Clustering-based adaptive low-power subframe configuration with load-aware offsetting in dense heterogeneous networks

Heterogeneous Networks (HetNets) and cell densification represent promising solutions for the surging data traffic demand in wireless networks. In dense HetNets, user traffic is steered toward the Low-Power Node (LPN) when possible to enhance the user throughput and system capacity by increasing the area spectral efficiency. However, because of the transmit power differences in different tiers of HetNets and irregular service demand, a load imbalance typically exists among different serving nodes. To offload more traffic to LPNs and coordinate the Inter-Cell Interference (ICI), Third-Generation Partnership Project (3GPP) has facilitated the development of the Cell Range Expansion (CRE), enhanced Inter-Cell Interference Coordination (eICIC) and Further enhanced ICIC (FeICIC). In this paper, we develop a cell clustering-based load-aware offsetting and an adaptive Low-Power Subframe (LPS) approach. Our solution allows the separation of User Association (UA) functions at the User Equipment (UE) and network server such that users can make a simple cell-selection decision similar to that in the maximum Received Signal Strength (max-RSS) based UA scheme, where the network server computes the load-aware offsetting and required LPS periods based on the load conditions of the system. The proposed solution is evaluated using system-level simulations wherein the results correspond to performance changes in different service regions. Results show that our method effectively solves the offloading and interference coordination problems in dense HetNets.     

受电弓离线电弧所引起的电磁干扰测试及分析

根据GB／T-24338-2（轨道交通电磁兼容）标准,提出了离线电弧所产生的电磁干扰的测试方案,并选择了在成灌高铁上实施测试。在车下进行EMI标准测试、外场步进EMI测试,并将测试结果分别与GB／T-24338和国家环保标准GB9175-88进行了对比分析,结果表明5倍冗余度下的动车环境磁场强度在特殊工业环境许可范围内。     

SZ绞成缆设备电控系统的构成及变频器故障分析

SZ绞设备是将单元绞制和缆芯绞制在一道工序完成的一种专业电缆生产设备。SZ绞设备采用了先进的变频调速技术和PLC控制技术,有的还采用了伺服驱动,使其控制快速、精确和可靠。本文就SZ绞设备的电控系统构成及在调试运行中遇到的变频器故障进行了分析。     

MIMO-OFDM系统抗干扰信道估计算法

提出一种抗干扰信道估计(AICE)算法用于解决MIMO-OFDM系统信道估计问题。该算法在接收端通过对收到的累加信号进行分解,得到来自不同发射天线的信号,通过消除符号间干扰与子载波间干扰及采用期望值最大迭代算法进行信道估计来减小估计误差,提高信道估计的准确性。与ICE算法的性能比较证明了AICE算法在瑞利衰落信道下具有更好的信道估计均方误差及误比特率性能。     

MB-OFDM UWB系统中的加窗算法设计

提出一种能抵抗超宽带系统中强窄带干扰的接收端加窗算法,它适用于多带正交频分复用(MB-OFDM)超宽带通信系统中采用的零值填充机制。相对于其他抗窄带干扰算法,接收端加窗算法具有算法复杂度低、无须对窄带干扰进行预估计等优点。在不同强窄带干扰源下的系统无线数据包错误率的仿真测试验证了该算法的有效性和可行性。     

An operational semantics for a calculus for wireless systems

In wireless systems, the communication mechanism combines features of broadcast, synchrony, and asynchrony. We develop an operational semantics for a calculus of wireless systems. We present different Reduction Semantics and a Labelled Transition Semantics and prove correspondence results between them. Finally, we apply CWS to the modelling of the Alternating Bit Protocol, and prove a simple correctness result as an example of the kind of properties that can be formalized in this framework.A major goal of the semantics is to describe the forms of interference among the activities of processes that are peculiar of wireless systems. Such interference occurs when a location is simultaneously reached by two transmissions. The Reduction Semantics differ on how information about the active transmissions is managed.We use the calculus to describe and analyse a few properties of a version of the Alternating Bit Protocol.     

认知网络中基于用户需求的功率控制算法研究*

为了解决认知网络功率控制问题,根据非合作博弈论和干扰温度,设计出一种新型效用函数,该函数综合考虑授权用户和认知用户功率分配的要求,满足用户需求。并通过此效用函数提出一个基于非合作博弈论的新颖功率控制算法,分析了该算法的收敛性、纳什均衡解的存在性和唯一性。仿真结果表明该算法在保证认知用户需求前提下,传输功率更加合理,体现出算法的有效性和公平性。与参考算法相比,节省30.12%的功耗,并且具有较好的抗背景噪声。最后,与KG算法相比,该算法具有较大的网络容量。     

物联网环境下Web数据库异常数据检测方法研究

物联网环境下web数据库网络承载着不同的网络载体和网络信道,web数据库通过云储存的形式来实现资源共享,云储存产生的异常数据会给网络信息web数据库空间带来一种危机感和存储数据容量空间的不足,所以对物联网环境下web数据库异常数据的检测要求更精准。传统的异常数据检测方法采用简化梯度方法进行web数据库异常数据检测,对含有干扰频率成份的web异常数据不能准确的去除,检测性能低。为此,提出一种基于时空关联的分布式的web数据库异常数据检测方法,通过与集中式算法的精度和消耗量进行对比,仿真实验表明,所提方法进行异常数据检测,减少了web数据的能量消耗,信号幅值大于干扰噪声数据幅值,具有较好的抗干扰性能。     

基于网格化频谱监测结果的无线干扰源定位研究

为了快速且高效地找出干扰源或非法电台,提出一种寻找干扰源位置的定位方法。该方法通过在待监测区域设置网格化分布的多个监测点来检测获取接收功率的大小,从而推测出干扰源的位置。通过仿真验证分析,验证了所提算法的正确性和有效性。