马尔科夫链在认知无线网络中的应用研究

文章研究的是在认知无线网络中,动态频谱的接入方法。文章提出了一种基于马尔科夫链的动态频谱接入方法,利用主用户和次用户的业务特征,建立马尔科夫链理论分析模型,并对模型进行了详细的设计分析与仿真。仿真结果表明这种方法能有效地保证主用户业务的QoS,并且最大化次级用户的吞吐量。     

认知无线网络中基于业务区分的自适应MAC协议

为了提高认知用户的接入效率,同时兼顾其QoS需求,提出了一种应用于认知无线网络的基于业务区分的自适应MAC协议。对不同业务采取不同的最优频谱检测时间,实时业务(RT)以最小化接入时延为目的确定最优的频谱检测时间,而非实时用户(NRT)则以最大化吞吐率为目标确定最佳的频谱检测时间。根据所确定出的最佳频谱检测时间,结合当前的业务到达速率和调度策略,不同业务以时隙Aloha的方式接入空闲频谱。理论分析和仿真表明,所提出的自适应MAC协议能够自适应地为认知用户的不同业务确定最优频谱检测时间,从而有效地提高频谱使用效率,保证不同业务的QoS需求。     

一种分布式认知MANET功率控制算法

对认知MANET的研究涉及到许多方面,其中认知MANET中的功率控制算法设计是目前的一个研究热点.为了实现认知MANET中的次用户网络和主用户网络频谱共享,从而提高频谱利用率,即次用户网络中的次用户可以机会接入授权主用户所在的频谱,且同时保证主用户和次用户的QoS需求.本文认知MANET中功率控制的目的就是在满足QoS...     

动态自适应频谱的接入策略研究

认知无线电技术利用非授权频谱资源进行业务传输,缓和了目前通信业务飞速发展与频谱资源稀缺的矛盾。动态频谱接入技术通过感知频带,使认知用户能够伺机接入未被主用户占用的频谱空洞。但是在接入过程中,特别是在多个认知用户竞争接入相同的可用频谱资源时,往往面临认知用户间的冲突和各认知用户的QoS得不到保证的问题。文章重点介绍了目前国内外在多个认知用户接入策略上的一些研究成果,并对这些成果进行分析,提出了今后需要进一步研究的方向。     

多业务LEO卫星网络中基于改进遗传算法的最优多门限信道预留(OMTCR)机制

在多业务LEO卫星网络中,最优多门限信道预留(Optimal Multiple Threshold Channel Reservation,OMTCR)机制能够从连接级对系统资源进行高效地配置,实现用户QoS和系统整体收益之间的折中平衡.本文设计相应的遗传算法(GA)求解OMTCR最优门限参数矢量,针对OMTCR策略对可行域及目标函数的特殊要求对基本GA进行修复,实现快速求解;为了避免业务类型增多和带宽提高给算法实际应用所带来的障碍,设计小种群数的迭代GA寻优策略,进一步减少计算量并加快收敛速度.以系统整体收益损失为评价指标,大量的仿真结果与穷举法所得到的最优解相比,改进GA在无QoS约束条件和有QoS约束条件下均能达到很好的性能,并且能够显著缩短计算时间.改进GA的计算精度和收敛速度保证了OMTCR机制应用于实际系统中的可行性.     

认知无线电中一种基于干扰温度的功率控制算法

基于干扰温度模型,应将认知无线电网络中认知用户与主用户共享频谱时应满足各自的用户服务质量（QoS）问题转化为有约束的非线性功率控制优化问题。运用迭代算法和拉格朗日相关理论探讨出一种近似最优的功率分配算法。理论分析和仿真表明,该算法既能满足主用户的干扰温度容限,同时又能使认知用户获得很好的信噪干扰比,增大系统的吞吐量。     

基于Bertrand博弈论的动态频谱分配算法

研究了在多主用户参与的完全竞争模式下的频谱价格问题,建立基于博弈理论的经济模型,分析主用户的频谱利润和频谱价格与认知用户的频谱需求之间的关系。考虑了主用户数、频谱替代因子和学习速率对频谱价格、纳什均衡、收敛性及稳定性的影响。仿真结果表明,合理的频谱价格及传输参数,可以使主用户获得的利润最大化,合理的学习速率可以使主用户的频谱价格快速收敛,通过有偿频谱共享,充分调动了主用户的积极性,从而有效的缓解了认知用户频谱资源紧张的局面。     

基于广义纳什讨价还价解的资源分配方法

为不同业务提供不同的服务质量保证(QoS)是IEEE 802.16无线Mesh网络关键问题。针对IEEE 802.16无线Mesh网络分布式资源分配问题,文中提出了一种基于广义纳什讨价还价解的资源分配方法。此方法结合多射频多信道的协议干扰模型,利用广义纳什讨价还价解,联合信道分配和自适应调制编码技术(AMC),定义业务端到端流量的效用函数,根据业务不同的讨价还价能力来提供业务的QoS保证。通过拉格朗日对偶原理,各个用户在分布式的情况下对对偶问题进行求解获得各自的速率分配方案,使用户在分布式的情况下以合作方式竞争资源,既能保证高优先级流的QoS,并使网络整体性能达到帕累托最优。实验结果表明所提方法能兼顾网络性能和用户优先级。     

认知 mesh 网络服务区分的动态频谱接入策略

动态频谱接入策略是实现认知无线电网络高效利用频谱的关键。与传统认知无线电网络不同,认知mesh网络中不同QoS需求的多类型业务共同接入,为适应这一特点,提出服务区分的动态频谱接入策略。策略依据业务的QoS需求确立优先级,针对不同优先级业务采取不同的信道接入方案,实时业务依据最优传输延迟期望选择接入信道集合,在减小传输延迟的同时降低数据传输过程授权用户出现的概率,普通业务选择最优理想传输成功概率的信道,降低信道切换概率。理论与实验结果表明,与传统的认知网络频谱接入策略相比,提出的策略能提供不同业务的服务区分,满足实时业务的低延迟需求,降低数据传输的中断率,同时在授权信道空闲率与网络负载较大时吞吐量性能较优。     

一个基于SLS的接入网边到边QoS管理机制

该文提出了一个基于SLS的EPON的边到边QoS管理机制.通过EPON很好地解决了带宽不足问题;通过优化度评估算法为用户业务选择业务类.由于优化度评估公式基于用户在市场上的行为,考虑了负载、价格和性能因素,因此不但可通过引导业务量沿不同的业务类有序分布在DiffServ机制下实现了对用户业务的定量的边到边QoS保证,同时可以提高用户业务的性能价格比.实验结果很好地证明该机制的优点.     

Novel pricing model for spectrum leasing in secondary spectrum market

According to the property rights model of cognitive radio,primary users who own the spectral resource have the right to lease or trade part of it to secondary users in exchange for appropriate profit. In this paper,an implementation of this framework is investigated,where a primary link can lease the owned spectrum to secondary nodes in exchange for cooperation (relaying). A novel pricing model is proposed that enables the trading between spectrum and cooperation. Based on the demand of secondary nodes,the primary link attempts to maximize its quality of service (QoS) by setting the price of spectrum. Taking the price asked by primary link,the secondary nodes aim to obtain most profits by deciding the amount of spectrum to buy and then pay for it by cooperative transmission. The investigated model is conveniently cast in the framework of seller/buyer (Stackelberg) games. Analysis and numerical results show that our pricing model is effective and practical for spectrum leasing based on trading spectral resource for cooperation.     

Dynamics of Multiple-Seller and Multiple-Buyer Spectrum Trading in Cognitive Radio Networks: A Game-Theoretic Modeling Approach

We consider the problem of spectrum trading with multiple licensed users (i.e., primary users) selling spectrum opportunities to multiple unlicensed users (i.e., secondary users). The secondary users can adapt the spectrum buying behavior (i.e., evolve) by observing the variations in price and quality of spectrum offered by the different primary users or primary service providers. The primary users or primary service providers can adjust their behavior in selling the spectrum opportunities to secondary users to achieve the highest utility. In this paper, we model the evolution and the dynamic behavior of secondary users using the theory of evolutionary game. An algorithm for the implementation of the evolution process of a secondary user is also presented. To model the competition among the primary users, a noncooperative game is formulated where the Nash equilibrium is considered as the solution (in terms of size of offered spectrum to the secondary users and spectrum price). For a primary user, an iterative algorithm for strategy adaptation to achieve the solution is presented. The proposed game-theoretic framework for modeling the interactions among multiple primary users (or service providers) and multiple secondary users is used to investigate network dynamics under different system parameter settings and under system perturbation.     

The Master-Slave Stochastic Knapsack Modeling for Fully Dynamic Spectrum Allocation

Scarcity problem of radio spectrum resource stimulates the research on cognitive radio technology, in which dynamic spectrum allocation attracts lots of attention. For higher access efficiency in cognitive radio context, we suggest a fully dynamic access scheme for primary and secondary users, which is modeled by a master-slave stochastic knapsack process. Equilibrium behavior of this knapsack model is analyzed: expressions of blocking probability of both master and slave classes are derived as performance criterion, as well as forced termination probability for the slave class. All the theoretic results are verified by numeric simulations. Compared to traditional opportunistic spectrum access (OSA), which can be regarded as half dynamic due to primary users?? rough preemption, our scheme leads to less termination events for the secondary users while keeping the same performance for the primary class, thus promotes the system access performance. Nonideal spectrum sensing algorithm with detection error is also taken into consideration to evaluate its impact on system access performance, which is a practical issue for implementation.     

A Decision-Theoretic Framework for Opportunistic Spectrum Access

Built on a hierarchical access structure with primary and secondary users, opportunistic spectrum access improves spectrum efficiency while maintaining compatibility with legacy wireless systems. The basic idea is to allow secondary users to exploit instantaneous spectrum availability while limiting the interference to primary users. In this article, we identify basic components, fundamental trade-offs, and practical constraints in opportunistic spectrum access. We introduce a decision-theoretic framework based on the theory of partially observable Markov decision processes. This framework allows us to systematically tackle the optimal integrated design and quantitatively characterize the interaction between signal processing for opportunity identification and networking for opportunity exploitation. A discussion of open problems, potential applications, and recent developments is also provided.     

On co-channel and adjacent channel interference mitigation in cognitive radio networks

Cognitive radio (CR) is an emerging wireless communications paradigm of sharing spectrum among licensed (or, primary) and unlicensed (or, CR) users. In CR networks, interference mitigation is crucial not only for primary user protection, but also for the quality of service of CR user themselves. In this paper, we consider the problem of interference mitigation via channel assignment and power allocation for CR users. A cross-layer optimization framework for minimizing both co-channel and adjacent channel interference is developed; the latter has been shown to have considerable impact in practical systems. Cooperative spectrum sensing, opportunistic spectrum access, channel assignment, and power allocation are considered in the problem formulation. We propose a reformulation–linearization technique (RLT) based centralized algorithm, as well as a distributed greedy algorithm that uses local information for near-optimal solutions. Both algorithms are evaluated with simulations and are shown quite effective for mitigating both types of interference and achieving high CR network capacity.     

Impact of secondary user mobility on spectrum handoff under generalized residual time distributions in cognitive radio networks

Cognitive Radio is an emerging technology to accommodate the growing demand for wireless technology via dynamic spectrum access to enhance spectrum efficiency. Spectrum handoff is an important component of Cognitive Radio technology for practical implementation of radio frequency access strategy and better utilization of spectrum in both primary and secondary networks. The probability of spectrum handoff and expected number of spectrum handoffs are key parameters in performance analysis and design of the cognitive radio network. This work presents an analytical model to evaluate the impact of secondary users’ mobility on intra-cell spectrum handoff considering primary users’ activity model in a cognitive radio network. A standard form of intra-cell spectrum handoff probability and expected number of intra-cell spectrum handoffs are derived for complete call duration of a non-stationary secondary user. The probability and expected number of intra-cell spectrum handoffs of a post inter-cell handoff call are also derived for generalized residual time distributions of call holding time and spectrum holes. A detailed analysis of these performance measuring metrics is presented under the impact of departure rate and cell crossing rate of secondary users. The accuracy of the derived analytical result is validated by Monte-Carlo simulation of the model.     

Towards trustworthy collaboration in spectrum sensing for ad hoc cognitive radio networks

Cognitive radio networks (CRN) make use of dynamic spectrum access to communicate opportunistically in frequency bands otherwise licensed to incumbent primary users such as TV broadcast. To prevent interference to primary users it is vital for secondary users in CRNs to conduct accurate spectrum sensing, which is especially challenging when the transmission range of primary users is shorter compared to the size of the CRN. This task becomes even more challenging in the presence of malicious secondary users that launch spectrum sensing data falsification (SSDF) attacks by providing false spectrum reports. Existing solutions to detect such malicious behaviors cannot be utilized in scenarios where the transmission range of primary users is limited within a small sub-region of the CRN. In this paper, we present a framework for trustworthy collaboration in spectrum sensing for ad hoc CRNs. This framework incorporates a semi-supervised spatio-spectral anomaly/outlier detection system and a reputation system, both designed to detect byzantine attacks in the form of SSDF from malicious nodes within the CRN. The framework guarantees protection of incumbent primary users’ communication rights while at the same time making optimal use of the spectrum when it is not used by primary users. Simulation carried out under typical network conditions and attack scenarios shows that our proposed framework can achieve spectrum decision accuracy up to 99.3 % and detect malicious nodes up to 98 % of the time.     

Power Control in Cognitive Radio Networks: How to Cross a Multi-Lane Highway

We consider power control in cognitive radio networks where secondary users identify and exploit instantaneous and local spectrum opportunities without causing unacceptable interference to primary users. We qualitatively characterize the impacts of the transmission power of secondary users on the occurrence of spectrum opportunities and the reliability of opportunity detection. Based on a Poisson model of the primary network, we quantify these impacts by showing that (i) the probability of spectrum opportunity decreases exponentially with the transmission power of secondary users, where the exponential decay constant is given by the traffic load of primary users; (ii) reliable opportunity detection is achieved in the two extreme regimes in terms of the ratio between the transmission power of secondary users and that of primary users. Such analytical characterizations allow us to study power control for optimal transport throughput under constraints on the interference to primary users. Furthermore, we reveal the difference between detecting primary signals and detecting spectrum opportunities, and demonstrate the complex relationship between physical layer spectrum sensing and MAC layer throughput. The dependency of this PHY-MAC interaction on the application type and the use of handshake signaling such as RTS/CTS is also illustrated.     

认知无线电的合作频谱检测分析

认知用户需要持续快速地检测主用户的出现,对基于认知无线电的合作频谱检测机制进行了分析。在理想的两用户网络中,认知用户采用合作机制可以提高系统的平均侦测时间。在多认知用户网络中,参与合作的用户越多,单个认知用户对主用户的侦测概率越高。对于多用户网络,还考虑了整体的实现。     

认知无线网络中第一和第二用户的积压和延迟分布研究

In a cognitive radio network, the secondary users can use the spectrum holes when the primary users do not utilize the spectrum, but they must vacant the spectrum when the primary users need to transmit data on the spectrum. In other words, the primary users have higher priority over the secondary users. In this paper, backlog and delay distribution bounds for both primary users and secondary users are obtained. The analysis is based on stochastic network calculus, for which, stochastic service curves are first derived for both primary users and secondary users, and the network calculus independent case analysis approach is used to find the distribution bounds. Numerical results and simulation results are also presented and discussed.