Collaborative Research: CIF: Small: Low-Complexity Algorithms for Unsourced Multiple Access and Compressed Sensing in Large Dimensions

合作研究：CIF：小型：大维度无源多址和压缩感知的低复杂度算法

• 批准号: 2131115
• 负责人: Yury Polyanskiy
• 金额: $ 17.5万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2024-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2131115&HistoricalAwards=false
• 关键词: Collaborative; Research; CIF; Small; Low

Wireless traffic is increasingly heterogeneous, with growth coming primarily from unattended devices. While early implementations of wireless communication systems have focused on voice telephony, subsequent generations of cellular infrastructures have enabled users to connect more broadly with the Internet, in support of applications such as gaming, browsing, and video watching. Looking into the future, unattended devices are predicted to grow rapidly and to generate a significant portion of the wireless data traffic. This evolution represents a formidable challenge for current infrastructures because such devices interact with the Internet in fundamentally different ways than humans. Individuals tend to establish sustained connections through their phones or computers, whereas machines often sporadically transmit status updates or control decisions with very short payloads. Without a fundamental redesign of the medium access control layer, wireless infrastructures will be unable to efficiently carry machine-type traffic, thereby creating a bottleneck for growth and innovation. The main goal of this research effort is to devise pragmatic random access schemes for machine-type data, with an eye towards addressing the aforementioned issues associated with the digital traffic of tomorrow. Findings from this project are expected to (i) help strengthen digital infrastructures, by now unanimously recognized as a key driver of the economy; (ii) train competent engineers with skills attuned to societal needs; and (iii) broaden participation in science, technology, engineering, and mathematics through recruiting and mentoring. Close connections will be exploited between multiple-access communication, compressed sensing, and sparse graph inference. The crucial challenges and main innovations arise from the exceedingly large dimensionality of the engineering problems considered, compared to the state-of-the-art. The envisioned structures and algorithms for performing at such scales are rooted in the divide-and-conquer approaches of stochastic binning and splitting data. Techniques from graph-based codes to modern iterative methods and interference management are expected to play important roles in pushing the boundaries of unsourced random access and inference in large dimensions. The fundamental limits of complexity-constrained algorithms in wireless communications will be characterized by leveraging recently developed tools from finite-block-length information theory, statistical physics, and applied probability. Key attributes of the proposed models include uncoordinated access and the ability to operate without explicitly acquiring device identities. This departure from established schemes is crucial for eliminating a reliance on individualized feedback, which has enabled fast connections in the past but would now become cost-prohibitive as a mechanism for machine-type traffic. Likely outcomes for this project include near-optimum, low-complexity schemes for the next-generation of random access wireless systems, which will be broadly applicable to deal with inference in exceedingly large dimensions.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.

无线流量越来越异质，主要来自无人看管的设备。尽管无线通信系统的早期实现集中在语音电话上，但随后的几代蜂窝基础架构使用户能够更广泛地与Internet建立联系，以支持游戏，浏览和视频观看等应用程序。展望未来，预计无人看管的设备将迅速增长，并产生大部分无线数据流量。这种进化代表了当前基础设施的巨大挑战，因为这种设备的方式与人类根本不同。个人倾向于通过手机或计算机建立持续的连接，而机器通常会以非常短的有效载荷来零星传输状态更新或控制决策。如果没有中等访问控制层的基本重新设计，无线基础架构将无法有效地携带机器型流量，从而为增长和创新创造瓶颈。这项研究工作的主要目的是为机器类型数据设计务实的随机访问方案，并着眼于解决与明天数字流量相关的上述问题。预计该项目的发现将（i）有助于加强数字基础设施，到目前为止，一致被认为是经济的主要驱动力； （ii）培训有能力满足社会需求的技能的有能力的工程师； （iii）通过招聘和指导扩大参与科学，技术，工程和数学。在多访问通信，压缩传感和稀疏图推理之间将利用关闭的连接。与最先进的工程问题相比，所考虑的工程问题的极大维度引起了至关重要的挑战和主要创新。在此类尺度上执行的设想结构和算法植根于随机嵌入和拆分数据的分裂和争议方法。从基于图的代码到现代迭代方法的技术和干扰管理将在突破不包含的随机访问和推理的界限中起重要作用。无线通信中复杂性约束算法的基本限制将以利用有限块长度信息理论，统计物理学和应用概率的最近开发的工具的特征。所提出的模型的关键属性包括不协调的访问和在不明确获取设备身份的情况下操作的能力。与已建立的计划的偏离对于消除对个性化反馈的依赖至关重要，这在过去已经实现了快速连接，但现在将成为机器型流量的机制。该项目的可能结果包括接近最省的低复杂性方案，用于下一代随机访问无线系统，这将非常适用于在极大的方面进行推断。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子优点和广泛的影响来评估NSF的法定任务，并被认为是值得的支持。

项目成果

Data-Driven Blind Synchronization and Interference Rejection for Digital Communication Signals

• DOI: 10.1109/globecom48099.2022.10001513
• 发表时间: 2022-09
• 期刊: GLOBECOM 2022 - 2022 IEEE Global Communications Conference
• 作者: A. Lancho;A. Weiss;Gary C. F. Lee;Jennifer Tang;Yuheng Bu;Yury Polyanskiy;G. Wornell

On the Advantages of Asynchrony in the Unsourced MAC

论异步在无源MAC中的优点

• DOI: 10.1109/isit54713.2023.10206586
• 发表时间: 2023
• 期刊: IEEE
• 作者: Fengler, Alexander;Lancho, Alejandro;Narayanan, Krishna;Polyanskiy, Yury
• 通讯作者: Polyanskiy, Yury

Capacity of Noisy Permutation Channels

噪声排列通道的容量

• DOI: 10.1109/tit.2023.3247812
• 发表时间: 2023
• 期刊: IEEE Transactions on Information Theory
• 影响因子: 2.5
• 作者: Tang, Jennifer;Polyanskiy, Yury
• 通讯作者: Polyanskiy, Yury

Empirical Bayes via ERM and Rademacher complexities: the Poisson model

• 发表时间: 2023-07
• 期刊: Frontiers in Veterinary Science
• 影响因子: 3.2
• 作者: Soham Jana;Yury Polyanskiy;Anzo Teh;Yihong Wu

Exploiting Temporal Structures of Cyclostationary Signals for Data-Driven Single-Channel Source Separation

• DOI: 10.1109/mlsp55214.2022.9943311
• 发表时间: 2022-08
• 期刊: 2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing (MLSP)
• 作者: Gary C. F. Lee;A. Weiss;A. Lancho;Jennifer Tang;Yuheng Bu;Yury Polyanskiy;G. Wornell