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

在过去的二十年中，由于对无线连接的需求不断增长，射频（RF）频谱的使用大大增加。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大幅增加其容量，这需要频谱和节能通信策略。有效利用射频频谱有两种流行的方法：一种是认知无线电网络，它允许移动用户共享已主要分配给其他业务（如电视广播、全球定位系统（GPS）、雷达、天气预报等）的频谱，前提是移动用户对现有业务施加有限的干扰。另一种方法是通过扩展带宽、大规模多输入多输出（MIMO）系统和致密异构网络（HetNets）来增强移动宽带网络。然而，这两种方法都有局限性，对频谱效率和能源效率的影响也不同。此外，当前的硬件平台在支持高计算复杂性和低功耗方面面临巨大挑战。该项目介绍了一种新的网络架构及其使用神经形态计算设备的特定应用硬件优化。新的无线网络架构允许移动用户执行时空频谱感知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。同时，模拟生物神经过程的神经形态计算设备将被设计用于以极低功耗解决新的动态频谱访问方法的高计算复杂性。通过这种方式，我们将能够使我们国家的下一代无线通信和网络在动态频谱环境中实现智能、频谱效率和节能。开发的概念和技术还将有助于实现旨在显著提高射频频谱利用效率的国家宽带计划。该项目有一个广泛的教育和推广计划，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件，在两个合作机构之间联合培训研究生和本科生研究人员，并通过研讨会和多元化计划向电信行业和代表性不足的学生推广。短距离和本地通信对频谱和能源效率极为有利。该项目的研究目标是：1)设计基于dsa的HetNets，实现短距离/本地频谱接入，提高频谱和能源效率；2)利用神经形态计算架构，以极高的能源效率高效解决相关的资源分配问题。为了实现这一目标，该项目被组织成四个相互关联的研究重点。重点研究MIMO收发器的时空频谱感知。Thrust 2研究基于dsa的HetNets的协作通信和资源分配。Thrust 3研究基于神经形态计算的基于dsa的HetNets硬件设计。Thrust 4开发并评估了硬件软件测试平台。所提出的从集中式基站控制方法到分散方法的范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访问中发挥更大的作用，并通过利用神经形态计算设备彻底改变网络拓扑结构。本项目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、能量收集通信等。

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.