theory-based resource management strategy for cognitive radio networks

This paper investigates the application of game theory tools in the context of cognitive radio networks (CRN). Specifically, we propose a resource management strategy with the objective to maximize a defined utility function subject to minimize the mutual interference caused by secondary users (SUs) with protection for primary users (PUs). In fact, we formulate a utility function to reflect the needs of PUs by verifying the outage probability constraint, and the per-user capacity by satisfying the signal-to-noise and interference ratio (SNIR) constraint, as well as to limit interference to PUs. Furthermore, the existence of the Nash equilibrium of the proposed game is established, as well as its uniqueness under some sufficient conditions. Theoretical and simulation results based on a realistic network setting, and a comparison with a previously published resource management method are provided in this paper. The reported results demonstrate the efficiency of the proposed technique in terms of CRN deployment while maintaining quality-of-service (QoS) for the primary system.     

基于分级的频谱切换技术研究

为解决认知无线电网络中频谱切换机制问题,以提高连接保持概率和频谱利用率,降低频谱切换延迟,综合考虑了频谱质量和次用户的业务差异性,将可用频谱资源进行分类并建立起两种使用模型;结合主用户出现概率、信道选择机制和冲突避免算法等因素,提出了一个完整的基于分级的频谱切换机制。仿真结果表明,该机制使得用户在切换时能够快速且无冲突地找到目标信道,有效地保证了主用户和次用户的服务质量。     

认知无线网络中动态频谱接入性能分析

认知无线电技术利用频谱空洞进行通信,有效缓解了频谱资源紧缺问题,动态频谱接入是其核心技术。网络中主用户对授权频谱的使用效率较高时,次用户接入网络无法完成符合QoS要求的通信,只有当主用户频谱效率在一定门限值下时网络才适合次用户接入。有限频谱空洞资源只能满足有限次用户的通信需求,为了保证通信质量,网络在固定的主用户频谱效率下只能接入适量的次用户。提出用强制优先排队理论对认知无线网络中的动态频谱接入过程进行模拟,通过仿真对次用户的切换概率、阻塞概率两个QoS因子进行分析,在给定的QoS条件下,得到了网络适合次用户接入的主用户频谱效率门限值,以及在固定的主用户频谱效率下网络适合接入次用户的量。     

混合保护准则下基于NC-OFDM的认知无线电系统功率分配

针对主用户采用OFDM通信体制,认知用户采用NC-OFDM通信体制,认知用户共享主用户授权频谱的工作场景,研究了认知无线电系统功率分配策略。提出依据主用户不同工作状态,采用不同的约束准则:对认知用户工作信道,引入功率泄漏干扰因子约束认知用户的发射功率,以限制对主用户工作信道的泄漏功率强度;对主用户工作信道,设置主用户速率损失上限,以确保主用户正常工作。功率分配算法以混合约束准则为基础,最大化认知无线电系统的传输速率。仿真结果表明,与干扰功率约束机制相比较,本算法能显著提升传输速率。     

认知无线电中能效优化的子载波匹配策略

针对协作认知无线电系统中的能量效率问题,提出一种以最大化能量效率为目标的资源联合分配算法。在满足服务质量要求和功率约束的情况下,首先通过一种能量效率优先的启发式方案对子载波进行匹配,再引入基于拉格朗日对偶算法对其中的功率分配问题进行非线性优化,从而最大限度地提高整个系统的能量效率。仿真实验结果表明,所提算法的能量效率得到显著提升,验证了所提方案的有效性。     

多用户MIMO无线通信系统跨层调度研究

无线频谱资源日益显得匮乏,而当今的无线通信系统需要为用户提供端到端的Q o S保障。为了在高速动态变化的无线环境下实现尽可能高的频谱利用率和Q o S保障,系统的几个层要联合运作才能达成上述两个目标。在分组网络中,实现这些目标的一个重要途径是设计适当的调度算法。本文根据信息论的框架,对分组调度算法的设计问题进行简要的介绍。然后重点介绍资源分配问题中跨层方法的优点,并对反馈信息量增加问题的折中方案进行分析。同时,对多发射多接收系统能够提供的额外自由度问题进行深入的讨论。     

RAC: Range Adaptive Cognitive Radio Networks

In recent years, cognitive radio has received a great attention due to tremendous potential to improve the utilization of the radio spectrum by efficiently reusing and sharing the licensed spectrum bands, as long as the interference power inflicted on the primary users of the band remains below a predefined threshold level. Cognitive radio allows the secondary users in the cognitive radio network to access the licensed spectrum of the primary users opportunistically. In this paper, an autonomous distributed adaptive transmission range control scheme for cognitive radio networks which is called the RAC is proposed. The RAC considers the QoS requirements of both the primary and the secondary users simultaneously. The cognitive user's maximization of its achievable throughput without interfering the primary user by adapting transmission range of the secondary users dynamically is the key feature of the RAC. One of the advantages of using the proposed scheme is its implementation simplicity. The RAC is compared to other cognitive radio schemes in a simulation environment by using ns2. Simulations indicate that, the RAC can well fit into the mobile cognitive radio ad hoc networks and improve the network performance. Having compared to the other schemes utilizing contemporary cognitive radio technology, the RAC provides better adaptability to the environment and maximizes throughput and minimizes data delivery latency.     

A cognitive management framework for spectrum selection

To increase cognitive radio (CR) operation efficiency, there has been an interest in enhancing the awareness level of spectrum utilization. In this context, this paper builds a new cognitive management functional architecture for spectrum selection (SS). It relies on a knowledge manager (KM) retaining a set of advanced statistics that track the suitability of spectral resources to support a set of heterogeneous applications under varying interference conditions. Based on this architecture, a novel proactive strategy is proposed for both SS and spectrum mobility (SM) functionalities. The required interactions between the proposed decision-making processes are described, and their capability to exhibit robustness to unexpected changes in the radio environment is highlighted. The results show that the proposed strategy efficiently exploits the KM support for low loads, while the SM functionality introduces significant gains for high loads with respect to other strategies. Finally, to assess the practicality of the proposed approach, the signaling requirements in the radio interface are evaluated.     

认知无线网络中基于信誉度管理的动态频谱接入研究

随着无线服务和相关设备的飞速发展,认知无线网络中特有频谱稀缺问题越来越引起研究者的重视。在集中式认知无线网络中,次级用户基站SUBS作为融合中心,通过收到周围的次级用户的感知信息来分配频谱资源。然而,环境的易变性使次级用户容易受到攻击从而影响次级用户感知信息,导致网络频谱资源分配错误。引入信誉度模型来表现次级用户在认知循环中的行为规范,在分配频谱阶段将信誉度作为评定标准,鼓励次级用户积极感知及规范运行。在感知阶段,次级用户感知信道数越多,感知信息越正确,其信誉度越高。在运行阶段,次级用户行为越符合网络规范,则信誉度越高。仿真结果表明,论文模型可以很好地减少次级用户基站的错误决策次数,提高其抗攻击性,同时使得网络在很好地分配资源的同时鼓励整个网络行为积极化。     

认知中继网络功率分配的优化算法

本文研究了认知中继网络的功率分配策略。在基于AF(amplify-and-forward)和DF(decode-and-forward)中继模式下,考虑认知用户传输功率受限以及主用户干扰容限等因素,建立了认知中继网络吞吐量的非凸函数和凸函数优化模型;并利用拉格朗日乘子得到最优解。仿真结果表明,所提算法在认知用户传输功率受限以及主用户干扰容限下优化了认知中继网络的吞吐量。     

无线网状网中基于TDMA模式的QoS路由策略

为支持无线网状网中具有服务质量（QoS）需求的实时应用,提出基于TDMA模式服务质量路由策略TD-MQR。对网络进行分层,提出新的链路指标值,在考虑链路容量和无线信道间干扰的基础上对网络的流量进行路由和调度。仿真表明,该协议能够对WMN中实时应用提供QoS保证。     

Multi-level scheduling for global optimization in grid computing

The paper presents a multi-level scheduling algorithm for global optimization in grid computing. This algorithm provides a global optimization through a cross-layer optimization realized by decomposing the optimization problem in different sub-problems each of them corresponding to one among the grid layers such as application layer, collective layer and fabric layer. The QoS of an abstraction level is a utility function that assigns at every level a different value and that depends on the kind of task that is executed on the grid. The global QoS is given by processing of the utility function values of the three different levels, using the Lagrangian method. Multi-level QoS scheduling algorithm is evaluated in terms of system efficiency and their economic efficiency, respectively. Economic efficiency includes user utility, service provider’s revenue and grid global utility. System efficiency includes execution success ratio and resource allocation ratio.