公开(公告)号：US09236955B2

公开(公告)日：2016-01-12

申请号：US14408807

申请日：2013-06-19

Applicant: Drexel University ,  The Trustees of The University of Pennsylvania

Inventor： Rohit Bahl ,  Nikhil Gulati ,  Kapil R. Dandekar ,  Dwight L. Jaggard

IPC: H04L1/02 ,  H04B15/00 ,  H04B7/04 ,  H04B7/06 ,  H04L27/26

CPC classification number: H04B15/00 ,  H04B7/0452 ,  H04B7/0456 ,  H04B7/0617 ,  H04L27/2601

Abstract: By using reconfigurable antenna based pattern diversity, an optimal channel can be realized in order to maximize the distance between two subspaces, thereby increasing sum-rate. The inventors show the benefits of pattern reconfigurability using real-world channels, measured in a MIMO-OFDM interference network. The results are quantified with two different reconfigurable antenna architectures. An additional 47% gain in choral distance and 45% gain in sum capacity were achieved by exploiting pattern diversity with IA. Due to optimal channel selection, the performance of IA can also be improved in a low SNR regime.

Abstract translation: 通过使用基于可重构天线的模式分集，可以实现最佳信道，以便最大化两个子空间之间的距离，从而增加总和速率。 本发明人使用在MIMO-OFDM干扰网络中测量的真实世界信道来显示模式可重构性的优点。 结果用两种不同的可重构天线架构进行量化。 通过利用IA的模​​式多样性，实现了合唱距离增加了47％的增长和总和能力的45％的增长。 由于最佳信道选择，IA的性能也可以在低SNR方案中得到改善。

公开(公告)号：US20150195047A1

公开(公告)日：2015-07-09

申请号：US14408807

申请日：2013-06-19

Applicant: Drexel University

Inventor： Rohit Bahl ,  Nikhil Gulati ,  Kapil R. Dandekar ,  Dwight L. Jaggard

IPC: H04B15/00 ,  H04L27/26 ,  H04B7/04

CPC classification number: H04B15/00 ,  H04B7/0452 ,  H04B7/0456 ,  H04B7/0617 ,  H04L27/2601

Abstract: By using reconfigurable antenna based pattern diversity, an optimal channel can be realized in order to maximize the distance between two subspaces, thereby increasing sum-rate. The inventors show the benefits of pattern reconfigurability using real-world channels, measured in a MIMO-OFDM interference network. The results are quantified with two different reconfigurable antenna architectures. An additional 47% gain in choral distance and 45% gain in sum capacity were achieved by exploiting pattern diversity with IA. Due to optimal channel selection, the performance of IA can also be improved in a low SNR regime.

Abstract translation: 通过使用基于可重构天线的模式分集，可以实现最佳信道，以便最大化两个子空间之间的距离，从而增加总和速率。 本发明人使用在MIMO-OFDM干扰网络中测量的真实世界信道来显示模式可重构性的优点。 结果用两种不同的可重构天线架构进行量化。 通过利用IA的模​​式多样性，实现了合唱距离增加了47％的增长和总和能力的45％的增长。 由于最佳信道选择，IA的性能也可以在低SNR方案中得到改善。

公开(公告)号：US20150140938A1

公开(公告)日：2015-05-21

申请号：US14565665

申请日：2014-12-10

Applicant: Drexel University

Inventor： Nikhil Gulati ,  David Gonzalez ,  Kapil Dandekar

IPC: H04W72/08 ,  H04W24/02 ,  H01Q13/20

CPC classification number: H04W72/082 ,  H01Q1/007 ,  H01Q13/20 ,  H04B1/44 ,  H04B7/0417 ,  H04B7/0626 ,  H04W24/02 ,  H04W72/085

Abstract: A method for selecting the state of a reconfigurable antenna installed at either the receiver or transmitter of a communication system is provided. The proposed method uses online learning algorithm based on the theory of multi-armed bandit to perform antenna state selection. The selection technique utilizes the Post-Processing Signal-to-Noise Ratio (PPSNR) as a reward metric and maximizes the long-term average reward over time. The performance of the learning based selection technique is empirically evaluated using wireless channel data. The data is collected in an indoor environment using a 2×2 MIMO OFDM system employing highly directional metamaterial Reconfigurable Leaky Wave Antennas. The learning based selection technique shows performance improvements in terms of average PPSNR and regret over conventional heuristic policies.

Abstract translation: 提供了一种用于选择安装在通信系统的接收机或发射机处的可重新配置天线的状态的方法。 提出的方法使用基于多武装强盗理论的在线学习算法进行天线状态选择。 选择技术利用后处理信噪比（PPSNR）作为奖励度量，并使长期平均奖励随时间推移最大化。 使用无线信道数据对基于学习的选择技术的性能进行经验性评估。 使用采用高度定向的超材料可重构泄漏波天线的2×2 MIMO OFDM系统在室内环境中收集数据。 基于学习的选择技术显示了平均PPSNR的性能改进，并对传统启发式策略感到遗憾。

公开(公告)号：US09924522B2

公开(公告)日：2018-03-20

申请号：US14867801

申请日：2015-09-28

Applicant: Drexel University

Inventor： Nikhil Gulati ,  David Gonzalez ,  Kapil R. Dandekar

IPC: H04B1/44 ,  H04W72/08 ,  H01Q1/00 ,  H01Q13/20 ,  H04W24/02 ,  H04B7/0417 ,  H04B7/06

CPC classification number: H04W72/082 ,  H01Q1/007 ,  H01Q13/20 ,  H04B1/44 ,  H04B7/0417 ,  H04B7/0626 ,  H04W24/02 ,  H04W72/085

Abstract: A method for selecting the state of a reconfigurable antenna installed at either the receiver or transmitter of a communication system is provided. The proposed method uses online learning algorithm based on the theory of multi-armed bandit to perform antenna state selection. The selection technique utilizes the Post-Processing Signal-to-Noise Ratio (PPSNR) as a reward metric and maximizes the long-term average reward over time. The performance of the learning based selection technique is empirically evaluated using wireless channel data. The data is collected in an indoor environment using a 2×2 MIMO OFDM system employing highly directional metamaterial Reconfigurable Leaky Wave Antennas. The learning based selection technique shows performance improvements in terms of average PPSNR and regret over conventional heuristic policies.

公开(公告)号：US20160021671A1

公开(公告)日：2016-01-21

申请号：US14867801

申请日：2015-09-28

Applicant: Drexel University

Inventor： Nikhil Gulati ,  David Gonzalez ,  Kapil R. Dandekar

IPC: H04W72/08 ,  H04B7/06 ,  H04B7/04

CPC classification number: H04W72/082 ,  H01Q1/007 ,  H01Q13/20 ,  H04B1/44 ,  H04B7/0417 ,  H04B7/0626 ,  H04W24/02 ,  H04W72/085

Abstract: A method for selecting the state of a reconfigurable antenna installed at either the receiver or transmitter of a communication system is provided. The proposed method uses online learning algorithm based on the theory of multi-armed bandit to perform antenna state selection. The selection technique utilizes the Post-Processing Signal-to-Noise Ratio (PPSNR) as a reward metric and maximizes the long-term average reward over time. The performance of the learning based selection technique is empirically evaluated using wireless channel data. The data is collected in an indoor environment using a 2×2 MIMO OFDM system employing highly directional metamaterial Reconfigurable Leaky Wave Antennas. The learning based selection technique shows performance improvements in terms of average PPSNR and regret over conventional heuristic policies.

Abstract translation: 提供了一种用于选择安装在通信系统的接收机或发射机处的可重新配置天线的状态的方法。 提出的方法使用基于多武装强盗理论的在线学习算法进行天线状态选择。 选择技术利用后处理信噪比（PPSNR）作为奖励度量，并使长期平均奖励随时间推移最大化。 使用无线信道数据对基于学习的选择技术的性能进行经验性评估。 使用采用高度定向的超材料可重构泄漏波天线的2×2 MIMO OFDM系统在室内环境中收集数据。 基于学习的选择技术显示了平均PPSNR的性能改进，并对传统启发式策略感到遗憾。