主用户活跃性对新的认知无线电系统容量的影响

在机会频谱接入认知无线电系统中,认知用户只有在通过感知确定信道空闲时才可以接入授权信道,因此频谱感知对于系统性能影响非常重要。本文提出了基于新帧结构的四元频谱感知模型,考虑主用户活跃性对认知网络吞吐量的影响,采用可同时最大化感知和数据传输时间的新帧结构模型,不需要考虑感知和吞吐量的均衡。理论分析新模型下感知时间,主用户活跃性,目标检测概率,主用户接收信噪比对系统吞吐量的影响,并与传统模型进行对比。     

基于POMDP的次用户多时隙信道选择算法

为了最大化次用户系统吞吐量,同时减少信道选择过程中的计算量,在多条主用户信道的次用户多时隙系统中,用半马尔科夫链(Partially Observable Markov Decision Process,POMDP)理论刻画次用户信道选择过程,并提出了对应的信道选择算法。在每一个时隙开始时,次用户选择部分信道进行感知,之后根据感知结果选择不同的传输功率接入信道,对于没有被感知的信道,次用户可以直接接入。对POMDP问题求解采用了最优策略、次优策略以及任意策略,并通过在不同仿真环境下的数值分析比较,论证了所提算法的实用有效。     

认知无线电网络中次用户平均吞吐量的优化

协作频谱感知的认知无线电网络中,已有研究表明增加参与协作频谱感知的次用户数量能够提高感知性能,进而提高信道吞吐量。然而,由于信道容量的限制,不断增加参与协作感知的次用户数量并不会使信道吞吐量无限提高,反而会使次用户平均可获得的吞吐量不断降低。针对上述问题,该文以次用户平均吞吐量为优化目标,证明多信道条件下,对于任意给定的融合参数,次用户的平均吞吐量是感知时间的凸函数,并提出交叉迭代算法进行2维优化。仿真结果表明,当信噪比为-10 dB时,次用户使用交叉迭代算法获得的平均吞吐量较已有算法可提高20%以上。     

基于业务请求数据包长度的机会频谱接入算法

针对非时隙主用户网络,研究了单个次用户在周期性感知框架下的机会频谱接入问题。通过建立次用户信道感知和接入模型,提出了一种基于次用户请求业务数据包长度的机会频谱接入算法。该算法根据每个时隙分配给次用户业务数据包长度,自适应调整机会频谱接入策略。仿真结果表明,所提算法能够在干扰水平要求较高情况下,提高次用户平均有效传输吞吐量的同时,实现有效吞吐量与碰撞概率的折中;同时当外部环境发生变化时算法具有较强的鲁棒性。     

Q学习算法在机会频谱接入信道选择中的应用

针对“先听后传”的机会频谱接入中认知用户的信道选择问题,本文提出了一种基于Q学习的信道选择算法。在非理想感知的条件下,通过建立认知用户的信道选择模型并设计恰当的奖励函数,使智能体能够与未知环境不断交互和学习,进而选择长期累积回报最大的信道接入。在学习过程中,本文引入了Boltzmann实验策略,运用模拟退火思想实现了资源探索与资源利用之间的折衷。仿真结果表明,所提算法能够在未知环境先验知识条件下可以快速选择性能较好的信道接入,有效提高认知用户的接入吞吐量和系统的平均容量。      

碰撞约束和非理想感知下认知多址接入信道的有效吞吐量

在碰撞约束和非理想信道信息下,研究认知多址接入信道中多个次用户接入主用户频谱的有效吞吐量问题。在机会频谱接入方式（OSA）下,定义有效吞吐量并作为次用户的性能指标。考虑信道质量信息,在信道质量分布参数未知的情况下采用有限的信道样本对参数进行估计,并对非理想感知下次用户的性能进行分析。通过仿真实验,分别讨论了信道质量门限与碰撞概率及次用户性能之间的关系,对比了理想感知和非理想感知下次用户的性能,并与其他算法进行对比,验证了本文接入策略的有效性。     

CVANET中基于POMDP模型的频谱接入算法

针对认知车载Ad Hoc网络(CVANET)信道的动态特性,以部分可观测马尔科夫决策过程(POMDP)为模型对认知车辆用户的频谱感知和频谱接入过程进行研究,提出基于POMDP模型的分布式机会频谱接入算法,并通过贪心算法降低POMDP算法计算量,最后通过仿真研究影响认知车辆用户吞吐量的主要因素,并验证算法的可行性。仿真分析结果表明,认知车辆用户通过本算法接入吞吐量得到有效提高,降低了交通中广播风暴的可能,并且降低了计算量。     

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。     

基于频谱数据库的分布式动态频谱接入算法

频谱数据库是一种直接获得频谱信息的方式,针对在复杂多变的网络环境中用户之间缺少信息交互,研究了数据库协助和没有数据库情况下的动态频谱接入算法。一方面,次用户通过历史感知数据估计信道的可用性而进行信道选择,另一方面,次用户通过数据库获得更加可靠的频谱信息从而做出策略。证明了用户之间的博弈是一个超模博弈,通过提出的分布式学习算法能收敛到一个纯策略纳什均衡点。仿真结果表明,提出的联合数据库感知算法和数据库协助算法比依靠感知的结果收敛更快,获得的系统吞吐量接近最大,减少了用户决策的时延,提高了频谱利用率。     

认知无线Mesh网络中基于马氏决策模型的MAC协议

为解决认知无线Mesh网络中专用控制信道较难获得的问题,提出一种基于POMDP的机会式频谱接入MAC协议,在不需要中心控制器和专用控制信道的协调下,实现动态频谱感知和接入。仿真结果表明,基于POMDP的接入策略能够有效提高网络频谱利用率和吞吐量,性能最优,而基于贪心算法的接入策略,在降低计算复杂度的同时,获得了较好的性能,实用性较强。     

多信道频谱感知与频谱接入的优化与折衷

基于贝叶斯推理的多信道频谱感知方法和思想,文章通过多个认知用户随机地选择部分信道进行协作感知并利用特殊设计的贝叶斯推理法则来快速有效地获取所有信道的活动状态。贝叶斯推理的多信道频谱感知方法也是多分辨率频谱感知的基础,具有重要的应用价值。文章还通过分析多用户多信道条件下频谱感知和频谱接入之间复杂而微妙的内在联系,对其进行适当地优化与折衷,以提高认知系统的性能和效率。     

Cluster‐based adaptive multispectrum sensing and access in cognitive radio networks

Spectrum sensing and access have been widely investigated in cognitive radio network for the secondary users to efficiently utilize and share the spectrum licensed by the primary user. We propose a cluster‐based adaptive multispectrum sensing and access strategy, in which the secondary users seeking to access the channel can select a set of channels to sense and access with adaptive sensing time. Specifically, the spectrum sensing and access problem is formulated into an optimization problem, which maximizes the utility of the secondary users and ensures sufficient protection of the primary users and the transmitting secondary users from unacceptable interference. Moreover, we explicitly calculate the expected number of channels that are detected to be idle, or being occupied by the primary users, or being occupied by the transmitting secondary users. Spectrum sharing with the primary and transmitting secondary users is accomplished by adapting the transmission power to keep the interference to an acceptable level. Simulation results demonstrate the effectiveness of our proposed sensing and access strategy as well as its advantage over conventional sensing and access methods in terms of improving the achieved throughput and keeping the sensing overhead low. Copyright © 2012 John Wiley & Sons, Ltd.     

On optimizing the backoff interval for random access schemes

To improve channel throughput and the fairness of random access channels, we propose a new backoff algorithm, namely, the sensing backoff algorithm (SBA). A novel feature of the SBA scheme is the sensing mechanism, in which every node modifies its backoff interval according to the results of the sensed channel activities. In particular, every active node sensing a successful transmission decreases its backoff interval by an additive factor of the transmission time of a packet. In order to find the optimum parameters for the SBA scheme, we have studied the optimum backoff intervals as a function of different numbers of active nodes (N) in a single transmission area with pure ALOHA-type channels. We find that the optimum backoff interval should be 4N times the packet transmission time when the random access channel operates under a pure ALOHA scheme. Based on this result, we have calculated numerically the optimum values of the parameters for SBA, which are independent of N. The SBA scheme operates close to the optimum backoff interval. Furthermore, its operation does not depend on a knowledge of N. The optimum backoff interval and the SBA scheme have been studied also by simulation. It is shown that the SBA scheme out-performs other backoff schemes, such as binary exponential backoff (BEB) and multiplicative increase linear decrease (MILD). As a point of reference, the SBA scheme offers a channel capacity of 0.19 when N is 10, while the MILD scheme can only offer 0.125. The performance gain is about 50%.     

面向实时业务的认知无线网络 MAC 层频谱接入方案

摘 要：为满足认知无线网络中宽带业务实时传输的需求,提出低延迟的MAC层频谱接入方案,包括频谱感知调度与信道接入竞争两部分。在频谱感知阶段,认知用户选取最佳可用信道数实现感知与传输的延迟最小化;在信道接入竞争阶段,协议考虑频谱资源动态变化的特点,通过设计数据帧格式以及邻居节点协同侦听机制,减小信道冲突与“聋终端”的影响。理论与实验结果表明,与传统的认知无线网络MAC层协议相比,提出的接入方案数据传输延迟更短,同时在授权信道空闲率较大时吞吐量性能略优。     

Sequential opportunistic spectrum access with imperfect channel sensing

In this paper, we exploit channel diversity for opportunistic spectrum access (OSA). Our approach uses instantaneous channel quality as a second criterion (along with the idle/busy status of the channel) in selecting channels to use for opportunistic transmission. The difficulty of the problem comes from the fact that it is practically infeasible for a cognitive radio (CR) to first scan all channels and then pick the best among them, due to the potentially large number of channels open to OSA and the limited power/hardware capability of a CR. As a result, the CR can only sense and probe channels sequentially. To avoid collisions with other CRs, after sensing and probing a channel, the CR needs to make a decision on whether to terminate the scan and use the underlying channel or to skip it and scan the next one. The optimal use-or-skip decision strategy that maximizes the CR’s average throughput is one of our primary concerns in this study. This problem is further complicated by practical considerations, such as sensing/probing overhead and sensing errors. An optimal decision strategy that addresses all the above considerations is derived by formulating the sequential sensing/probing process as a rate-of-return problem, which we solve using optimal stopping theory. We further explore the special structure of this strategy to conduct a “second-round” optimization over the operational parameters, such as the sensing and probing times. The aggregate throughput performance when a network of CRs coexist with primary radios is evaluated under homogeneous and heterogeneous spectrum environments, respectively. We show through simulations that significant throughput gains (e.g., about 100%) are achieved using our joint sensing/probing scheme over the conventional one that uses sensing alone.     

能量采集认知无线电系统中的最优频谱感知(英文)

In this paper,we consider a cognitive radio system with energy harvesting,in which the secondary user operates in a saving-sensing-transmitting(SST) fashion.We investigate the tradeoff between energy harvesting,channel sensing and data transmission and focus on the optimal SST structure to maximize the SU's expected achievable throughput.We consider imperfect knowledge of energy harvesting rate,which cannot be exactly known and only its statistical information is available.By formulating the problem of expected achievable throughput optimization as a mixed-integer non-linear programming one,we derive the optimal saveratio and number of sensed channels with indepth analysis.Simulation results show that the optimal SST structure outperforms random one and performance gain can be enhanced by increasing the SU's energy harvesting rate.     

Enhanced binary exponential backoff algorithm for fair channel access in the ieee 802.11 medium access control protocol

The medium access control protocol determines system throughput in wireless mobile ad hoc networks following the ieee 802.11 standard. Under this standard, asynchronous data transmissions have a defined distributed coordination function that allows stations to contend for channel usage in a distributed manner via the carrier sensing multiple access with collision avoidance protocol. In distributed coordination function, a slotted binary exponential backoff (BEB) algorithm resolves collisions of packets transmitted simultaneously by different stations. The BEB algorithm prevents packet collisions during simultaneous access by randomizing moments at stations attempting to access the wireless channels. However, this randomization does not eliminate packet collisions entirely, leading to reduced system throughput and increased packet delay and drop. In addition, the BEB algorithm results in unfair channel access among stations. In this paper, we propose an enhanced binary exponential backoff algorithm to improve channel access fairness by adjusting the manner of increasing or decreasing the contention window based on the number of the successfully sent frames. We propose several configurations and use the NS2 simulator to analyze network performance. The enhanced binary exponential backoff algorithm improves channel access fairness, significantly increases network throughput capacity, and reduces packet delay and drop. Copyright © 2013 John Wiley & Sons, Ltd.     

基于BP神经网络的认知无线电疑似跳转信道预测研究

为了在认知无线电环境中进行跟踪干扰,需要预测认知用户在遭受干扰后进行频谱切换的目标信道——疑似跳转信道,以便在这些疑似跳转信道上对其进行跟踪检测和干扰。提出了一种在多信道多用户条件下,认知用户在感知得到的空闲信道上智能决策并选择合适信道接入的频谱模型,并用基于BP神经网络的预测算法预测疑似跳转信道,为衡量预测效果,提出了预测切准率和预测精度两个参考标准,仿真结果验证了该算法的可行性。