SpecEES: Collaborative Research: Enabling Spectrum and Energy-Efficient Dynamic Spectrum Access Wireless Networks using Neuromorphic Computing

SpecEES：协作研究：使用神经形态计算实现频谱和节能动态频谱接入无线网络

• 批准号: 1811497
• 负责人: Lingjia Liu
• 金额: $ 47.88万
• 依托单位: Virginia Polytechnic Institute and 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: 2017-08-10 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1811497&HistoricalAwards=false
• 关键词: SpecEES; Collaborative; Research; Enabling; Spectrum

During the last two decades, the use of Radio Frequency (RF) spectrum has increased tremendously due to the ever growing demand for wireless connectivity. The existing network technologies that support the current wireless data demand are expected to increase their capacity significantly in the next decade, calling for spectrum and energy efficient communication strategies. There are two popular approaches to efficiently utilize the RF spectrum: One is the cognitive radio networks which allow mobile users to share the spectrum that has been primarily allocated to other services such as television broadcasting, global position system (GPS), radar, weather forecasting, etc., provided that the mobile users impose limited interference to existing services. Another approach is to enhance the mobile broadband networks via expanded bandwidth, massive Multiple-Input Multiple-Output (MIMO) systems, and densified heterogeneous networks (HetNets). However, both approaches have limitations and have different impacts on spectrum efficiency and energy efficiency. In addition, current hardware platforms exhibit formidable challenges in supporting high computational complexity and low power consumption. This project introduces a novel network architecture and its application-specific hardware optimization using neuromorphic computing devices. The new wireless network architecture allows mobile users to perform spatio-temporal spectrum sensing and actively search for dynamic spectrum access (DSA) opportunities to enable short-range and local communications. Meanwhile, neuromorphic computing devices that mimic bio-neurological processes will be designed to tackle the high computational complexity of the new dynamic spectrum access approach with extremely low power consumption. In this way, we will be able to enable our nation's next-generation wireless communications and networking that are intelligent, spectrum-efficient, and energy-efficient in a dynamic spectrum environment. The developed concepts and technologies will also help achieve National Broadband Plan which targets at significant improvements in the efficiency of RF spectrum utilization. The project has an extensive education and outreach plan which includes designing new course components on energy-efficient communications, analog neuron circuits, and computational intelligence for wireless networks, joint training of graduate and undergraduate researchers between the two collaborative institutions, and outreach to telecommunication industry and underrepresented students through seminars and diversity programs.Short-range and local communications are extremely beneficial for spectrum and energy efficiency. The research objective of the project is to 1) design DSA-enabled HetNets to enable short-range/local spectrum access to improve the spectrum and energy efficiency, and 2) leverage neuromorphic computing architecture to efficiently solve the associated resource allocation problems with extremely high energy efficiency. To achieve the goal, the project is organized into four interconnected research thrusts. Thrust 1 focuses on spatio-temporal spectrum sensing with MIMO transceivers. Thrust 2 investigates cooperative communications and resource allocation for DSA-enabled HetNets. Thrust 3 studies neuromorphic computing based hardware design for DSA-enabled HetNets. Thrust 4 develops and evaluates the hardware-software test-bed. The proposed paradigm shift from centralized base-station-controlled approach to the decentralized approach will revolutionize the future wireless network design, where the individual users will play stronger role in spectrum access and drastically change the network topology by utilizing neuromorphic computing devices. The hardware-software co-design methodologies developed in this project can be readily applied to other related fields: computer communication networks, cyber security, and energy-harvesting communications, etc.

在过去二十年中，由于对无线连接的需求不断增长，射频（RF）频谱的使用大幅增加。支持当前无线数据需求的现有网络技术预计将在未来十年内显着增加其容量，这需要频谱和节能的通信策略。有两种流行的方法来有效地利用RF频谱：一种是认知无线电网络，其允许移动的用户共享主要被分配给其他服务（诸如电视广播、全球定位系统（GPS）、雷达、天气预报等）的频谱，只要移动的用户对现有服务施加有限的干扰。另一种方法是通过扩展带宽、大规模多输入多输出（MIMO）系统和密集异构网络（HetNet）来增强移动的宽带网络。然而，这两种方法都有局限性，并且对频谱效率和能量效率具有不同的影响。此外，当前的硬件平台在支持高计算复杂度和低功耗方面表现出巨大的挑战。该项目介绍了一种新的网络架构及其应用特定的硬件优化使用神经形态计算设备。新的无线网络架构允许移动的用户执行时空频谱感测并主动搜索动态频谱接入（DSA）机会以实现短距离和本地通信。与此同时，模拟生物神经过程的神经形态计算设备将被设计为以极低的功耗解决新的动态频谱接入方法的高计算复杂性。 通过这种方式，我们将能够在动态频谱环境中实现智能、频谱高效和节能的下一代无线通信和网络。开发的概念和技术还将有助于实现国家宽带计划，该计划旨在显著提高射频频谱利用效率。该项目有一个广泛的教育和推广计划，其中包括设计关于节能通信、模拟神经元电路和无线网络计算智能的新课程组成部分，两个合作机构之间的研究生和本科生研究人员的联合培训，通过研讨会和多元化项目，与电信行业和代表性不足的学生进行接触。范围和本地通信对于频谱和能量效率是极其有益的。该项目的研究目标是：1）设计支持DSA的HetNet，以实现短距离/本地频谱接入，从而提高频谱和能源效率; 2）利用神经形态计算架构，以极高的能源效率有效解决相关的资源分配问题。为了实现这一目标，该项目分为四个相互关联的研究重点。推力1集中于使用MIMO收发器的时空频谱感测。推力2研究DSA使能的HetNet的协作通信和资源分配。Thrust 3研究了基于神经形态计算的DSA支持的HetNet硬件设计。推力4开发和评估硬件软件测试平台。从集中式基站控制的方法到分散式方法的模式转变将彻底改变未来的无线网络设计，其中个人用户将在频谱接入中发挥更大的作用，并通过利用神经形态计算设备来彻底改变网络拓扑。本项目所开发的软硬件协同设计方法可以很容易地应用于其他相关领域：计算机通信网络、网络安全和能量收集通信等。

项目成果

Random Network Coding Enabled Routing in Swarm Unmanned Aerial Vehicle Networks

• DOI: 10.1109/globecom38437.2019.9013858
• 发表时间: 2019-12
• 期刊: 2019 IEEE Global Communications Conference (GLOBECOM)
• 作者: Hao Song;Lingjia Liu;Scott M. Pudlewski;E. Bentley

Learning for Detection: MIMO-OFDM Symbol Detection Through Downlink Pilots

检测学习：通过下行链路导频进行 MIMO-OFDM 符号检测

• DOI: 10.1109/twc.2020.2976004
• 发表时间: 2020
• 期刊: IEEE Transactions on Wireless Communications
• 影响因子: 10.4
• 作者: Zhou, Zhou;Liu, Lingjia;Chang, Hao-Hsuan
• 通讯作者: Chang, Hao-Hsuan

Cache-aided Cooperative Device-to-Device (D2D) Networks: A Stochastic Geometry View

缓存辅助协作设备到设备 (D2D) 网络：随机几何视图

• DOI: 10.1109/tcomm.2019.2931556
• 发表时间: 2019
• 期刊: IEEE Transactions on Communications
• 影响因子: 8.3
• 作者: Junchao Ma;Lingjia Liu;Bodong Shang;Pingzhi Fan
• 通讯作者: Pingzhi Fan

QoS-Aware D2D Cellular Networks With Spatial Spectrum Sensing: A Stochastic Geometry View

• DOI: 10.1109/tcomm.2018.2889246
• 发表时间: 2019-05
• 期刊: IEEE Transactions on Communications
• 影响因子: 8.3
• 作者: Hao Chen;Lingjia Liu;Harpreet S. Dhillon;Y. Yi

Energy-Efficient Wireless Communications: From Energy Modeling to Performance Evaluation

• DOI: 10.1109/tvt.2019.2921304
• 发表时间: 2019-06
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: F. Mahmood;E. Perrins;Lingjia Liu