认知无线电系统的资源分配问题研究

发布时间：2018-03-17 15:49

本文选题：认知无线电　切入点：资源分配　出处：《吉林大学》2014年博士论文　论文类型：学位论文

【摘要】：频谱资源有限性与人们对无线频谱需求的日益增长之间的矛盾日益突出和加剧，如何快速、有效地提高频谱利用效率是各国无线通信应用领域都面临的重大研究问题。认知无线电系统作为智能的无线通信系统能够以高效的方式利用无线频谱，是解决上述矛盾的主要途径。通过机会式频谱访问或者与授权用户共享频谱，认知无线电能够有效地提高频谱利用率。机会式频谱访问方式需要检测和利用频谱空穴。一旦授权用户出现，认知用户必须立即退出，找寻其它的频谱空穴。频谱共享方式允许认知用户与授权用户共享同一频谱，条件是认知用户带来的干扰不损害授权用户的性能。本文以频谱共享的访问方式作为认知无线电系统的研究平台。近年来，认知无线电系统的功率控制问题得到了广泛研究。认知无线电能够感知、学习、优化，并根据相应环境重新调整操作参数。认知用户通常要在最大化自身的数据容量和最小化其带给授权接收机的干扰两者中权衡。不降低授权用户的QOS情况下，认知用户妥善控制其发射功率以求得到高的数据容量。本文从不同角度对功率分配策略进行了深入的研究，并给出了系统模型和相应解决方案。主要研究工作如下： 1）大多数传统资源分配算法都是假定信道状态信息被完美估计的。然而，认知无线电工作在高度动态环境中，信道的参数存在一定程度的扰动性。鲁棒优化可以处理信道参数的不确定性。我们提出一种鲁棒优化的认知系统资源分配方法。该方法利用椭圆不确定性集合描述信道干扰增益的不确定性。除了考虑干扰功率约束来保护授权用户，还考虑了认知用户自身的发射功率限制。该认知系统功率分配问题是一个具有无限个约束条件的优化问题。通过鲁棒优化，可以转化为最坏情况下的有限个约束问题。即使信道存在扰动，该方法也能保证认知系统的稳定性和鲁棒性，同时也给每个用户提供了无缝通信。 2）目标容量跟踪是认知系统的一个新的关键问题。为了跟踪目标容量，利用时延的状态空间模型描述动态容量误差。将功率控制问题转化为状态反馈控制问题。引入状态反馈控制器使其输出总是跟踪实际系统的目标容量。通过H控制理论得到线性矩阵不等式。利用线性矩阵不等式可以获得反馈控制器的增益，，进而得到跟踪容量。每个认知用户在各个子载波上的功率分配情况随之明了。该方法从全新的角度运用控制理论做资源分配问题研究，既保证了授权用户的干扰功率约束，也获得了认知用户满意的QoS。 3）为了使认知系统获得更高的通信容量，功率控制是一个非常重要的因素。保证授权用户和认知用户的QoS，形成一个最大化每个认知用户数据容量的功率控制策略。认知系统的功率控制问题转化为混合整数非线性规划问题。该混合整数非线性规划问题等效于凸多面体的变分不等式问题。通过将变分不等式转化为互补问题，最后运用修正投影算法得到了认知用户功率分配的最优解。该策略方案使系统快速收敛到纳什均衡，并且获得最大系统容量。 4）协作通信是提高功率效率、增加系统容量非常重要的方法，而且可以扩大信号覆盖范围。本文提出一种自适应中继选择和功率分配方案，该方法收敛速度快，稳定性好。认知用户非协作通信时，其获得的SINR如果低于自身目标，该用户自适应寻找最优中继进行协作通信。此时功率控制问题转变为混合整数非线性规划问题，利用KKT方法得到最优解。该方案的目标是保证SINR的同时最大化系统容量。该算法不仅大大提高了数据容量，而且增大了信号覆盖范围。 5）在认知无线电系统中，大多数资源分配方案都是基于授权用户干扰功率约束控制的。本文提出一种新的准则称授权用户允许SINR损失准则来保护授权用户。所谓允许SINR损失指保证授权用户正常通信其所能承受的最大SINR损失。运用该准则保护授权用户，取代传统干扰功率约束控制。与传统运用干扰约束的功率控制方法相比，本法能够获得更高的信道容量，并且能使认知系统稳定在均衡状态。该方法不仅易于实现，而且执行性能更好。
[Abstract]:The contradiction between the limited spectrum resources and the growing demand for wireless spectrum is becoming more and more intensified, how to quickly and effectively improve the spectrum utilization efficiency is a major research problem of wireless communication applications are facing all countries. As a cognitive radio system intelligent wireless communication system can use the wireless spectrum in an efficient way, is the main way to solve these contradictions.
Through opportunistic spectrum access or share the spectrum with the authorized users, cognitive radio can effectively improve spectrum utilization. Opportunistic spectrum access need to detect and use spectrum holes. Once the authorized users, cognitive users must immediately exit, find other frequency spectrum hole. Spectrum sharing allows the primary users to share the same the spectrum condition is interference in cognitive users without compromising the performance of authorized users. Based on the spectrum sharing access mode as the research platform of cognitive radio system.
In recent years, cognitive radio power control problem has been widely studied. Cognitive radio can perception, learning, optimization, and re adjust the operating parameters according to the corresponding environment. Cognitive users usually in their maximum data capacity and minimize the interference to authorized balance receiver. The two does not reduce the QOS authorized users under the control of the transmission power of the cognitive users properly in order to get high data capacity. This paper studied the power allocation strategy from different angles, and gives the system model and the corresponding solutions. The main research work is as follows:
1) most of the traditional resource allocation algorithm is based on channel state information is perfect estimation. However, cognitive radio work in a highly dynamic environment, there is a certain degree of disturbance of the channel parameters. The robust optimization can handle channel parameter uncertainty. We propose a robust optimization system. The method of cognitive resource allocation method using elliptic uncertainty set describing channel interference gain uncertainty. Besides considering the interference power constraints to protect the licensed users, also considering the transmitting power of cognitive users to its limits. The cognitive system of power allocation problem is an infinitely constrained optimization problem. By robust optimization, can be transformed into the worst case a finite constraint problem. Even if the channel disturbance, this method can guarantee the stability and robustness of the cognitive system, but also to each user It provides seamless communication.
2) target tracking capacity is a key issue in the cognitive system. In order to track the target capacity, the state space model to describe the dynamic delay capacity error. The power control problem is transformed into a state feedback control problem. The state feedback controller so that the output is always tracking the actual system target capacity. Through H control theory the linear matrix inequality. Feedback controller gain can be obtained by using the linear matrix inequality, and then obtain the tracking capacity. Power allocation in each subcarrier of each cognitive user will be clear. This method from the perspective of a new control theory is used to do the research on the resource allocation problems, both to ensure the interference power constraints of authorized users, also gained recognition customer satisfaction QoS.
3) in order to obtain a higher cognitive system communication capacity, power control is a very important factor. To ensure the authorized users and cognitive users of QoS, a maximum power of each user data capacity control strategy. The power of the cognitive system control problem is transformed into a mixed integer nonlinear programming problem. The equivalent of the hybrid integer nonlinear programming problem in convex variational inequality problem. By applying the variational inequality into complementarity problem, finally the optimal cognitive user power allocation solution is obtained using the modified projection algorithm. This strategy makes the system fast convergence to the Nash equilibrium, and maximize the system capacity.
4) cooperative communication is to improve the power efficiency, increase the system capacity is very important, but also can expand the signal coverage. This paper proposes an adaptive relay selection and power allocation scheme, the method has fast convergence speed and good stability. The cognitive users of non cooperative communication, the SINR below its goal, the user adaptive to find the optimal relay cooperative communication. The power control problem is transformed into a mixed integer nonlinear programming, using the KKT method to obtain the optimal solution. The goal of the program is to ensure SINR and maximize the system capacity. The algorithm not only greatly improves the data capacity, but also increases the signal coverage.
5) in cognitive radio systems, most resource allocation schemes are based on the authorized user interference power constraint control. This paper proposes a new criterion that authorized users to allow SINR loss standards to protect the authorized user. The so-called loss refers to allow SINR to ensure authorized user normal communication can withstand the maximum loss of SINR. By using the criteria for authorized users, to replace the traditional interference power constraint control. Compared with the traditional power using the interference constraint control method, this method can obtain higher channel capacity, and can make the cognitive system stable in equilibrium. This method is not only easy to implement, and the performance is better.

【学位授予单位】：吉林大学
【学位级别】：博士
【学位授予年份】：2014
【分类号】：TN925

【相似文献】