异构认知网络自适应频谱决策方法

To improve spectrum utilization and mini-mize interference to Primary User (PU), an adaptive spectrum decision method is proposed for Seconda-ry User (SU), while taking traffic load balancing and spectrum heterogeneity into consideration. Long-term statistics and current sensing results are integrated into the proposed decision method of spectrum access. Two decision methods, namely probability based and sensing based, are presented, compared and followed by performance analysis in terms of delay. For probability based spectrum deci-sion, Short-Time-Job-First (STJF) priority queuing discipline is employed to minimize average residual time and theoretical conclusion is derived in a novel way. For sensing based decision we treat the inter-rupted service of SU as newly incoming and re-de-cision process is initialized to find available spec-trum in a First-Available-First-Access (FAFA) fash-ion. Effect of sensing error in PHY layer is also an-alyzed in terms of extended average residual time. Simulation results show that, for relatively low arri-ving rate of SU traffic, the proposed spectrum deci-sion method yields at least a delay reduction of 39. 5% compared with non-adaptive method. The pro-posed spectrum decision can significantly improve delay performance even facing sensing errors, which cause performance degeneration to both PU and SU.     

具有约束条件的认知无线电网络最优频谱价格函数

保证主用户的QoS是认知无线电网络中必须遵循的原则之一.本文利用伺机频谱共享方式中的实际约束条件,求解出既能保证主用户的QoS,又能使主业务运营商获得较大利润的最优频谱价格函数.该价格函数能反映实际的通信环境(如信道质量、业务动态性)对频谱价格的影响,同时在动态环境下,通过迭代可以使频谱价格收敛到最优价格.仿真结果表明...     

认知无线电网络中基于博弈论的频谱切换方法

提出了一种认知无线电网络(Cognitive Radio Networks,CRN)中基于博弈论的频谱切换方法.首先,定义次用户(Secondary User,SU)的期望累积切换时延,并将其作为优化目标.其次,将频谱切换问题表征为Stochastic博弈,其至少存在一个混合策略纳什均衡(Nash Equilibriu...     

认知无线电网络中频谱分配算法

 随着新型无线业务的不断发展,频谱供需矛盾日益明显.认知无线电网络被认为是实现动态频谱共享、缓解频谱供需矛盾的重要途径,近年来相关研究受到了广泛关注.本文对认知无线电网络中的频谱分配研究进展进行了分析.论文首先介绍了认知无线电网络的技术背景,分析了认知无线电网络中频谱分配的关键问题和算法设计目标.在此基础上总结了主流频谱分配模型的设计思想与技术特点,并详细描述了各模型经典分配算法的实现机制.最后,对频谱分配研究趋势进行了展望.     

认知网络中基于中继的频谱资源分配

根据无线认知中继网络上、下行链路子载波的信道特性,研究认知网络的频谱资源分配,提出一种上、下行链路子载波联合优化的分配算法。该算法根据子信道增益差值因子的大小分配下行链路子载波,以源节点和中继节点功率最小化为优化目标配对上行链路子载波,以用户的实时需求分配子载波的比特和功率,有效降低了系统的发射功率,提高了系统吞吐量。仿真结果表明,与启发—集中式和分布式辅助反馈传输功率分配算法比较,该联合优化算法的单位比特功耗降低了1.5~3 dBμW,误比特率性能提高了1个数量级左右。     

一种认知无线Adhoc网络跨层最优频谱共享方案

针对由认知无线电组成的Adhoc网络中的频谱共享提出了一个跨层方案。通过综合考虑认知用户之间的干扰约束和流量需求,特别考虑到用户双向连接以及信道的非均匀性,以BFP（bandwidth—footprint-product）值最小为目,建立了一个二进制整数线性规划（BILP）模型。仿真结果给出了最优的频带分配和路由选择方案。     

认知无线电网络中频谱感知算法研究

基于已有研究成果,对认知无线电中频谱感知算法进行了研究,涉及了几种常用经典算法及改进型新算法,并时各算法的优缺点分别进行了分类比较研究,探讨了未来可能的研究方向.     

多跳认知无线Mesh网络中频谱决策与路由的跨层设计

灵活高效的频谱共享技术能够实现频谱资源的最大化利用,有效地缓解了无线频谱紧缺的现状。在一个与TDMA/FDMA蜂窝网络共存的多跳认知无线Mesh网络环境下,基于覆盖式（Underlay）与机会式（Overlay）相结合的频谱共享机制,提出了一种有效的链路层频谱决策与网络层路由的跨层设计方案。仿真结果表明,该方案比基于最小跳数的随机信道选择算法在网络端到端传输性能上有显著的优势。     

认知容量收集网络中网络切片频谱共享策略

针对认知容量收集网络中网络切片的频谱共享问题,提出了一种认知容量收集网络中网络切片频谱共享策略.通过建立4D冲突图模型,提出了无冲突节点集的求解方法,并建立了频谱共享的机会容量模型,推导了非授权信道的机会容量,联合4D冲突图模型和机会容量模型提出了一种认知容量收集网络中网络切片频谱共享策略.仿真结果表明,与现有算法相比...     

认知无线电最优线性协作宽带频谱感知

本文提出了基于最优线性协作的宽带频谱感知方案。通过次级用户之间的协作,为认知网络的频谱感知提供分集,利用融合中心融合多个次级用户的宽带频谱感知数据来获取最优权重,并生成全局判决统计量,最终使用全局阈值完成最后的检测判决。由于所提出的两种协作宽带感知方案或需要精确地估计授权用户的信号强度和噪声方差,或感知性能不足,因而,还提出一种更易实现的方案。理论分析和仿真结果表明,本文所提出的协作感知方案可以有效地提高频谱感知性能,并且性能优于传统等增益合并方案。      

Effective autocorrelation-based spectrum sensing technique for cognitive radio network applications

Spectrum sensing based on detection techniques enables cognitive radio networks to detect vacant frequency bands. The spectrum sensing gives the opportunity to increase the radio spectrum channels re-utilization. However, the main challenge in spectrum sensing is the simplicity of the considered detection approach and the amount of prior information needed to make an accurate decision. This paper proposes a novel sensing technique based on the autocorrelation function. This novel approach is based on the speed of convergence to zero of all autocorrelation coefficients. This technique shows the highest probability of detection for the same probability of false alarm target at low signal-to-noise ratio (SNR) compared with many standard detection techniques. The proposed method has been implemented using GNU Radio software and SDR (software-defined radio) platforms. The experimental results show the effectiveness of the proposed method under real scenarios.     

Sensor node-assisted asynchronous cooperative spectrum sensing for cognitive radio network

In order to take advantage of the asynchronous sensing information, alleviate the sensing overhead of secondary users （SUs） and improve the detection performance, a sensor node-assisted asynchronous cooperative spectrum sensing （SN-ACSS） scheme for cognitive radio （CR） network （CRN） was proposed. In SN-ACSS, each SU is surrounded by sensor nodes （SNs）, which asynchronously make hard decisions and soft decisions based on the Bayesian fusion rule instead of the SU. The SU combines these soft decisions and makes the local soft decision. Finally, the fusion center （FC） fuses the local soft decisions transmitted from SUs with different weight coefficients to attain the final soft decision. Besides, the impact of the statistics of licensed band occupancy on detection performance and the fact that different SNs have different sensing contributions are also considered in SN-ACSS scheme. Numerical results show that compared with the conventional synchronous cooperative spectrum sensing （SCSS） and the existing ACSS schemes, SN-ACSS algorithm achieves a better detection performance and lower cost with the same number of SNs.     

Detection efficiency of cooperative spectrum sensing in cognitive radio network

Sensing the spectrum in a reliable and efficient manner is crucial to cognitive radio. To combat the channel fading suffered by the single radio, cooperative spectrum sensing is employed, to associate the detection of multiple radios. In this article, the optimization problem of detection efficiency under the constraint of detection probability is investigated, and an algorithm to evaluate the required radio number and sensing time for maximal detection efficiency is presented. To show the effect of cooperation on the detection efficiency, the proposed algorithm is applied to cooperative sensing using the spectral correlation detector under the Rayleigh flat fading channel.     

Exploiting temporal and spatial diversities for spectrum sensing and access in cognitive vehicular networks

In cognitive vehicular networks (CVNs), spectrum sensing and access are introduced as the promising technologies to fully exploit the underutilized licensed spectrum. Because the sensing ability of a single secondary vehicular user (SVU) is affected by high mobility, dynamic topology, and unreliable wireless environment, collaborative sensing is developed to increase the sensing accuracy and efficiency. Generally, the synchronization is required in the collaborative sensing in CVN. However, it is difficult to keep all SVUs synchronized with others for sensing under the high dynamic network topology, and the sensing overhead of the synchronous cooperative action may be significant. In this paper, we first propose an asynchronous cooperative sensing scheme in which each SVU provides an energy information (EI) that is tagged with location and time information. The sensing decision will be made on account of the EI. Considering the temporal and spatial diversities of each SVU, we assign different weights to each EI and formulate the probabilities of detection and false alarm as the optimization problems to find the optimal weight of each EI. Then, based on the asynchronous sensing, the specifications of the opportunistic spectrum access mechanism are elaborated in both centralized and decentralized CVNs for the sake of practical implementation. We analyze the system performance in terms of achievable throughput and transmission delay. Numerical results show that the proposed scheme is able to achieve substantially higher throughput and lower delay, as compared with existing schemes. Copyright © 2014 John Wiley & Sons, Ltd.     

Performance evaluation of sequential spectrum handoff in cognitive radio networks: a single‐user case

Powerful spectrum handover schemes enable cognitive radios to exploit transmission opportunities in primary users’ channels appropriately. In this paper, modeling and performance evaluation of sequential spectrum sensing strategy are addressed. To this end, the average number of handovers required for finding a transmission opportunity is evaluated assuming that a prior knowledge of the presence and absence probabilities of the primary users is available. Moreover, the average throughput maximization of a secondary user by optimizing its spectrum sensing time is formulated, and a set of illustrative numerical results is then presented to validate the analytical analysis.Copyright © 2013 John Wiley & Sons, Ltd.     

基于业务感知的协作频谱决策机制研究

认知无线电频谱分配中为了追求绝对公平而采用随机分配策略,然而由于次用户业务服务质量要求不同,会导致用户中断概率增加.在主用户行为预测模型中应用二状态马尔科夫链,主用户出现概率随机产生,不能反映信道真实状态.为此,提出时频联合检测与实时监测相结合的主用户行为预测,在频谱分配中区分业务类型.仿真结果显示,所提算法次用户平均中断概率优于随机分配机制.     

Dynamic control approaches of spectrum sensing in multi-band cognitive radio networks

In this paper,the dynamic control approaches for spectrum sensing are proposed,based on the theory that prediction is synonymous with data compression in computational learning. Firstly,a spectrum sensing sequence prediction scheme is proposed to reduce the spectrum sensing time and improve the throughput of secondary users. We use Ziv-Lempel data compression algorithm to design the prediction scheme,where spectrum band usage history is utilized. In addition,an iterative algorithm to find out the optimal number of spectrum bands allowed to sense is proposed,with the aim of maximizing the expected net reward of each secondary user in each time slot. Finally,extensive simulation results are shown to demonstrate the effectiveness of the proposed dynamic control approaches of spectrum sensing.     

认知自组网分布式频谱感知最优协作算法

认知无线电技术使得自组织网络节点能够充分利用空闲频谱资源,提高了传输性能。通过协作频谱感知,可有效解决由于无线信道存在阴影、噪声和衰落等情况导致的单节点感知准确性偏低。为了解决梯度算法随着协作节点数量增大后计算复杂度变高,文中提出部分梯度算法ψ-GBCS,该模型通过基于SNR的动态阈值保证了感知准确性,同时通过最佳协作节点数提高了感知效率。仿真结果表明,该模型下,综合评估系统效率和性能的J函数值提高37%,能耗降低50%,有效保证大规模认知自组网频谱感知的鲁棒性,降低了对主用户的干扰及设备功耗。     

Maximizing the lifetime of energy‐constrained cooperative spectrum sensing sensor network

Adding the cognitive capability to wireless sensor networks allows the sensors to monitor the spectrum and identify the spectrum holes to operate in different frequencies according to the radio environment which result in better spectrum utilization. Spectrum sensing is a main component in any cognitive radio network. In this paper, we propose a new cooperative sensing scheme based on energy detection for cognitive sensor networks which is constrained by the energy limitation of the wireless sensor elements. The proposed scheme minimizes the sensing energy for individual sensor and carefully selects the suitable participant sensors in each cooperative sensing process. This results in maximizing the lifetime of energy‐constrained wireless sensor networks. The proposed scheme also takes into consideration the constraints on the detection accuracy. The simulation results show that the proposed scheme prolonged the lifetime of the cognitive network, makes efficient usage of available spectrum by secondary users, and satisfy the target detection performance.     

Optimal fusion rule for cooperative spectrum sensing in cognitive radio networks

In this paper,we analyze performance of cooperative spectrum sensing under counting rules when exponential model is utilized to characterize the burst nature of primary user(PU) link.Our objective is to minimize the average error probability(AEP) so that the link utilization in the considered link achieves its maximum.We derive a closed-form expression of AEP as well as the probability of interference(PoI) by classifying cognitive transmission into six events.Then,we consider the minimization of AEP over counting rules under the constraint of interference.As the solution,we develop an efficient algorithm to evaluate the optimal fusion rule.Finally,we verify our analysis in numerical results.