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SpecEES: Collaborative Research: CoSeC-RAN: Cognitive Secure Cloud RAN for Efficient Spectrum Sharing

SpecEES：协作研究：CoSeC-RAN：用于高效频谱共享的认知安全云 RAN

基本信息

批准号：
1731698
负责人：
Eylem Ekici
金额：
$ 16.46万
依托单位：
Ohio State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-10-01 至 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1731698&HistoricalAwards=false
关键词：
SpecEES Collaborative Research CoSeC RAN

项目摘要

Next generation wireless networks will be characterized by larger volume, faster information transfer, and diversity. Wireless industry has been altering conventional license-based spectrum access policies through approaches utilizing unlicensed spectrum. This leads to dynamic spectrum access (DSA), where unlicensed use of a spectrum should avoid harm to licensed users, or should ensure a fair share of spectrum with other unlicensed users. DSA places an additional burden on business operations because revenue needs to be generated over dynamically changing resources, while providing expected quality of service to potential users. Yet, existing high-level approaches for spectrum sharing are not well poised to solve the core problem. Instead, finer-scale spectrum sharing in time, space, and spectrum dimensions is required. Furthermore, at such finer scales, it is essential to consider human behaviors and integrate economics into the tool-set of spectrum sharing. Finally, recent cyber-security concerns necessitate that such a system should incorporate security and privacy in its core. This project designs and develops advanced spectrum sharing techniques at the nexus of spectrum, pricing, and privacy, for next-generation DSA solutions. The project is conducted by an interdisciplinary team of experts in security, operations management, decision science, wireless communication, networking, and optimization. The fundamental results emerging from this research can enable transformative cognitive radio network management and operation solutions. The project supports multiple graduate students. Insights from the proposed collaborative research project between Ohio State and University of Nebraska Lincoln will educate both industry and academia across rural Nebraska and urban Ohio regions.The project explores four main goals: (1) Secure spectrum resource metering solutions with a Cognitive Secure Cloud Radio Access Networks (CoSeC-RAN) architecture. Preserving the digital base-band data within the network leads to more accurate resource assignment and pricing decisions. (2) Advanced security and privacy-preserving solutions. The novel CoSeC-RAN architecture enables new directions to provide security to and by the network while preserving user privacy. (3) Dynamic pricing algorithms, where prices are updated in real-time based on available capacity and customer load. These pricing algorithms reflect non-stationary and stochastic nature of both available capacity and demand. Customer- specific bandwidth requirements and mechanisms for ensuring customer privacy are incorporated into algorithm design. (4) Spectrum sensing methods with incomplete information. CoSeC-RAN estimates primary signals at secondary user locations, with incomplete information from the network, to ensure minimum interference. Developed solutions are rigorously tested through large-scale simulations and experimentally in a city-wide testbed.

下一代无线网络将以更大的容量、更快的信息传输和多样性为特征。无线产业已经通过利用未授权频谱的方法来改变传统的基于许可的频谱接入策略。这导致动态频谱接入（DSA），其中频谱的未授权使用应当避免对授权用户的伤害，或者应当确保与其他未授权用户公平地共享频谱。每日生活津贴给业务运营增加了额外负担，因为需要通过动态变化的资源产生收入，同时向潜在用户提供预期的服务质量。然而，现有的频谱共享高级方法并不能很好地解决核心问题。相反，需要在时间、空间和频谱维度上进行更精细尺度的频谱共享。此外，在如此精细的尺度上，考虑人类行为并将经济学整合到频谱共享的工具集中是至关重要的。最后，最近的网络安全问题要求这样一个系统必须将安全和隐私纳入其核心。该项目为下一代DSA解决方案设计和开发了频谱、定价和隐私的高级频谱共享技术。该项目由安全、运营管理、决策科学、无线通信、网络和优化方面的跨学科专家团队进行。这项研究的基本成果可以实现变革性的认知无线电网络管理和运营解决方案。该项目支持多名研究生。俄亥俄州和内布拉斯加大学林肯分校之间的拟议合作研究项目的见解将教育内布拉斯加州农村和俄亥俄州城市地区的工业和学术界。该项目探讨了四个主要目标：（1）使用认知安全云无线电接入网络（CoSeC-RAN）架构的安全频谱资源计量解决方案。将数字基带数据保存在网络内，可以实现更准确的资源分配和定价决策。(2)先进的安全和隐私保护解决方案。新的CoSeC-RAN架构为网络提供了新的方向，同时保护用户隐私。(3)动态定价算法，根据可用容量和客户负载实时更新价格。这些定价算法反映了可用容量和需求的非平稳性和随机性。客户特定的带宽要求和确保客户隐私的机制被纳入算法设计中。(4)不完全信息下的频谱感知方法。CoSeC-RAN利用来自网络的不完整信息估计次要用户位置处的主要信号，以确保最小干扰。开发的解决方案通过大规模模拟和在全市范围的测试平台上进行严格测试。