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。     

Novel spectrum sensing method based on the spatial spectrum for cognitive radio systems

Spectrum sensing is the key problem for Cognitive Radio (CR) systems. A method based on the Peak-to-Average Amplitude-Ratio (PAAR) of the Spatial Spectrum (SS) of the received signals is proposed to sense the available spectrum for the cognitive users with the help of the multiple antennas at the receiver of the cognitive users. The greatest advantage of the new method is that it requires no information of the noise power and is free of the noise power uncertainty. Both the simulation and the analytical results show that the proposed method is robust to noise uncertainty, and greatly outperform the classical Energy Detector (ED) method.     

Graph-theoretic algorithm for spectrum allocation in IEEE 802.22 wireless mesh network

The IEEE 802.22 standard based on wireless Cognitive Radio (CR) is an optimal solution to resolve the inefficient spectrum utility problem. In this paper, we focus on the spectrum allocation in IEEE 802.22 mesh networks and propose a new graph-theory algorithm. The algorithm aims at two objectives: one is the sum of the allocated channel bandwidth is maximum, and the other is the number of users can be active simultaneity is maximum. In this proposed algorithm, the topology of network was modeled as a general graph and could be transformed into a weighted complete bipartite-graph by three processes. The simulations show that the presented algorithm can improve the performance of spectrum allocation.     

Spectrum handoff in cognitive radio with fuzzy logic control

The secondary usage of spectrum has been investigated in Cognitive Radio (CR) network to resolving the spectrum scarcity issue in wireless communication. When Primary Users (PU) who own the spectrum appear, spectrum handoff is needed to maintain the communications of Secondary Users. But the decision making of spectrum handoff is a challenge issue for CR network, because the input of decision making, which obtain through spectrum sensing, is heterogeneous and inexact. In this paper we will use fuzzy logic control theory to solve this issue and make use of new information for handoff operation: the probability of PU’s occupancy at a certain channel. Our new algorithm can make more intelligent decision compared to simple traditional spectrum handoff decision making and reduce the probability of spectrum handoff, also the performance of SU’s communication can be enhanced.     

认知雷达抗干扰中的博弈论分析综述

雷达对抗的核心研究内容主要是干扰策略与抗干扰策略之间的对抗博弈,其作为电子战研究领域的热点一直备受学者们关注.该文综述了学者们利用合作与非合作博弈方法来分析雷达在进行目标探测和干扰抑制时所使用的策略,主要通过不同体制的雷达利用认知技术感知和学习外界复杂的电磁环境,合理地分配发射功率、控制编码序列、设计波形、研究检测和跟踪方法以及分配雷达通信资源等.这样雷达既节约发射所消耗的功率,又可以自适应地搜索和跟踪目标而不被敌方所发现,从而使雷达在复杂多变的现代战场环境中达到自身最优的性能.最后,对认知雷达抗干扰中的博弈论分析研究进行总结和展望,并指出了一些博弈论在认知雷达抗干扰策略应用中所面临的潜在问题和挑战.     

Traffic-Aware Green Cognitive Radio

Cognitive Radio Network (CRN) has emerged as an effective solution to the spectrum under-utilization problem, by providing secondary users (SUs) an opportunistic access to the unoccupied frequency bands of primary users (PUs). Most of the current research on CRN are based on the assumption that the SU always has a large amount of data to transmit. This leads to the objective of SU throughput maximization with a constraint on the allowable interference to the PU. However, in many of the practical scenarios, the data arrival process of the SU closely follows an ON–OFF traffic model, and thus the usual throughput optimization framework may no longer be suitable. In this paper, we propose an intelligent data scheduling strategy which minimizes the average transmission power of the SU while maintaining the transmission delay to be sufficiently small. The data scheduling problem has been formulated as a finite horizon Markov Decision Process (MDP) with an appropriate cost function. Dynamic programming approach has been adopted to arrive at an optimal solution. Our findings show that the average transmitted power for our proposed approach can be as small as 36.5% of the power required for usual throughput maximization technique with insignificant increase in average delay.     

Prediction based channel allocation performance for cognitive radio

The interdependency, in a cognitive radio (CR) network, of spectrum sensing, occupancy modelling, channel switching and secondary user (SU) performance, is investigated. Achievable SU data throughput and primary user (PU) disruption rate have been examined for both theoretical test data as well as data obtained from real-world spectrum measurements done in Pretoria, South Africa. A channel switching simulator was developed to investigate SU performance, where a hidden Markov model (HMM) was employed to model and predict PU behaviour, from which proactive channel allocations could be made. Results show that CR performance may be improved if PU behaviour is accurately modelled, since accurate prediction allows the SU to make proactive channel switching decisions. It is further shown that a trade-off may exist between achievable SU throughput and average PU disruption rate. When using the prediction model, significant performance improvements, particularly under heavy traffic density conditions, of up to double the SU throughput and half the PU disruption rate were observed. Results obtained from a measurement campaign were comparable with those obtained from theoretical occupancy data, with an average similarity score of 95% for prediction accuracy, 90% for SU throughput and 70% for PU disruption rate.     

A dynamic and weighted spectrum decision mechanism based on SNR Tracking in CRAHNs

The spectrum decision concept in Cognitive Radio Ad-Hoc Networks (CRAHNs) introduces important challenges. These include the time-dependent SNR observations of individual CRAHN users due to the fading and shadowing effects in the licensed channels, the necessity of fusion mechanisms for accurate decisions, and the difficulties depending on multi-hop deployment. Considering these challenges, in this paper, we propose a dynamic, cooperative and distributed spectrum decision mechanism in order to decide the channel usage in CRAHNs accurately. The proposed mechanism introduces the SNR Tracking System which considers the time-varying local SNR observations and decisions of the CRAHN users. The proposed mechanism employs a distributed Weighted Fusion Scheme (WFS), to combine the individual decisions and hence, to obtain the cooperative decision. The proposed spectrum decision mechanism adapts itself dynamically to the multi-hop architecture of the network. The performance of the proposed mechanism is compared to some conventional fusion mechanisms based on the AND, OR and MAJORITY rules, and it is shown that the proposed weighted mechanism gives lower false alarm and higher detection probabilities compared to the conventional fusion mechanisms.     

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.