联合考虑主用户QoS和安全约束下认知NOMA安全传输

本文研究了认知非正交多址接入(NOMA)网络中联合考虑主用户服务质量(QoS)约束和安全约束下次用户物理层安全传输.该网络中采用认知功率分配策略,优先保障主用户的QoS和安全性能.在此功率分配方案下,推导得出了次用户连接中断概率(COP)和安全中断概率(SOP)的闭式表达式,揭示了系统参数配置对次用户传输可靠性和安全性的影响关系,以及安全性-可靠性之间的折衷关系.仿真结果表明,较强的主用户安全约束恶化了次用户的安全性能,但是提升了次用户的安全-可靠折衷性能.     

基于最佳中继选择的异构多媒体自适应协作分集算法

本文提出一种自适应协作分集方案,以保证异构多媒体业务中主用户传输的服务质量(QoS),并改善次级用户的数据传输性能。采用基于功率控制的系统模型,使得主用户的平均信道增益相对稳定,并且能够在次用户中进行估计,可以节省在反馈信道上的资源。根据次级用户传输过程中的中断概率提出次级中断概率的闭式表达式 ,在满足主用户传输过程中所需要的中断概率约束的条件下,针对主用户和次用户的协作性和瑞利衰落信道的适应性提出了最佳中继选择的自适应协作分集,尽可能地次级中断概率,并获得的分集增益。实验仿真结果表明,该算法可以减少次级用户的中断概率并提高频谱效率,有助于提升次级用户的数据传输性能。     

Distributed power control in cooperative cognitive ad hoc networks

In this paper, a relay selection strategy and distributed power control algorithm are proposed for the underlay spectrum sharing mode based cooperative cognitive ad hoc network with energy-limited users. The study aims to minimize the total power consumption of cooperative cognitive ad hoc network while ensuring the quality of service (QoS) requirement of cognitive user and keeping the interference to primary user below interference tolerance. The power control problem is transformed into a convex optimization problem. Based on Lagrange dual decomposition theory, a gradient iterative algorithm is constructed to search for the optimal solution and complete distributed power optimization. Simulation results show that the algorithm converges fast and reduces transmit power of cognitive users effectively while guaranteeing the QoS requirement.     

一种认知无线电网络接入控制和功率控制联合设计

在underlay频谱共享模式下,提出一种认知无线电网络接入控制和功率控制的联合算法。该算法针对主用户干扰温度和次用户信号干扰噪声比(SINR)限制,通过引入冲突因子,实现网络自适应调整被拒绝参与频谱共享的次用户数目,通过迭代进行接入控制和功率分配实现频谱共享。仿真结果表明,该算法能够保证接入共享主网络频带的次用户数近似最优,而且缩短了接入控制的时间。     

OFDMA认知无线电网络中面向功率控制的频谱定价与分配

针对OFDMA认知无线电网络,提出一种基于Stackelberg博弈的频谱定价和分配模型.对于次基站控制次网络传输功率来保护主网络通信的场景,主基站可通过该模型获得最优的频谱定价方案.从功率控制的角度,重新设计次用户的效用函数,运用Stackelberg博弈对单个主基站和多个次用户在频谱租赁市场中的交易行为进行建模.通过逆向归纳法,求解市场均衡下的最优频谱定价,使得主基站在考虑主网络QoS降级的同时获得最大收益.此外,对于主基站只能获取本地信息的情形,提出了基于动态Stackelberg博弈的分布式频谱定价和分配模型.仿真实验表明,该模型能够在控制次网络传输功率的基础上,提供最优频谱定价和频谱分配方案.     

基于DQN的超密集网络能效资源管理

小基站的密集随机部署会产生严重干扰和较高能耗问题,为降低网络干扰、保证用户网络服务质量(QoS)并提高网络能效,构建一种基于深度强化学习(DRL)的资源分配和功率控制联合优化框架.综合考虑超密集异构网络中的同层干扰和跨层干扰,提出对频谱与功率资源联合控制能效以及用户QoS的联合优化问题.针对该联合优化问题的NP-Har...     

A game theoretic approach for power allocation with QoS constraints in wireless multimedia sensor networks

The distinctive features of wireless multimedia sensor networks (WMSNs) include application-specific quality-of-service (QoS) requirements and limited energy supply, with which each node makes its own decisions selfishly. Therefore this paper presents a power control game theoretic approach for WMSNs by studying the effect of transmission power on QoS and energy efficiency. The game approach determines the transmission strategy using utility optimization according to the fluctuation of channel states. Here, the utility function is defined by effective throughput per unit power while satisfying the user’s delay QoS constraints. The existence and uniqueness of Nash equilibrium for the proposed game are proved. Finally, the simulation results show that each user chooses the optimal transmission power to maximize its utility based on other constant parameters and the effects of delay constraints on the user’s utility are quantified as well.     

基于干扰温度限制的认知正交频分复用系统功率分配算法

在认知正交频分复用（OFDM）系统中,为避免对主用户（PU）的干扰,需要对认知用户（CU）基站的发射功率进行控制和分配。针对认知用户基站无法合理分配其发射功率及无法有效提高数据传输速率等问题,在传统注水功率分配算法的基础上,提出了一种双因子二分搜索最优化功率分配算法。该算法充分考虑认知用户信道上干扰温度的限制,首先,在满足总功率限制的条件下引入剩余函数;然后,利用剩余函数的单调性,通过双层二分搜索迭代方法求得拉格朗日因子的准确值;最后,通过拉格朗日因子的值求出各子信道上所分配的功率值。仿真结果表明,所提算法能有效利用主用户频带间的频谱空穴,在总功率限制和干扰温度（IT）限制下,最大化认知用户的数据传输率,其值逼近传统注水算法。同时该算法所得到的数据传输速率比总功率平均控制算法和干扰温度平均控制算法有明显的提高,在相同仿真环境下其传输总速率超出约4×105b/s。在迭代过程中所提算法处理时间较少,并体现出良好的鲁棒性。     

On optimized decode and forward relay assisted CR system design for throughput maximization

This paper proposes a joint spectral sensing and secondary data transmission scheme in a decode-and-forward (DF) relay assisted cognitive radio network (CRN). Periodic spectral sensing is done in each frame that primarily consists of two slots: sensing slot and data transmission slot. Sensing information of cognitive source (CS) and cognitive relay (CR) are cooperatively combined at CS to determine reliably the presence or absence of the primary user (PU). An optimization framework is developed to determine the sensing time, data transmission time for CS and CR along with their associated transmission power with an aim to maximize the average throughput of secondary transmission under the constraints of target detection probability, sum transmission power and interference to PU. Simulation results show that significant improvement in average throughput is achieved when the transmission power of CS and CR are jointly optimized with the sensing duration and transmission time of CS and CR. Performance improvement over amplify-and-forward (AF) relay is also reported.     

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.     

Spectral efficiency of cognitive radio networks under interference constraint and QoS guarantees

We study the problem of maximizing spectral efficiency of cognitive radio network deployments subject to an interference constraint and under specific quality of service (QoS) guarantees. The interference constraint corresponds to the upper limit of the received power that can be tolerated at the licensed users’ due to transmissions from unlicensed users. The QoS guarantees stem from the requirements imposed by the applications running at the users’ terminals. A cross-layer design is adopted that maps the user’s requirements into delay related QoS guarantees at the data link layer and error probability QoS guarantees at the physical layer. The obtained numerical results provide important insights regarding the impact of the considered constraint and guarantees on the achievable spectral efficiency of cognitive radio networks.     

认知无线网络的Rubinstein博弈频谱共享方法研究

针对传统的功率控制算法限制认知用户的发射功率而影响其服务质量(Quality of Service, QoS)的问题,提出了一种基于功率控制的多人Rubinstein博弈频谱分配算法。该算法通过牛顿迭代公式降低认知用户的发射功率并依据各个用户间的干扰得到相应的链路质量;将经济学中的贴现因子与用户的链路质量建立映射关系;通过链路质量调整认知用户子博弈顺序使得网络总传输速率达到一个相对稳定的状态。仿真结果表明:多个认知用户在同一信道下共享频谱时,采用多人Rubinstein博弈算法对系统的总传输速率有明显的提升,使系统处于稳定且高速的传输状态并节省了一半以上的频谱分配时间。     

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.     

Distributed power adjustment based on control theory for cognitive radio networks

In this paper, we propose two power adjustment methods for cognitive radio networks. In the first algorithm, the transmitter derives the transmission power with PID control in order to satisfy the QoS constraints in secondary networks. The derived transmission power is compared with a constraint condition in order to avoid the interference with primary networks, and then the actual transmission power is decided. Because the constraint condition affects the performance of our proposed method significantly, we propose an effective update algorithm. On the other hand, the second algorithm is based on model predictive control (MPC). In this method, the decision of transmission power is formulated as quadratic programming (QP) problem and the transmission power is derived directly with the constraint condition. We evaluate the performances of our proposed methods with simulation and compare the proposed methods with the distributed power control (DPC) method. In numerical examples, we show that our proposed methods are more effective than the existing method in some situations. We also prove analytically that the interference with primary networks can be avoided with probability one by using our proposed method if each transmitter has the information about every channel gain.     

Joint base station activation,user admission control and beamforming in downlink green networks

Consider a multicell downlink network, where the base stations (BSs) in different cells cooperate in the precoder level, while those in the same cell are coordinated for joint processing (JP). To achieve green communication, we optimize the network power consumption under some quality-of-service (QoS) constraints. Different from the conventional approaches based on BS activation and beamforming, we further integrate user admission control into network management, and thus minimize the power consumption by jointly optimizing the active BSs, the admissible users and the transmit beamformers. This strategy has two advantages. First, it ensures that the network still works even when some users cannot achieve the QoS requirements. Second, it helps select a subset of users whose QoS requirements can be satisfied with relatively low power cost, thereby improving the power efficiency. However, this problem is challenging due to the mixed-integer programming. To pursue some efficient approximate solution, we first reformulate it as a convex sparse optimization problem and then develop a distributed algorithm to iteratively solve the problem, utilizing the alternating direction method of multipliers (ADMM). The proposed algorithm has very low complexity since each step can be computed in closed form. Its efficacy and efficiency are demonstrated by numerical simulations.     

部分信道状态信息下MIMO-OFDM的跨层资源分配算法

针对跨层多用户多输入多输出-正交频分复用（MIMO-OFDM）系统,以系统最大吞吐量为目标,给出一种基于部分信道状态信息的跨层资源分配算法。该优化问题设计的目标函数包括功率限制、传输速率、子载波占用、不同业务的服务质量需求与数据链路层的队列状态信息等约束条件。在数据链路层存在有限缓存条件下,通过均值反馈模型描述信道状态信息的反馈过程,推导出相应的跨层资源分配准则。仿真结果表明,所提算法与现有方案相比,满足了不同业务用户的QoS要求,并获得了好的吞吐率,降低了丢包率。     

User satisfaction aware routing and energy modeling of polymorphic network on chip architecture

In mobile devices, multiple applications contend for limited resources in the underlying embedded system framework. Application resource requirements in mobile systems vary by computation needs, energy consumption and user interaction frequency. Quality of service (QoS) is the predominant metric of choice to manage resources among contending applications. Resource allocation policies to support static QoS for applications do not reflect the changing demands of the user in contemporary network on chip (NoC) based embedded architectures. User satisfaction with the user interactions and user interface design ought to be the primary design driver. Some recent research has integrated a saturating, non-linear user satisfaction function in the application thread scheduler. The application and operating system level user satisfaction research assumes that the throughput of inter-thread edges is limited only by the computational constraints of the nodes. With NoC, however, NoC resource allocation policies play an important role in determining the inter-thread communication flow’s throughput and the resulting application level user satisfaction. In this paper, we filter down the user satisfaction from an application layer attribute to a router level attribute to improve the resource and energy utilization for routing in order to leverage the user satisfaction at the application and system level. We demonstrate that this technique improves the user satisfaction of audio (MP3) application by 10% while maintaining the user satisfaction of video (MPEG-2) application. Experiments also show that a fixed energy source can be extended for an average of 18% of the time using the NoC user satisfaction based energy optimization proposed in this research.     

认知传感网络的信能无线传输优化

针对目前无线传感网络频谱资源匮乏、能源供给不足等问题,本文构建了一种新型的认知多播无线传感网络,并研究适用于该网络的信能协同传输问题。以网络中所有次级用户收集到的和能量为优化目标,结合多播技术,在次用户对主用户的干扰、次用户的信干噪比、次级发射器的发射功率的约束下,构建了一种非线性非凸优化问题。针对该优化问题,提出一种基于半正定松弛的算法,从而延长网络的生存周期;并在此基础上提出一种基于序贯参数凸逼近技术的优化算法以降低复杂度。仿真结果表明,本文所提的算法有效提高了次用户能量收集的性能。     

Big Data Based Self-optimization Networking: A Novel Approach Beyond Cognition

It is essential to satisfy class-specific QoS constraints to provide broadband services for new generation wireless networks. A self-optimization technique is introduced as the only viable solution for controlling and managing this type of huge data networks. This technique allows control of resources and key performance indicators without human intervention, based solely on the network intelligence. The present study proposes a big data based self optimization networking (BD-SON) model for wireless networks in which the KPI parameters affecting the QoS are assumed to be controlled through a multidimensional decision-making process. Also, Resource Management Center (RMC) was used to allocate the required resources to each part of the network based on made decision in SON engine, which can satisfy QoS constraints of a multicast session in which satisfying interference constraints is the main challenge. A load-balanced gradient power allocation (L-GPA) scheme was also applied for the QoS-aware multicast model to accommodate the effect of transmission power level based on link capacity requirements. Experimental results confirm that the proposed power allocation techniques considerably increase the chances of finding an optimal solution. Also, results confirm that proposed model achieves significant gain in terms of quality of service and capacity along with low complexity and load balancing optimality in the network.     

一种联合QoS约束及波束形成准则的蜂窝网络高效协作传输技术

针对由于通信干扰严重导致的蜂窝网络数据传输效率不高、能耗较大的问题,提出一种联合QoS约束及波束形成准则的蜂窝网络高效协作传输技术。该技术采用了由宏蜂窝、飞蜂窝组成的蜂窝网络系统模型,分析了网络通信过程中的信号干扰及功率情况。基于QoS约束的高效协作传输算法,采用QoS约束把数据传输的能量效率问题优化成波束成形和功率分配问题,并将Qos约束考虑进波束形成准则,从而改善网络的干扰状态,提高能量效率。实验仿真结果的对比情况表明,该技术可以提高蜂窝网络进行数据传输的能量效率,并减少通信干扰。