NeTS: Small: Massive Wireless Random Access: Principles and Protocols

NetS：小型：大规模无线随机接入：原理和协议

• 批准号: 1817205
• 负责人: Ayfer Ozgur
• 金额: $ 50万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1817205&HistoricalAwards=false
• 关键词: NeTS; Small; Massive; Wireless; Random

The next generation of wireless systems are widely expected to connect a massive number of low-energy sensors and actuators that form the fabric of smart technologies and cyberphysical systems. The resultant 'Internet of Things' is expected to be one of the major drivers of technological growth over the next decade. However, its design presents new challenges to wireless engineers. One of the key challenges is how to enable a massive number of low-energy wireless devices to sporadically access the scarce spectrum with minimal coordination and channel estimation overheads. While there are several existing solutions for random access, they are not well-suited for this emerging paradigm due to high energy cost for retransmissions and carrier sensing, near-far effects, or significant control layer overheads.This project aims to fundamentally rethink wireless random access for IoT networks by identifying the sporadic access of a massive number of severely energy limited devices as its key distinguishing characteristic. It develops a mathematical framework to study this problem and shows how this framework relates to a seemingly unrelated problem in theoretical computer science called group testing. By building on this connection it aims to develop optimal mathematical solutions that can be translated to innovative random-access protocols which (a) jointly optimize control and information layer tasks, such as device discovery and data transmission; (b) embrace sensor collisions and thus, eliminate the complexity and energy cost of collision resolution and retransmissions; (c) are designed to satisfy stringent energy constraints and match the energy dynamics at the transmitters; (d) employ simple modulation and detection techniques that do not require channel estimation; and (e) are low-complexity (encoding/decoding), flexible (can be applied in heterogeneous settings), and resilient to noise and device imperfections. The multi-disciplinary nature of the formulated problems will enable the cross-fertilization of ideas from different sub-disciplines of electrical engineering, computer science and applied mathematics. The project is complemented with a strong education and outreach program with an emphasis on broadening participation in electrical engineering.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人们普遍期望下一代无线系统连接大量的低能传感器和执行器，这些传感器和执行器构成了智能技术和网络物理系统的结构。预计由此产生的“物联网”将成为未来十年中技术增长的主要驱动力之一。但是，其设计给无线工程师带来了新的挑战。关键挑战之一是如何启用大量低能无线设备，以最小的协调和渠道估计开销零星地访问稀缺频谱。虽然有几种现有的随机访问解决方案，但由于重新启动和载体感测的高能源成本，接近效果或重大的控制层高架，它们并不适合这种新兴的范式。该项目旨在通过从根本上重新识别IoT网络的无线随机访问，通过确定众多能源限制的功能来区分ioT网络，以识别出众多能源限制的功能。它开发了一个数学框架来研究此问题，并显示了该框架与理论计算机科学中看似无关的问题的关系，称为小组测试。通过基于这种联系，它旨在开发最佳的数学解决方案，这些解决方案可以转化为创新的随机访问协议，该协议（a）共同优化控制和信息层任务，例如设备发现和数据传输； （b）拥抱传感器碰撞，因此消除了碰撞解决和重新回旋的复杂性和能源成本； （c）旨在满足严格的能量限制并与发射器处的能量动力学相匹配； （d）采用不需要通道估计的简单调制和检测技术； （e）是低复杂性（编码/解码），灵活（可以在异质设置中应用），并适合噪声和设备缺陷。配制问题的多学科性质将使电气工程，计算机科学和应用数学的不同子学科的思想交叉利用。该项目与强大的教育和外展计划相辅相成，重点是扩大电气工程的参与。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估标准来评估的。

项目成果

Pointwise Bounds for Distribution Estimation under Communication Constraints

通信约束下分布估计的逐点界限

• 发表时间: 2021
• 期刊: Advances in neural information processing systems
• 作者: Wei-Ning Chen, Peter Kairouz

Adaptive Group Testing on Networks with Community Structure

具有社区结构的网络的自适应分组测试

• DOI: 10.1109/isit45174.2021.9517888
• 发表时间: 2021
• 期刊: 2021 IEEE International Symposium on Information Theory (ISIT
• 作者: Ahn, Surin;Chen, Wei-Nina;Ozgur, Ayfer
• 通讯作者: Ozgur, Ayfer

Sparse Combinatorial Group Testing

稀疏组合组测试

• DOI: 10.1109/tit.2019.2951703
• 发表时间: 2020
• 期刊: IEEE Transactions on Information Theory
• 影响因子: 2.5
• 作者: Inan, Huseyin A.;Kairouz, Peter;Ozgur, Ayfer
• 通讯作者: Ozgur, Ayfer

Over-the-Air Statistical Estimation of Sparse Models

• DOI: 10.1109/globecom46510.2021.9685768
• 发表时间: 2021-12
• 期刊: 2021 IEEE Global Communications Conference (GLOBECOM)
• 作者: Chuan-Zheng Lee;L. P. Barnes;Wenhao Zhan;Ayfer Özgür

Breaking The Dimension Dependence in Sparse Distribution Estimation under Communication Constraints

打破通信约束下稀疏分布估计的维数依赖性

• 发表时间: 2021
• 期刊: Proceedings of Machine Learning Research
• 作者: Wei-Ning Chen, Peter Kairouz