空闲认知用户在认知无线电频谱检测中的应用
发布时间:2019-02-15 09:25
【摘要】:随着通信技术的高速发展,,频谱逐渐成为一种稀缺的资源。频谱利用率低的问题严重制约着无线通信的应用。认知无线电技术通过动态地利用空闲频谱资源进行传输,大幅提升频谱利用率,是未来无线通信系统中的关键技术之一。检测技术作为获取空闲频谱信息的手段,在认知无线电技术中起着重要的作用。在某一时刻,空闲频谱资源是由主要用户处于空闲状态形成的,与之相似,将处于空闲状态的认知用户用于检测可以提升检测的性能和效果。本文在考虑将空闲认知用户用于检测的情况下,重点从协作检测、联合检测以及对空闲认知用户的分配机制等方面研究了认知无线电网络检测优化问题。具体研究成果如下: 1.在overlay传输模式下提出了一种利用空闲认知用户的检测模型,通过分析得出认知网络的检测性能。由于利用空闲认知用户需要额外的消耗,同时考虑认知网络的吞吐量和使用效率,提出空闲认知用户在协作检测中的额外检测时长的优化问题。在公平性原则的要求下,允许空闲认知用户选择是否进行协作,提出了一种兼顾吞吐量和效率的优化算法。理论与仿真证明了加入空闲认知用户进行协作检测可以提升检测的性能。 2.针对主要用户网络存在网间干扰时,underlay传输模型下认知网络吞吐量较低的问题,提出了一种利用导频检测在underlay传输模式下切换发射功率以提升认知网络吞吐量的检测时长优化算法。首先考虑主要用户接收信干噪比满足的溢出概率,将对认知用户发射功率的优化问题转化成一个”检测-传输”的权衡问题。然后对权衡问题进行分析,得出使得吞吐量最大的检测时长优化算法。仿真结果表明,采用最优检测时长算法可以获得最大的认知网络吞吐量。 3.由于认知网络中主要用户状态的不确定性,随机检测策略虽然易于实现,但无法保证认知用户准确地寻找到可用信道。本文提出一种利用认知无线网络中空闲认知用户进行联合随机检测的策略。首先加入空闲认知用户进行检测,然后利用马尔科夫模型对认知基站获得的可用信道数进行描述,推导出此时认知网络的检测性能。理论分析和仿真结果表明,利用空闲认知用户可以减小服务时延,并且提高认知网络的吞吐量。在考虑认知用户汇报检测信息所占用的时长后,通过优化算法,可以得出最优的参与联合检测的空闲认知用户数。 4.在认知无线网络中,针对分配式联合检测中的最优检测顺序问题,本文提出了一种利用空闲认知用户的检测策略以最大化认知网络的吞吐量。首先使用连续时间马尔科夫模型对主要用户的活动状况进行建模,然后考虑检测性能对认知网络吞吐量的影响,得出此时认知网络吞吐量的表达式。并以此为目标函数,提出最大化认知网络吞吐量的检测顺序优化算法。由于上下行链路的差异性,在下行链路提出一种最优的检测策略;在上行链路提出一种次优的但是复杂度较低的检测策略。仿真证明了提出的检测顺序优化算法与随机检测相比拥有更好的吞吐量,并能获得更高的频谱利用率,同时降低了认知用户的等待时延。在考虑空闲认知用户的汇报时长后,得出最大化认知网络吞吐量的参与检测的空闲认知用户数。 5.每一个活跃认知用户都希望尽可能多的获得信道状态信息。为了解决认知网络中空闲认知用户的所属权问题,本文提出一种利用拍卖算法对空闲认知用户进行分配的方案。研究了在第二价格的拍卖场景下,认知用户在传输量拍卖和满意度拍卖两种模式下所能获得的收益。同时提出了一种存在预留价格、预算受限以及公平性限制等条件下的拍卖算法。仿真结果表明,同其他算法相比,基于拍卖的空闲认知用户分配算法可以使认知网络获得较好的性能。
[Abstract]:With the rapid development of communication technology, the spectrum becomes a scarce resource. the problem of low frequency spectrum utilization seriously restricts the application of wireless communication. The cognitive radio technology is one of the key technologies in the future wireless communication system by dynamically utilizing the free spectrum resources for transmission, and greatly improving the frequency spectrum utilization rate. As a means of obtaining free spectrum information, detection technology plays an important role in cognitive radio technology. At a certain time, the idle spectrum resource is formed by the main user in the idle state, and the cognitive user in the idle state is used for detecting the performance and the effect that can improve the detection. In this paper, we have studied the optimization of cognitive radio network from the aspects of cooperative detection, joint detection and the allocation mechanism of free cognitive users, considering the use of idle cognitive users in the detection. The specific research results are as follows: 1. An idle cognitive user's detection model is proposed in the overlay transmission mode, and the detection performance of the cognitive network is obtained through the analysis. can solve the problem that an idle cognitive user needs extra consumption, and meanwhile, the throughput and the using efficiency of the cognitive network are taken into account, and the long optimization question of the idle cognitive user in the cooperation detection is proposed In the request of the principle of fairness, the free cognitive user is allowed to choose whether to cooperate, and an optimal calculation of the throughput and the efficiency is proposed. The theory and the simulation prove that the cooperative detection of the added idle cognitive user can improve the detection performance. can................................................................................................................................................................................................................................................. The method comprises the following steps of: firstly, considering the overflow probability that the main user receives the signal-to-noise ratio, and converting the optimization problem of the transmission power of the cognitive user into a 鈥漝etection-transmission鈥
本文编号:2423191
[Abstract]:With the rapid development of communication technology, the spectrum becomes a scarce resource. the problem of low frequency spectrum utilization seriously restricts the application of wireless communication. The cognitive radio technology is one of the key technologies in the future wireless communication system by dynamically utilizing the free spectrum resources for transmission, and greatly improving the frequency spectrum utilization rate. As a means of obtaining free spectrum information, detection technology plays an important role in cognitive radio technology. At a certain time, the idle spectrum resource is formed by the main user in the idle state, and the cognitive user in the idle state is used for detecting the performance and the effect that can improve the detection. In this paper, we have studied the optimization of cognitive radio network from the aspects of cooperative detection, joint detection and the allocation mechanism of free cognitive users, considering the use of idle cognitive users in the detection. The specific research results are as follows: 1. An idle cognitive user's detection model is proposed in the overlay transmission mode, and the detection performance of the cognitive network is obtained through the analysis. can solve the problem that an idle cognitive user needs extra consumption, and meanwhile, the throughput and the using efficiency of the cognitive network are taken into account, and the long optimization question of the idle cognitive user in the cooperation detection is proposed In the request of the principle of fairness, the free cognitive user is allowed to choose whether to cooperate, and an optimal calculation of the throughput and the efficiency is proposed. The theory and the simulation prove that the cooperative detection of the added idle cognitive user can improve the detection performance. can................................................................................................................................................................................................................................................. The method comprises the following steps of: firstly, considering the overflow probability that the main user receives the signal-to-noise ratio, and converting the optimization problem of the transmission power of the cognitive user into a 鈥漝etection-transmission鈥
本文编号:2423191
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