会员体验
专利管家(专利管理)
工作空间(专利管理)
风险监控(情报监控)
数据分析(专利分析)
侵权分析(诉讼无效)
联系我们
交流群
官方交流:
QQ群: 891211   
微信请扫码    >>>
现在联系顾问~
热词
    • 2. 发明授权
    • Modified overhead adjustment function
    • 修改开销调整功能
    • US08385940B2
    • 2013-02-26
    • US13033178
    • 2011-02-23
    • Gregory P. PolliniVikram KaulStephanie Demers
    • Gregory P. PolliniVikram KaulStephanie Demers
    • H04W72/00H04W40/00H04W36/00H04W4/00H04B1/38H04B7/00H04B7/185
    • H04W16/06H04W52/343
    • A MOAF algorithm is used to resize cells for balancing capacity. The MOAF algorithm bases its decision on a cell and all other cells which the algorithm decides are sufficiently close in a propagation sense to affect the results. The MOAF algorithm also automatically determines those cells in an area which are most heavily loaded and those cells which are lightly loaded. The MOAF algorithm will only decrease the size of a cell if it determines specific adjacent cells that are willing and able to accept the load, and the MOAF algorithm will increase the size of a cell only if there is a nearby heavily loaded cell that requires the removal of load. Moreover, the MOAF algorithm can be tuned (via the threshold parameter T) to shift the focus of the optimization from avoidance of coverage holes to the avoidance of creation of excessive handover legs. Because the changes are electrical rather than physical they can be deployed instantaneously via the network management system thus saving the cost of sending a field crew to the site. Moreover, it is possible to make these changes much more frequently than has been possible in the past thereby permitting the optimization of the network to be done in near real time to meet the temporal changes in the geographic distribution of customers.
    • MOAF算法用于调整单元格的大小以平衡容量。 MOAF算法将其决定放在一个单元上,并且算法决定的所有其他单元在传播意义上足够接近以影响结果。 MOAF算法还自动确定那些最重负载的区域和那些轻载入的单元。 如果MOAF算法确定了愿意并能够接受负载的特定相邻单元,则仅减小单元的大小,并且只有当存在附近需要重载的单元时,MOAF算法将增加单元的大小 去除负载。 此外,可以调整MOAF算法(通过阈值参数T)将优化的焦点从避免覆盖孔转移到避免产生过多的切换腿。 因为这些变化是电而不是物理的,所以它们可以通过网络管理系统瞬时部署,从而节省了将现场工作人员送到现场的成本。 此外,可以使这些变化比过去更频繁地进行,从而允许网络的优化在近实时地完成以满足客户的地理分布的时间变化。
    • 3. 发明申请
    • Method and Procedures for Automatic Calibration of a Wireless Communications System Simulation
    • 无线通信系统仿真自动校准的方法和程序
    • US20090029651A1
    • 2009-01-29
    • US12147072
    • 2008-06-26
    • Gregory P. PoliniStephanie DemersVikram Kaul
    • Gregory P. PoliniStephanie DemersVikram Kaul
    • H04B7/00
    • H04W16/22H04B17/21H04B17/309H04B17/3912H04W24/06
    • Performance optimization of mobile wireless communication networks is complex and typically requires extensive offline modeling and simulation prior to deploying changes that may have unforeseen adverse effects on the live customer network. It is necessary to calibrate the simulation model against the actual network at a level of fidelity such that the engineer is confident that the simulation's response to network changes accurately reflects the results that would be experienced if those changes were deployed in an actual live network. This process is typically quite time consuming and requires significant case-by-case insight into the workings of the actual network as well as the simulation model. We have invented a method of automatic calibration in which the simulation adapts itself to more closely resemble the actual network. For a given network architecture and a probabilistic customer usage profile a simulation provides an estimate of key performance metrics. These simulated metrics are compared against actual measurements from the network. To the extent that they do not match within a prescribed tolerance, an iterative adaptive calibration procedure is used to perturb slightly the probabilistic model of network usage.
    • 移动无线通信网络的性能优化是复杂的,并且在部署可能对实时客户网络具有不可预见的不利影响的更改之前通常需要大量的离线建模和仿真。 有必要以真实的级别对实际网络进行仿真模型的校准,这样工程师就可以肯定模拟对网络变化的响应准确地反映了在实际网络中部署了这些变化时会遇到的结果。 这个过程通常是相当耗时的,并且需要对实际网络的运行以及仿真模型进行明显的逐个洞察。 我们已经发明了一种自动校准的方法,其中仿真使其自身更接近实际网络。 对于给定的网络架构和概率客户使用情况,模拟提供关键性能指标的估计。 将这些模拟指标与来自网络的实际测量进行比较。 在规定的公差范围内它们不匹配的情况下,迭代自适应校准过程被用来轻微扰乱网络使用的概率模型。
    • 4. 发明授权
    • Distributed method for minimum delay multi-hop data delivery in vehicular networks
    • 车载网络中最小延迟多跳数据传输的分布式方法
    • US08761175B2
    • 2014-06-24
    • US12408221
    • 2009-03-20
    • Ratul K. GuhaWai ChenStephanie DemersJasmine Chennikara-Varghese
    • Ratul K. GuhaWai ChenStephanie DemersJasmine Chennikara-Varghese
    • H04L12/28H04J3/16G06F15/173
    • G08G1/161H04L2012/40273H04W40/20
    • An inventive method for data delivery in a multi-hop vehicular network with multiple vehicles and intersections is presented. The method comprises, at each source vehicle, initiating packet flow, labeling packets with destination coordinates and a current location, and forwarding the packet flow, and at each intersection, selecting a header vehicle, computing a backlog indicator and listening for broadcasts with a matrix and delay information, updating the matrix in accordance with the backlog indicator if the matrix is present, otherwise initializing the matrix, forwarding the packet flow, and broadcasting the matrix from the header vehicle. In one embodiment, selection of the header vehicle is performed based on random countdown and vehicle ID. The method converges to the optimal (lowest latency) state irrespective of the initial starting point of the network and continues to tend towards the optimal state even as the network conditions alter.
    • 提出了一种在多车辆和交叉路口的多跳车辆网络中进行数据传输的创新方法。 该方法包括:在每个源车辆处,发起分组流,标记具有目的地坐标和当前位置的分组,以及转发分组流,并且在每个交点处,选择头部车辆,计算积压指示符并用矩阵收听广播 和延迟信息,如果矩阵存在,则根据积压指示符更新矩阵,否则初始化矩阵,转发分组流,以及从头部车辆广播矩阵。 在一个实施例中,基于随机倒计时和车辆ID执行头部车辆的选择。 该方法收敛到最优(最低等待时间)状态,而与网络的起始起始点无关,并且即使网络条件发生变化,也继续趋向于最佳状态。
    • 7. 发明申请
    • Modified overhead adjustment function
    • US20060234714A1
    • 2006-10-19
    • US11342100
    • 2006-01-27
    • Gregory PolliniVikram KaulStephanie Demers
    • Gregory PolliniVikram KaulStephanie Demers
    • H04Q7/20
    • H04W16/06H04W52/343
    • A MOAF algorithm is used to resize cells for balancing capacity. The MOAF algorithm bases its decision on a cell and all other cells which the algorithm decides are sufficiently close in a propagation sense to affect the results. The MOAF algorithm also automatically determines those cells in an area which are most heavily loaded and those cells which are lightly loaded. The MOAF algorithm will only decrease the size of a cell if it determines specific adjacent cells that are willing and able to accept the load, and the MOAF algorithm will increase the size of a cell only if there is a nearby heavily loaded cell that requires the removal of load. Moreover, the MOAF algorithm can be tuned (via the threshold parameter T) to shift the focus of the optimization from avoidance of coverage holes to the avoidance of creation of excessive handover legs. Because the changes are electrical rather than physical they can be deployed instantaneously via the network management system thus saving the cost of sending a field crew to the site. Moreover, it is possible to make these changes much more frequently than has been possible in the past thereby permitting the optimization of the network to be done in near real time to meet the temporal changes in the geographic distribution of customers.
    • 10. 发明授权
    • Modified overhead adjustment function
    • 修改开销调整功能
    • US07920867B2
    • 2011-04-05
    • US11342100
    • 2006-01-27
    • Gregory P. PolliniVikram KaulStephanie Demers
    • Gregory P. PolliniVikram KaulStephanie Demers
    • H04W36/00H04W72/00H04W40/00H04W4/00
    • H04W16/06H04W52/343
    • A MOAF algorithm is used to resize cells for balancing capacity. The MOAF algorithm bases its decision on a cell and all other cells which the algorithm decides are sufficiently close in a propagation sense to affect the results. The MOAF algorithm also automatically determines those cells in an area which are most heavily loaded and those cells which are lightly loaded. The MOAF algorithm will only decrease the size of a cell if it determines specific adjacent cells that are willing and able to accept the load, and the MOAF algorithm will increase the size of a cell only if there is a nearby heavily loaded cell that requires the removal of load. Moreover, the MOAF algorithm can be tuned (via the threshold parameter T) to shift the focus of the optimization from avoidance of coverage holes to the avoidance of creation of excessive handover legs. Because the changes are electrical rather than physical they can be deployed instantaneously via the network management system thus saving the cost of sending a field crew to the site. Moreover, it is possible to make these changes much more frequently than has been possible in the past thereby permitting the optimization of the network to be done in near real time to meet the temporal changes in the geographic distribution of customers.
    • MOAF算法用于调整单元格的大小以平衡容量。 MOAF算法将其决定放在一个单元上,并且算法决定的所有其他单元在传播意义上足够接近以影响结果。 MOAF算法还自动确定那些最重负载的区域和那些轻载入的单元。 如果MOAF算法确定了愿意并能够接受负载的特定相邻单元,则仅减小单元的大小,并且只有当存在附近需要重载的单元时,MOAF算法将增加单元的大小 去除负载。 此外,可以调整MOAF算法(通过阈值参数T)将优化的焦点从避免覆盖孔转移到避免产生过多的切换腿。 因为这些变化是电而不是物理的,所以它们可以通过网络管理系统瞬时部署,从而节省了将现场工作人员送到现场的成本。 此外,可以使这些变化比过去更频繁地进行,从而允许网络的优化在近实时地完成以满足客户的地理分布的时间变化。