认知MIMO系统基于授权通信模式信息的空频域机会接入

针对认知MIMO系统设计一种空频域机会接入策略。当存在频谱空洞时,认知业务以交叉共享方式实现传输;当无空闲频谱资源可用时,利用授权通信模式信息与系统间干扰信道信息,以重叠共享方式实现认知业务传输。该方法综合利用空间相关度与特征模式传输增益进行空域资源质量评价,实现授权频道与认知特征模式的选择分集。并且,基于授权通信模式信息的信号处理降低了认知系统天线配置要求以及认知信号的功率损失。仿真结果表明,所提策略能够在不影响授权业务的前提下显著改善认知业务性能,获得接近最佳的认知吞吐率。     

认知无线电网络基于F范数的频谱共享

针对多用户多输入多输出认知无线电网络的频谱共享问题,提出一种在保证授权用户服务质量要求的前提下,以认知网络容量最大化为目标的基于F范数的频谱共享方法.该方法利用信道矩阵的F范数选择认知用户以获得认知网络的多用户分集增益,并采用两次选择的方式降低算法的复杂度,通过将认知用户的发射信号投射到干扰信道的零空间来避免认知用户对...     

基于认知无线电的WiMAX系统多用户资源分配算法

基于认知无线电(CR)和OFDM的传输特性,提出了一种WiMAX系统多用户资源分配算法.感知用户根据认知到的频谱资源信息,在不对授权用户造成干扰的前提下使用空闲频谱资源.利用注水算法对多个用户请求使用同一子载波进行裁决,在满足传输速率以及QoS要求下,完成子载波、比特和功率分配,使整个系统传输功率最小.仿真结果表明,基于认知无线电的WiMAX系统多用户资源分配算法的传输功率和性能明显优于使用静态算法方案的系统.     

多天线认知无线电网络自适应空间映射频谱共享

针对集中式多用户多天线认知无线电网络,提出一种基于自适应空间映射的频谱共享策略,根据授权用户接入的随机性所带来的频谱和空间资源占用情况的变化,认知系统将发射信号自适应地映射在认知基站与授权用户之间信道的子空间上,避免或抑制系统间干扰,从而在保证授权用户通信质量的前提下,为认知用户提供通信机会,并且在认知系统内部利用块对角化和奇异值分解方法分离不同认知用户的信号,消除系统内干扰。性能分析和仿真结果表明,与已有的频谱共享方法相比较,该策略不仅具有更高的认知系统可达和速率,并且对于由不同授权系统负载情况形成的不同场景具有更强的鲁棒性。     

上海地区白频谱应用规范的分析研究（下）

认知用户的无线电频谱感知技术 1．频谱感知技术分类 认知用户,需要利用频谱感知技术侦测授权频段的频率空闲情况,从中找出可使用信道。当授权用户重新接入时,即需检测到．并迅速让出占用频道．切换到其他空闲频道或中止通信,以避免授权用户受到干扰。     

OFDM混合调制信号在认知网络中的应用

对OFDM混合调制信号进行了谱相关分析,并将其应用到认知网络中。在采用OFDM混合调制信号传输的认知网络中,利用信号的循环平稳特性可以快速识别出信道空闲、认知信号传输、主用户信号传输和两种信号混叠这四种状态,为认知网络中认知信号的快速可靠接入和退出提供了保障。进一步利用混合调制的位置来标记不同的认知网络,认知节点可以区分不同认知网络信号。在提出的认知网络应用模型中,传输和接收设备较简单,而且不需专门的信道来传输控制命令,节约了带宽。仿真结果显示,在认知网络中,该模型可以提高频谱利用率,使通信系统的性能得到改善。     

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。     

认知无线电网络子空间映射频谱共享

针对集中式多用户多天线认知无线电网络,分析了子空间映射能够为认知系统提供的通信机会以及理想信道条件下的空间子信道分配方案。在此基础上,提出了一种基于子空间映射的频谱共享策略。根据认知系统的感知结果,计算可以利用的空间子信道数,通过认知用户接入控制和子空间映射避免或抑制系统间干扰,从而在保证授权用户通信质量的前提下,为认知用户提供通信机会。仿真结果表明,与已有的子空间映射频谱共享方法相比,该策略不仅具有更高的认知系统可达和速率,而且能够为认知系统提供更多通信机会。     

感知不确定下卫星认知无线网络多频谱接入优化策略

针对卫星认知无线网络频谱感知不确定性较大导致传统频谱接入机制效率降低的问题,该文提出一种基于动态多频谱感知的信道接入优化策略。认知LEO卫星根据频谱检测概率与授权用户干扰门限之间的关系,实时调整不同频谱感知结果下的信道接入概率。在此基础上以系统吞吐量最大化为目标,设计了一种基于频谱检测概率和虚警概率联合优化的判决门限选取策略,并推导了最佳感知频谱数量。仿真结果表明,认知用户能够在不大于授权用户最大干扰门限的前提下,根据授权信道空闲状态动态选择最佳频谱感知策略,且在检测信号信噪比较低时以更加积极的方式接入授权频谱,降低了频谱感知不确定性对信道接入效率的影响,提高了认知系统吞吐量。     

认知MIMO系统基于部分投影的干扰抑制算法

为实现认知多输入多输出(MIMO)系统中主次用户的高效频谱共享,提出一种基于部分投影的干扰抑制算法,该算法将主用户的空间传输特性引入次用户的设计中。分析了干扰的结构化特征,指出泄漏到主用户空闲维度上的次用户信号并非有害干扰。通过将次用户发射信号投影到干扰信道低维子空间的零空间上即可消除干扰。理论分析和仿真表明,算法在有效消除对主用户干扰的同时,次用户传输速率和算法适用性相比传统算法都有显著提升。     

认知无线电网络基于空分复用的机会频谱接入

传统的认知无线电技术采用机会频谱接入,认知用户的通信质量难以得到保证。通过利用多天线技术提供的空间信号处理能力,该文提出一种基于空分复用的机会频谱接入方法,当存在频谱空洞时,采用传统的机会频谱接入;无空闲频率资源可用时,认知系统利用空域信息完成发射预编码与接收滤波,从而以空分复用的方式实现通信。文中对认知系统天线配置要求进行了分析,当满足该要求时,认知系统能够与授权系统在同一授权频道实现无互扰共存。仿真结果表明,与传统的机会频谱接入相比,所提方法能够在不影响授权系统性能的情况下有效改善认知系统的通信性能。     

Adaptive Beamforming in CR-MIMO System Exploiting Multi-Channel and Eigenmode Diversity

In this paper, a space division multiplexing (SDM) based joint adaptive beamforming downlink transmission scheme for infrastructured cognitive radio multi-input multi-output system is proposed. When spectrum holes exist traditional opportunistic spectrum access is employed. While there is no idle spectrum available, cognitive base station and cognitive user firstly evaluate spatial correlation between cognitive transmission and inter-system interference based on the preprocessed channel information. Incorporated with transmission gain, authorized frequency channel and eigenmode are jointly selected. Then on the premise that no interference is imposed on the primary, cognitive transmission is carried out adopting SDM. The proposed scheme exploits selection diversity of both authorized channel and cognitive eigenmode, thus achieves better spatio-frequency resource management and near-optimal cognitive throughput performance.     

分布式认知无线电网络中的主动频谱切换

主要研究分布式认知无线电网络中的频谱切换,并提出一种主动的频谱切换方案。认知用户通过感知结果与历史信息建立授权用户的活动模型,预测频谱的使用情况。在不对授权用户产生干扰的前提下,提前安排频谱切换。同时,采用基于蚁群任务分工算法的频谱选择方法,使得认知用户综合考虑通信参数,实现认知用户的按需切换,保证不间断的传输。仿真表明,提出的方案能够提前执行切换,并在较短的时间内完成业务量的传输,同时具备较高的灵活性,适用于分布式认知无线电网络。     

基于OFDM认知无线电网络的最优路径算法

针对基于正交频分复用的分布式认知无线电网络,在考虑频谱移动特性的同时,为保证认知用户数据传输的可靠性,提出一种最优路径的基于OFDM认知无线电网络路由算法.该算法首先通过建立认知无线电网络的模型计算信道的期望传输时间和信道干扰,结合类似于按需路由的基本流程得到所有可能的路径.最后,根据最小累积期望传输时间和路径平均吞吐量的指标来选择最优路径.仿真的结果表明,OROCR算法可以明显地减少平均端到端时延,大大地提高平均端到端吞吐量.     

Adaptive dual‐radio spectrum‐sensing scheme in cognitive radio networks

In this paper, a novel spectrum‐sensing scheme, called adaptive dual‐radio spectrum‐sensing scheme (ADRSS), is proposed for cognitive radio networks. In ADRSS, each secondary user (SU) is equipped with a dual radio. During the data transmission, with the received signal‐to‐noise ratio of primary user (PU) signal, the SU transmitter (SUT) and the SU receiver (SUR) are selected adaptively to sense one channel by one radio while communicating with each other by the other one. The sensing results of the SUR are sent to the SUT through feedback channels (e.g., ACK). After that, with the sensing results from the SUT or the SUR, the SUT can decide whether the channel switching should be carried out. The theoretical analysis and simulation results indicate that the normalized channel efficiency, defined as the expected ratio of time duration without interference to PUs in data transmission to the whole frame length, can be improved while satisfying the interference constraint to PUs. After that, an enhanced ADRSS is designed by integrating ADRSS with cooperative spectrum sensing, and the performance of ADRSS under imperfect feedback channel is also discussed. Copyright © 2011 John Wiley & Sons, Ltd.     

Spectrum sensing in cognitive radio using goodness of fit testing

One of the most important challenges in cognitive radio is how to measure or sense the existence of a signal transmission in a specific channel, that is, how to conduct spectrum sensing. In this letter, we first formulate spectrum sensing as a goodness of fit testing problem, and then apply the Anderson-Darling test, one of goodness of fit tests, to derive a sensing method called Anderson-Darling sensing. It is shown by both analysis and numerical results that under the same sensing conditions and channel environments, Anderson-Darling sensing has much higher sensitivity to detect an existing signal than energy detector-based sensing, especially in a case where the received signal has a low signal-to-noise ratio (SNR) without prior knowledge of primary user signals.     

Cognitive Engine with Dynamic Priority Resource Allocation for Wireless Networks

A cognitive engine with dynamic priority resource allocation (CE-DPRA) is proposed for wireless networks by utilizing a maximum likelihood estimation method. The receiving signal strength (RSS) from an unlicensed cognitive radio (CR) user can be measured through estimating the unknown position of a licensed mobile user. The priority algorithm for access control enables the selection of a proper CR user waiting for transmission. Both data rate and spectral efficiency can be increased after adapting CE-DPRA. Also the power constraint method can avoid excessive interference caused by signal transmitting from CR users so as to improve the communication quality of mobile users. Simulation results show that the proposed CE-DPRA achieves the performance of high transmission data rate, less interference power, and low average outage probability.     

Blind Spectrum Sensing Algorithms for Cognitive Radio Networks

In a cognitive radio network, the spectrum that is allocated to primary users can be used by secondary users if the spectrum is not being used by the primary user at the current time and location. The only consideration is that the secondary users have to vacate the channel within a certain amount of time whenever the primary user becomes active. Thus, the cognitive radio faces the difficult challenge of detecting (sensing) the presence of the primary user, particularly in a low signal-to-noise ratio region, since the signal of the primary user might be severely attenuated due to multipath and shadowing before reaching the secondary user. In this paper, a blind sensing algorithm is derived, which is based on oversampling the received signal or by employing multiple receive antennas. The proposed method combines linear prediction and QR decomposition of the received signal matrix. Then, two signal statistics are computed from the oversampled received signal. The ratio of these two statistics is an indicator of the presence/absence of the primary signal in the received signal. Our algorithm does not require the knowledge of the signal or of the noise power. Moreover, the proposed detection algorithm in this paper is blind in the sense that it does not require information about the multipath channel distortions the primary signal has undergone on its way to reaching the secondary user. Simulations have shown that our algorithm performs much better than the commonly used energy detector, which usually suffers from the noise uncertainty problem.