CCF: SHF: Small: Self-Adaptive Interference-Avoiding Wireless Receiver Hardware through Real-Time Learning-Based Automatic Optimization of Power-Efficient Integrated Circuits

CCF：SHF：小型：通过基于实时学习的高能效集成电路自动优化实现自适应干扰避免无线接收器硬件

• 批准号: 2218845
• 负责人: Francesco Restuccia
• 金额: $ 59.99万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2218845&HistoricalAwards=false
• 关键词: CCF; SHF; Small; Self; Adaptive

The sheer number of devices in the Internet of Things (IoT) is creating an extremely crowded and dynamic spectrum environment. As such, continuous and seamless adaptation of wireless-communication parameters will become essential in the next few years. On the other hand, typical radio-frequency (RF) integrated circuits are statically optimized for a fixed set of parameters and communication standards, which leaves limited room for real-time optimization at the intersection of hardware and software. Indeed, many of today’s devices are tailored for worst-case scenarios associated with a particular communication standard, which leads to limited performance and excessive power consumption. Conversely, real-time optimization allows to quickly reconfigure circuits for energy-efficient operation while achieving system-level performance goals. This challenge will be addressed by devising Radio Real-Time Machine Learning (RadioRTML), a platform that will demonstrate the feasibility of automatic RF integrated-circuit optimization through machine learning (ML) techniques directly implemented with reconfigurable hardware. This research will transform how the optimization of radio frequency systems is done today by demonstrating that real-time ML-based adaptation of RF parameters is able to achieve significant performance improvements. The outcomes will have long-lasting benefits for the design and optimization of low-power RF circuits for adaptive energy-efficient communication. Furthermore, the project will provide unique training for graduate and undergraduate students at the crossroads of machine learning and integrated circuit design. Automatic machine-learning-based optimization of RF integrated circuits will be investigated through digital control of analog RF front-end circuits. Novel deep reinforcement-learning (DRL) algorithms will be developed to deliver unprecedented flexibility while improving energy efficiency and minimizing interference impacts. A key challenge in the application of DRL is to design a policy network expressive enough to achieve the required performance, yet implementable in a resource-constrained embedded IoT platform. For this reason, new techniques for effective and efficient policy network design will be created. Since DRL is known to exhibit slow convergence times and high energy consumption, this research will include the design of novel transfer-learning techniques to speed up DRL convergence, and it will leverage edge-computing techniques to significantly reduce the energy consumption of the platform. At the RF circuit level, customized topologies and design techniques will be created to construct a flexible receiver front-end. RadioRTML will be prototyped on a System-on-Chip (SoC)-based software-defined radio (SDR) connected to a custom-designed printed circuit board for the RF front-end chip. To thoroughly train and test the RadioRTML algorithms, large-scale data collection will be performed utilizing Arena (a 64-antenna 24-SDR system located at Northeastern University), Colosseum (the world’s largest network emulator), and the NSF POWDER testbed.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.

物联网（IoT）中设备的绝对数量正在创造一个极其拥挤和动态的频谱环境。因此，在未来几年内，无线通信参数的持续和无缝调整将变得至关重要。另一方面，典型的射频（RF）集成电路是针对一组固定的参数和通信标准进行静态优化的，这使得硬件和软件交叉点的实时优化空间有限。事实上，今天的许多设备都是针对与特定通信标准相关的最坏情况量身定制的，这导致了有限的性能和过度的功耗。相反，实时优化允许快速重新配置电路以实现节能操作，同时实现系统级性能目标。这一挑战将通过设计无线电实时机器学习（RadioRTML）来解决，该平台将通过直接使用可重构硬件实现的机器学习（ML）技术来演示自动射频集成电路优化的可行性。这项研究将改变射频系统的优化方式，证明基于实时ml的射频参数适应能够实现显著的性能改进。研究结果将对设计和优化低功耗射频电路以实现自适应节能通信具有持久的好处。此外，该项目将为机器学习和集成电路设计交叉领域的研究生和本科生提供独特的培训。通过模拟射频前端电路的数字控制，研究基于自动机器学习的射频集成电路优化。将开发新的深度强化学习（DRL）算法，以提供前所未有的灵活性，同时提高能源效率并最大限度地减少干扰影响。DRL应用中的一个关键挑战是设计一个足够表达的策略网络，以达到所需的性能，但在资源受限的嵌入式物联网平台中可实现。因此，将创造有效和高效的政策网络设计的新技术。由于已知DRL表现出缓慢的收敛时间和高能耗，本研究将包括设计新颖的迁移学习技术来加速DRL的收敛，并将利用边缘计算技术来显着降低平台的能耗。在射频电路级，将创建定制拓扑和设计技术来构建灵活的接收器前端。RadioRTML将在基于系统级芯片（SoC）的软件定义无线电（SDR）上进行原型设计，并连接到用于射频前端芯片的定制设计印刷电路板。为了彻底训练和测试RadioRTML算法，将利用Arena（位于东北大学的64天线24 sdr系统）、Colosseum（世界上最大的网络模拟器）和NSF POWDER试验台进行大规模数据收集。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。