In Cognitive Radio(CR) networks,CR user has to detect the spectrum channel periodically to make sure that the channel is idle during data transmission frame in order to avoid the collisions to the primary users.Hence recent research has been focused on the interference avoidance problem.Quality of Service(QoS) requirement of CR user will affect the time of data transmission in each frame.In this paper,in order to solve the interference avoidance and spectrum utilization problems without cooperation among CR users,a new scheme to obtain the optimal duration of data transmission frame is proposed to maximize the spectrum utilization and guarantee the protection to the primary users.The main advantages of our proposed scheme include the followings:(1) QoS requirement of CR user is concerned;(2) p-persistent Media Access Control(MAC) random access is used to avoid the collisions among CR users;(3) CR network system capacity is considered.We develop a Markov chain of the primary spectrum channel states and an exponential distribution of the CR user's traffic model to analyze the performance of our proposed scheme.Computer simulation shows that there is an optimal data transmission time to maximize the spectrum utilization.However,the regulatory constraint of the collision rate to the primary users has to be satisfied at the expense of spectrum utilization.And also the tradeoff between the spectrum utilization and the capacity of the CR system is taken into account.
资源受限的传感器节点密集分布在无线传感器网络监控区域,sink节点通过收集节点间观测信息对监控区域内发生的事件进行感知.本文提出SCMAR(Spatial Correlation-based Mobile Agent Routing)路由算法,在移动代理架构内,利用节点观测数据的空间相关性以能量有效的方式对感知事件进行估计.仿真结果表明SCMAR在各种应用环境下能量有效性均优于MARDF(Mobile Agent Routes for Data Fusion)路由算法.
针对无线传感器网络节点距离测量精度问题,提出了一种基于平滑跳数梯度的间接测距方法DV-SHG(DV-hop with Smoothing Hop Gradient)。DV-SHG应用节点的邻居节点信息对跳数值和平均每跳距离进行修正以提高测距精度。理论分析及仿真结果表明,与DV-GNN(DV-hop with the Number of Gradient Neighbors)算法相比,在相同的计算和通信开销下,DV-SHG算法能获得较高的测距精度,在节点密集分布的无线传感器网络中具有很好的测距效果。
由于移动节点间的相遇机会的不确定性,容迟网络采用机会转发机制完成分组的转发。这一机制要求节点以自愿合作的方式来完成消息转发。然而,在现实中,绝大多数的节点表现出自私行为。针对节点的自私行为,提出了基于信任蚁群的自组织路由算法TrACO(Trust Ant Clone Optimization)。该算法利用蚁群算法基于群空间的搜索能力和快速的自适应学习特性,能够适应容迟网络动态复杂多变的网络环境。最后对TrACO进行性能仿真分析,仿真结果表明TrACO能够在较低的消息冗余度和丢弃数下获得较高的分组转发率和较低的消息传输时延,表现出较强的挫败节点自私行为的能力。
This paper proposes a compressed sensing （CS） scheme to reconstruct and estimate the signals. In this scheme, the framework of CS is used to break the Nyquist sampling limit, making it possible to reconstruct and estimate signals via fewer measurements than that is required traditionally. However, the reconstruction algorithms based on CS are normally non-deterministic polynomial hard （NP-hard） in mathematics, which makes difficulties in obtaining real-time analysis-results. Therefore, a new compressed sensing scheme based on back propagation （BP） neural network is proposed under an assumption that every sub-band is the same. In this new scheme, BP neural network is added into detection process, replacing for signal reconstruction and decision-making. By doing this, heavy calculation cost in reconstruction is moved into pre-training period, which can be done before the real-time analysis, bringing about a sharp reduction in time consuming. For simplify, 1-bit quantification is taken on compressed signals. Simulations demonstrate the performance enhancement in the proposed scheme： compared with normal CS-based scheme, the proposed one presents a much shorter response time as well as a better robustness performance to noise via fewer measurements.
数据挖掘中如何根据数据之间的相似度确定簇(Cluster)数一直是聚类算法中需要解决的难题。文中在经典谱聚(Spectral Clustering)算法的基础上提出了一种基于特征间隙检测簇数的谱聚类算法(Spectral Clustering with Identifying Clustering Number based on Eigengap,SC-ICNE)。通过构建规范的拉普拉斯矩阵,顺序求解其特征值和相应特征向量,并得到矩阵相邻特征值的间隙,通过判断特征间隙的位置来确定簇数k。最后,通过对前k个特征向量的k-means算法实现数据集的聚类。文中通过仿真分析了高斯相似度函数对SC-ICNE聚类性能的影响,在非凸球形数据集和UCI数据集上进行了性能仿真,并和k-means聚类算法进行了对比,在检测簇数和聚类准确性方面,验证了SC-ICNE算法的有效性。
This paper studies an interference coordination method by means of spectrum allocation in Long-Term Evolution (LTE) multi-cell scenario that comprises of macrocells and femtocells. The purpose is to maximize the total throughput of femtocells while ensuring the Signal-to-Interference plus Noise Ratio (SINR) of the edge macro mobile stations (mMSs) and the edge femtocell Mobile Stations (fMSs). A new spectrum allocation algorithm based on graph theory is proposed to reduce the interference. Firstly, the ratio of Resource Blocks (RBs) that mMSs occupy is obtained by genetic algorithm. Then, after considering the impact of the macro Base Stations (mBSs) and small scale fading to the fMS on different RBs, multi-interference graphs are established and the spectrum is allocated dynamically. The simulation results show that the proposed algorithm can meet the Quality of Service (QoS) requirements of the mMSs. It can strike a balance between the edge fMSs' throughput and the whole fMSs' throughput.
针对DV-HOP(distance vector hop)算法的定位精度对节点间跳数信息依赖性较强的特点,提出一种基于接收信号强度指示(received signal strength indicator,RSSI)每跳分级和平均跳距修正的DV-HOP改进算法RADV-HOP(RSSI and average hopping distance modifying DV-HOP)。仿真结果表明:在相同的网络环境里,与传统DV-HOP算法相比,RADV-HOP定位算法仅需节点通信芯片带有RSSI指示功能及增加少量的计算和通信开销,不需要额外的硬件开销,将每跳分为3个子级时,归一化定位误差能下降65%;与其他DV-HOP修正算法相比,RADV-HOP算法以相同的通信开销和稍微增加的计算开销使定位误差下降了45%。
