认知无线电网络的MAC协议机制

针对认知无线电网络可用信道资源随时间和空间环境变化的特点,分析了认知无线电网络MAC协议面临的问题,提出了一种基于全局控制信道的MAC协议方案,在此基础上阐述了两种接入方式及相应适用场合,实现了认知无线电节点对可用信道资源变化的感知,可为认知无线电MAC协议研究及应用提供参考.     

认知无线电网络安全研究

分析了认知无线电网络中存在的两类安全隐患,归纳了针对这些安全隐患的网络攻击行为,介绍了目前安全领域的研究成果,并探讨了认知无线电网络安全技术未来的发展方向.     

选用最优数据信道的认知无线电MAC协议

如何选择数据信道进行数据的传输是认知无线电网络MAC协议的重要环节,许多现有的MAC机制在选用数据信道没有考虑信道的可用概率,而可用概率对网络性能有着重要的影响。提出了一种最优信道传输的认知无线电MAC协议。次用户在频谱感知后,选用质量最优数据信道对数据进行传输,评价信道质量的标准中考虑了信道的可用概率。最后,使用ns...     

认知无线电协作频谱感知技术综述

频谱感知是实现认知无线电功能的前提条件,也是认知无线电领域的一个研究热点.近年来人们提出了很多种频谱感知方法,尤其协作感知技术日益受到关注.综述了协作频谱感知技术的最新研究进展,先描述典型的认知无线电协作频谱感知模型,然后讨论了协作感知中信息融合及性能分析等关键问题,最后指出了协作感知的研究挑战和发展趋势.     

认知无线电研究综述

认知无线电是一种智能频谱共享技术。它通过感知频谱环境、智能学习并实时调整其传输参数,实现频谱的再利用,可显著地提高频谱的利用率,特别是可让未授权用户使用授权用户的频谱。本文在概述了认知无线电技术的发展之后,重点介绍了认知无线电技术及其应用的研究现状,包括认知无线电的总体概念、相关频谱政策和标准化工作进展、相关关键技术和安全技术的研究,以及它在UWB、Mesh、WRAN中的应用现状。最后对认知无线电技术的发展进行了展望。     

认知无线电技术综述

认知无线电是一种智能频谱共享技术,通过它授权用户的频谱资源得以利用.认知无线电具备感知无线通信环境、并可根据一定的学习和决策算法,实时、自适应地改变系统工作参数的能力,通过对它的利用实现了对无线频谱资源的动态使用,提高频谱资源的利用率.在对认知无线电技术提出的背景、认知无线电技术概念的发展和主要研究内容进行了介绍.     

认知无线电技术分析

近年来,可用频谱严重缺乏的现状促使人们开始寻求新的方法来提高无线频谱使用效率,认知无线电技术便是这种备受瞩目的通信方式。文章简述认知无线电及认知环的概念,阐述认知无线电技术的四个关键技术:频谱检测(感知)、频谱管理、频谱移动、频谱共享,分析认知无线电技术的应用场景,最后探讨动态频谱分配技术,指出动态频谱设计两个体系框架及设计过程中需要注意的问题。     

基于纳什议价的认知无线电合作感知算法

认知无线电技术是建立在授权空闲频谱或非授权频谱基础上,主用户(Licensed user)享有优先频谱使用权,为保护主用户的正常需求,提高探测数据的可靠性,提出了一种新型两人纳什议价合作感知NBS算法,利用两个用户的合作感知信息,根据认知用户(secondary users)与主用户基站的距离和能量的差异,自适应的调节能量检测门限.并将多用户合作感知指派分组为两人纳什合作议价问题,结合"AND"规格融合每组感知数据,在保障主用户通信基础上,提高频谱利用率.仿真结果表明,NBS算法可有效的提高数据可信度,该算法与"AND"数据融合规则相比,感知数据的探测概率显著提高.     

基于认知无线电系统合作检测的数据融合研究

从多传感器数据融合角度研究认知无线电系统合作感知问题.对于特定的在线用户规模,存在一个最佳的参与部分融合用户数,可以固定选择每次检测的融和用户数使系统平均检测概率最大.在此基础上提出一种基于测量的融合方法,根据每次检测的接收信号状况动态调整参与融合的用户数.仿真表明,这种方法可以使系统检测性能在60%以上的检测中相对于固定融合用户数的方法有所提高.     

认知无线电技术综述

讨论了在感知学习循环的过程中的频谱检测,动态自适应频谱分配,以及频谱管理等关键技术,并对认知无线电应用的场景进行了分析及展望.     

On cognitive processes in cognitive radio networks

In this article we model the cognitive processes and evaluate their impact on the performance of cognitive radio networks (CRN). Operation of the cognitive radio nodes, can be characterized by two types of processes: communication processes such as packets transmission, and cognitive processes such as estimation of the network state and decision-making for dynamic resource allocation. We propose a continuous time Markov chain model of CRN that couples these processes into unified queueing framework and analyze it by means of the matrix-geometric approach. From the obtained results, we derive the performance measures of CRN such as average delay and throughput, and establish their dependencies on the underlying cognitive processes. Additionally, we design an efficient policy for accessing the vacant channels and managing the transmission-sensing trade-off, which arises when transmissions and sensing are mutually exclusive. The policy search is carried out by the stochastic optimization method of cross-entropy. The optimized policy leads to significantly enhanced performance of CRN.     

Securing cognitive radio networks

Cognitive radio is a promising technology aiming to improve the utilization of the radio electromagnetic spectrum. A cognitive radio device uses general purpose computer processors that run radio applications software to perform signal processing. The use of this software enables the device to sense and understand its environment and actively change its mode of operation based on its observations. Unfortunately, this solution entails new security challenges. Our objective in this paper is to analyze the security issues of the main recent developments and architectures of cognitive radio networks. We present vulnerabilities inherent to those systems, identify novel types of abuse, classify attacks, and analyze their impact on the operation of cognitive radio‐based systems. Moreover, we discuss and propose security solutions to mitigate such threats. Copyright © 2010 John Wiley & Sons, Ltd.     

Toward secure distributed spectrum sensing in cognitive radio networks [cognitive radio communications and networks]

Cognitive radio is a revolutionary technology that promises to alleviate the spectrum shortage problem and to bring about remarkable improvement in spectrum utilization. Spectrum sensing is one of the essential mechanisms of CR and is an active area of research. Although the operational aspects of spectrum sensing are being studied actively, its security aspects have attracted very little attention. In this paper, we discuss security issues that may pose a serious threat to spectrum sensing. Specifically, we focus on two security threats - incumbent emulation and spectrum sensing data falsification - that may wreak havoc in distributed spectrum sensing. We also discuss methods for countering these threats and the technical hurdles that must be overcome to implement such countermeasures.     

A survey on spectrum management in cognitive radio networks [cognitive radio communications and networks]

Cognitive radio networks will provide high bandwidth to mobile users via heterogeneous wireless architectures and dynamic spectrum access techniques. However, CR networks impose challenges due to the fluctuating nature of the available spectrum, as well as the diverse QoS requirements of various applications. Spectrum management functions can address these challenges for the realization of this new network paradigm. To provide a better understanding of CR networks, this article presents recent developments and open research issues in spectrum management in CR networks. More specifically, the discussion is focused on the development of CR networks that require no modification of existing networks. First, a brief overview of cognitive radio and the CR network architecture is provided. Then four main challenges of spectrum management are discussed: spectrum sensing, spectrum decision, spectrum sharing, and spectrum mobility.     

Fuzzy logic for cross-layer optimization in cognitive radio networks [cognitive radio communications and networks]

The search for the ultimate architecture for cross-layer optimization in cognitive radio networks is characterized by challenges such as modularity, interpretability, imprecision, scalability, and complexity constraints. In this article we propose fuzzy logic as an effective means of meeting these challenges, as far as both knowledge representation and control implementation are concerned.     

Dynamic cooperator selection in cognitive radio networks

The primary objective of cooperation in cognitive radio (CR) networks is to increase the spectrum access efficiency and improve the network performance. However, Byzantine adversaries or unintentional erroneous conduct in cooperation can lead to destructive behavior of CR users that can decrease their own and others’ performances. This work presents a dynamic solution for cooperation reliability in conditions with constraints typical for a CR network. Specifically, in CR networks, the information on the success of cooperation can be limited only to cases with interference; when malicious, cooperators can be completely non-correlated and can alter behavior; and the set of available cooperators can dynamically change in time. In order to face these challenges, each CR user autonomously decides with whom to cooperate by learning cooperators behavior with a reinforcement learning (RL) algorithm. This RL algorithm determines the suitability of the available cooperators, and selects the most appropriate ones to cooperate with the objective to increase the efficiency of spectrum access in CR networks. The simulation results demonstrate the learning capabilities of the proposed solution and especially its reliable behavior under highly unreliable conditions.     

Enhancing throughput in multi-radio cognitive radio networks

Wireless Networks - In recent years, cognitive radio networks (CRNs) have been widely investigated to solve the well-known spectrum scarcity problem through enhancing spectrum utilization. Another...     

On detecting termination in cognitive radio networks

The cognitive radio networks are an emerging wireless communication and computing paradigm. The cognitive radio nodes execute computations on multiple heterogeneous channels in the absence of licensed users (a.k.a. primary users) of those bands. The termination detection is a fundamental and non‐trivial problem in distributed systems. In this paper, we propose a termination detection protocol for multi‐hop cognitive radio networks where the cognitive radio nodes are allowed to tune to channels that are not currently occupied by primary users and to move to different locations during the protocol execution. The proposed protocol applies a credit‐distribution‐and‐aggregation approach and maintains a new kind of logical structure, called the virtual tree‐like structure. The virtual tree‐like structure helps in decreasing the latency involved in announcing termination. Unlike conventional tree structures, the virtual tree‐like structure does not require a specific node to act as the root node that has to stay involved in the computation until termination announcement; hence, the root node may become idle soon after finishing its computation. Also, the protocol is able to detect the presence of licensed users and announce strong or weak termination, whichever is possible. Copyright © 2014 John Wiley & Sons, Ltd.     

Wireless distributed computing in cognitive radio networks

Individual cognitive radio nodes in an ad-hoc cognitive radio network (CRN) have to perform complex data processing operations for several purposes, such as situational awareness and cognitive engine (CE) decision making. In an implementation point of view, each cognitive radio (CR) may not have the computational and power resources to perform these tasks by itself. In this paper, wireless distributed computing (WDC) is presented as a technology that enables multiple resource-constrained nodes to collaborate in computing complex tasks in a distributed manner. This approach has several benefits over the traditional approach of local computing, such as reduced energy and power consumption, reduced burden on the resources of individual nodes, and improved robustness. However, the benefits are negated by the communication overhead involved in WDC. This paper demonstrates the application of WDC to CRNs with the help of an example CE processing task. In addition, the paper analyzes the impact of the wireless environment on WDC scalability in homogeneous and heterogeneous environments. The paper also proposes a workload allocation scheme that utilizes a combination of stochastic optimization and decision-tree search approaches. The results show limitations in the scalability of WDC networks, mainly due to the communication overhead involved in sharing raw data pertaining to delegated computational tasks.     

Local control of cognitive radio networks

In a network deployment, a cognitive radio will have to perform two fundamental tasks. First, each cognitive radio needs to optimize its internal operation, and second, it needs to derive a configuration that will enable and optimize communication with other nodes in the network. This latter requirement, however, relies on knowledge about the other nodes’ current configuration settings, which needs to be incorporated into this decision-making process. Collecting and distributing such global knowledge is, however, a difficult and costly process, which, in the past, has been approached by introducing a centralized control authority, distributed negotiation policies, or a dedicated coordination channel in the network, each resulting in vulnerability and scaling issues. In this paper, we propose an alternative approach to the global configuration of a cognitive radio network that eliminates the need to collect global network state information and, instead, uses local information for its decision making process. This technique is built upon the principles of swarm intelligence, as seen in schools of fish and flocks of birds, and allows for efficient and robust coordination of a cognitive radio network in a variety of tasks. We have implemented a working prototype showing the feasibility of this technique in two simulation environments and in a hardware testbed, and find that a solution based on swarm intelligence is well suited to interoperate in heterogeneous deployment environments with other control algorithms, requires low computational overhead, and scales with the number of nodes and the amount of spectrum, thus making it a versatile control algorithm for many deployment scenarios.