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

无线流量的异构性越来越强，增长主要来自无人值守设备。虽然无线通信系统的早期实现侧重于语音电话，但随后的几代蜂窝基础设施使用户能够更广泛地连接到互联网，以支持诸如游戏、浏览和视频观看等应用。展望未来，无人值守设备预计将快速增长，并产生相当大一部分无线数据流量。这一演变对当前的基础设施来说是一个巨大的挑战，因为这些设备与互联网的交互方式与人类完全不同。个人倾向于通过他们的手机或电脑建立持续的连接，而机器经常零星地使用非常短的有效负载来传输状态更新或控制决策。如果不从根本上重新设计介质访问控制层，无线基础设施将无法有效地承载机器类型的流量，从而造成增长和创新的瓶颈。这项研究工作的主要目标是为机器类型的数据设计实用的随机存取方案，着眼于解决与明天的数字通信有关的上述问题。该项目的成果预计将有助于(I)加强数字基础设施，目前已被一致认为是经济的关键驱动力；(Ii)培养具有适应社会需求的技能的有能力的工程师；(Iii)通过招聘和指导，扩大对科学、技术、工程和数学的参与。将利用多路访问通信、压缩传感和稀疏图推理之间的紧密联系。与最先进的技术相比，关键的挑战和主要的创新来自于所考虑的工程问题的极大维度。用于在这种规模上执行的设想的结构和算法植根于随机入库和分割数据的分而治之的方法。从基于图的代码到现代迭代方法和干扰管理的技术，预计将在推动大规模无源随机访问和推理的边界方面发挥重要作用。无线通信中复杂性约束算法的基本限制将通过利用来自有限块长度信息理论、统计物理和应用概率的最新发展工具来表征。建议的模型的关键属性包括不协调的访问以及在不明确获取设备身份的情况下进行操作的能力。这种与现有方案的背离对于消除对个性化反馈的依赖至关重要，个性化反馈在过去实现了快速连接，但现在作为机器类型流量的一种机制，成本将变得过高。该项目的可能结果包括用于下一代随机接入无线系统的近最佳、低复杂性的方案，这些方案将广泛适用于处理超大维度的推断。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.