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

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
批准号:
1942806
负责人:
Hun-Seok Kim
金额:
$50.0万
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
项目状态:
未结题
起止时间:
2020-02-01 至 2025-01-31

项目摘要

项目成果

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中文摘要
翻译
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,在普适物联网概念中,大规模部署的异构物联网设备无需人工网络管理即可无缝连接。这项研究的成果可以加速数万亿物联网设备的愿景,并实现异构类新应用,如能量收集和无线连接的智能粉尘设备,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。提出的研究目标是将无线连接的功耗和复杂性降低数量级,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。进入能源自主物联网平台的现实世界,该项目研究新的超低功耗无线连接解决方案,辅以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。提议的跨学科研究涵盖了广泛的主题,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必不可少的。对于大多数关键任务和能源受限的物联网应用来说,无线通信通常是延迟和功耗的主要来源。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,最佳地利用稀缺的能量,并通过新颖的数字信号处理显着提高超低功耗模拟电路的性能。提出了一种利用新型超维调制稀疏性的非正交调制多址方案,有效地消除了需要显式同步的正交调制多址方案所带来的巨大功率和复杂性开销。研究范围包括1)新的非正交调制和多址方案,用于能源自主,一次性物联网传感器节点,不需要耗电的锁相环电路及其频率参考晶体;2)新的超维调制,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;3)统一的软件定义无线电和深度学习处理器架构,实现动态通信计算权衡、基带调制解调器处理的节能执行、基于收缩阵列的信念传播算法加速和深度神经网络辅助信号处理。该计划包括实验研究,制造收发器集成电路和设计实时原型系统,以通过广泛的现场试验呈现现实场景,演示所提出的非正交调制和多址无线通信概念。该奖项反映了美国国家科学基金会的法定使命,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。
英文摘要
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.
期刊论文(5)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1109/tcomm.2023.3274158
发表时间: 2022-06
期刊: IEEE Transactions on Communications
影响因子: 8.3
作者: [Chenghong Bian;Chin-Wei Hsu;Changwoo Lee;Hun-Seok Kim]
通讯作者: Chenghong Bian;Chin-Wei Hsu;Changwoo Lee;Hun-Seok Kim
DOI: 10.48550/arxiv.2302.09174
发表时间: 2023-02
期刊:
影响因子: --
作者: [Changwoo Lee;Xiao Hu;Hun-Seok Kim]
通讯作者: Changwoo Lee;Xiao Hu;Hun-Seok Kim
Deep Learning-Based Joint Channel Coding and Frequency Modulation for Low Power Connectivity
用于低功耗连接的基于深度学习的联合信道编码和频率调制
DOI: 10.1109/icc45041.2023.10278753
发表时间: 2023
期刊: IEEE
影响因子: --
作者: [Chang, Boxuan, Wang, Chenyu, Kim, Hun-Seok]
通讯作者: Kim, Hun-Seok
Instantaneous Feedback-Based Opportunistic Symbol Length Adaptation for Reliable Communication
基于瞬时反馈的机会符号长度自适应以实现可靠通信
DOI: 10.1109/tcomm.2023.3266356
发表时间: 2023
期刊: IEEE Transactions on Communications
影响因子: 8.3
作者: [Hsu, Chin-Wei, Anastasopoulos, Achilleas, Kim, Hun-Seok]
通讯作者: Kim, Hun-Seok
SWIFT: Instantaneous Feedback-based Adaptive Communications and Networks
海外基金