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CAREER: Heterogeneous Non-orthogonal Wireless Communications Enhanced by Unified SDR - Deep Learning Architectures

职业：统一 SDR 增强的异构非正交无线通信 - 深度学习架构

基本信息

批准号：
1942806
负责人：
Hun-Seok Kim
金额：
$ 50万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-02-01 至 2025-01-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942806&HistoricalAwards=false
关键词：
CAREER Heterogeneous Non orthogonal Wireless

项目摘要

Enabling energy-efficient, ubiquitous connectivity is a critical task for the highly anticipated concept of pervasive Internet-of-Things, where massively deployed heterogeneous Internet-of-Things devices are seamlessly interconnected without the need of manual network management. The outcomes of this research can expedite the vision of trillions of Internet-of-Things devices and enable heterogeneous classes of new applications such as energy-harvesting and wirelessly-connected smart-dust devices, autonomous aerial and ground vehicles with ultra-reliable low latency communications, and intelligent automated factories with deep learning-assisted collaborative networks in highly congested channels. The proposed research targets orders-of-magnitude reduction in power consumption and complexity for wireless connectivity to realize ultra-low cost, ultra-small, disposable, and ubiquitous wireless Internet-of-Things devices. Leading into the realistic world of energy-autonomous Internet-of-Things platforms, this program investigates new ultra-low power wireless connectivity solutions assisted by novel digital signal processor architectures optimized for software-defined radio processing and machine learning. The proposed interdisciplinary research spans a wide range of topics including digital communication, low power integrated circuits, machine learning, and processor architectures to explore cross-layer approaches that are indispensable to tackle challenges in heterogenous classes of energy-efficient and versatile communication systems.Wireless communication is often the dominant source of latency and power consumption for the majority of mission-critical and energy-constrained Internet-of-Things applications. One main objective of the proposed research is to deliver a truly energy-autonomous, fully self-contained wireless communication system that optimally utilizes the scarce harvested energy and dramatically enhances ultra-low power analog circuits performance via novel digital signal processing. A new non-orthogonal modulation and multiple access scheme that exploits sparsity of novel hyper-dimensional modulation is proposed to efficiently eliminate significant power and complexity overhead imposed on orthogonal modulation and multiple access schemes that require explicit synchronization. The research scope includes 1) new non-orthogonal modulation and multiple access schemes for energy-autonomous, disposable IoT sensor nodes without a power-hungry phase-lock-loop circuit and its frequency reference crystal, 2) novel hyper-dimensional modulation to replace or complement conventional orthogonal modulation and error correction codes for ultra-reliable low latency communication, and 3) unified software-defined radio and deep learning processor architectures to enable dynamic communication-computation tradeoffs, energy-efficient execution of baseband modem processing, systolic array based belief propagation algorithm acceleration, and deep neural network assisted signal processing. This program includes experimental researches to fabricate transceiver integrated circuits and design real-time prototype systems to demonstrate the proposed concept of non-orthogonal modulation and multiple access wireless communication throughout extensive field trials rendering realistic scenarios.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.

实现节能、无处不在的连接是备受期待的普适物联网概念的关键任务，在普适物联网中，大规模部署的异构物联网设备无缝互连，而无需手动网络管理。这项研究的成果可以加速数万亿物联网设备的愿景，并实现异构类别的新应用，例如能量收集和无线连接的智能灰尘设备，具有超可靠低延迟通信的自主空中和地面车辆，以及在高度拥挤的通道中具有深度学习辅助协作网络的智能自动化工厂。拟议的研究目标是将无线连接的功耗和复杂性降低几个数量级，以实现超低成本、超小型、一次性和无处不在的无线物联网设备。该计划旨在进入能源自主物联网平台的现实世界，研究新的超低功耗无线连接解决方案，该解决方案由针对软件定义无线电处理和机器学习优化的新型数字信号处理器架构辅助。拟议的跨学科研究涵盖广泛的主题，包括数字通信，低功耗集成电路，机器学习，和处理器架构，以探索跨层方法，这些方法对于解决异构类节能和通用通信系统中的挑战是不可或缺的。无线通信通常是大多数关键任务和节能通信系统的延迟和功耗的主要来源。受限的物联网应用。提出的研究的一个主要目标是提供一个真正的能量自主，完全独立的无线通信系统，最佳地利用稀缺的收获的能量，并通过新的数字信号处理大大提高超低功耗模拟电路的性能。提出了一种新的非正交调制和多址接入方案，该方案利用了新颖的高维调制的稀疏性，以有效地消除施加在需要显式同步的正交调制和多址接入方案上的显著功率和复杂度开销。研究范围包括：1）用于能量自主的一次性物联网传感器节点的新型非正交调制和多址接入方案，无需功耗高的锁相环电路及其频率参考晶体，2）新型超维调制，以取代或补充传统的正交调制和纠错码，实现超可靠的低延迟通信，和3）统一的软件定义无线电和深度学习处理器架构，以实现动态通信计算权衡、基带调制解调器处理的节能执行、基于脉动阵列的置信传播算法加速以及深度神经网络辅助信号处理。该计划包括制造收发器集成电路和设计实时原型系统的实验研究，以通过广泛的现场试验来展示非正交调制和多址无线通信的概念，从而呈现真实的场景。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。