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CCSS: AI-Assisted Reconfigurable Dual-Input Load-Modulation Transmitter Array for Energy- and Spectrum-Efficient Massive MIMO Communications

CCSS：人工智能辅助可重构双输入负载调制发射机阵列，用于节能和频谱高效的大规模 MIMO 通信

基本信息

批准号：
2218808
负责人：
Kenle Chen
金额：
$ 50万
依托单位：
The University of Central Florida Board of Trustees
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=2218808&HistoricalAwards=false
关键词：
CCSS AI Assisted Reconfigurable Dual

项目摘要

The scarcity of spectrum, especially in the sub-6-GHz frequency range, has motivated the spectrally efficient massive multi-input multi-output (mMIMO) communications. However, the use of large and dense antenna array with multiple high-power radio frequency (RF) transmitters creates technical challenges of antenna-amplifier impedance mismatch, efficiency degradation, and sharp temperature rise. The overarching goal of this project is to shift the paradigm of transmitter operation from ‘static and model-driven’ to ‘dynamic, intelligent and data-driven’ to significantly enhance the energy and spectrum efficiencies of next-generation wireless systems. The AI-based reconfiguration framework for RF transmitter array can be applied to many other reconfigurable RF circuits and subsystems, e.g., mMIMO receivers with dynamic spatial filtering, tunable filters, antenna tuners, and RF signal processors, making truly intelligent radios feasible. Beyond wireless communications, outcomes of this research may also impact on a variety of other antenna array systems, such as active phased array radars, wireless imaging and sensing, and wireless power transfer. Moreover, the proposed learning-based method for solving such a highly dynamic and non-stationary problem can be generalized to other complex real-time systems including robotic control, intelligent transportation systems, and next-generation wireless networks. The impact of this project will be further expanded through the following integrated educational efforts: a) attracting and retaining underrepresented students through appropriate programs; b) engaging undergraduate students through appropriate programs; c) integration of research findings in graduate and undergraduate courses at University of Central Florida; d) outreach to local community. The RF power amplifier (PA) has conventionally been designed and deployed under the assumption of static/quasi-static load impedance and ambient temperature. Nevertheless, these assumptions are invalid for the multi-antenna mMIMO systems due to strong antenna and thermal couplings, leading to degraded spectral and energy efficiencies at system level. To address this fundamental challenge, this project aims to transform the cutting-edge AI/machine-learning (ML) technologies into the hardware-centric RF transmitter design. Specifically, a novel dual-input hybrid load modulated balanced amplifier (DI-HLMBA) is proposed, offering unparalleled efficiency, bandwidth, and linearity. More importantly, the highly reconfigurable nature of DI-HLMBA in both digital and analog domains enables dynamic closed-loop control to counteract antenna mismatch and temperature upsurge during mMIMO operation, which can be generalized as a reinforcement-learning (RL) process. Additionally, the problem of dynamically optimizing DI-HLMBA will be formulated with a RL framework based on nonstationary Markov Decision Processes and a meta-stability-based hardware implementation strategy with reconfigurable field programmable gate array (FPGA) technology, tightly coupled to achieve real-time low-latency optimization. Furthermore, the AI-assisted operation as well as multi-band multi-standard capability will be extended from the individual PA/transmitter to the mMIMO array through a unique design method for the wideband fractal-shaped antenna array. Overall, this research establishes a cross-disciplinary design methodology based on a holistic integration of digital backend, RF frontend, antenna array, sensing, AI algorithm, FPGA acceleration, and inter-module interfaces to form an energy- and spectrum-efficient mMIMO system.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.

频谱稀缺，尤其是 6 GHz 以下频率范围内的频谱稀缺，推动了频谱高效的大规模多输入多输出 (mMIMO) 通信的发展。然而，使用具有多个高功率射频 (RF) 发射器的大型且密集的天线阵列会带来天线放大器阻抗失配、效率下降和急剧温升的技术挑战。该项目的总体目标是将发射机操作模式从“静态和模型驱动”转变为“动态、智能和数据驱动”，以显着提高下一代无线系统的能量和频谱效率。基于人工智能的射频发射器阵列重构框架可应用于许多其他可重构射频电路和子系统，例如具有动态空间滤波的mMIMO接收器、可调谐滤波器、天线调谐器和射频信号处理器，使真正的智能无线电成为可能。除了无线通信之外，这项研究的成果还可能影响各种其他天线阵列系统，例如有源相控阵雷达、无线成像和传感以及无线电力传输。此外，所提出的用于解决这种高度动态和非平稳问题的基于学习的方法可以推广到其他复杂的实时系统，包括机器人控制、智能交通系统和下一代无线网络。该项目的影响将通过以下综合教育工作进一步扩大： a) 通过适当的计划吸引和留住代表性不足的学生； b) 通过适当的项目吸引本科生； c) 将研究成果整合到中佛罗里达大学研究生和本科生课程中； d) 与当地社区的外展活动。射频功率放大器 (PA) 传统上是在静态/准静态负载阻抗和环境温度的假设下设计和部署的。然而，由于强天线和热耦合，这些假设对于多天线 mMIMO 系统无效，导致系统级频谱和能量效率下降。为了应对这一根本挑战，该项目旨在将尖端的人工智能/机器学习 (ML) 技术转变为以硬件为中心的射频发射器设计。具体来说，提出了一种新型双输入混合负载调制平衡放大器（DI-HLMBA），提供无与伦比的效率、带宽和线性度。更重要的是，DI-HLMBA 在数字和模拟领域的高度可重构特性使得动态闭环控制能够抵消 mMIMO 操作期间的天线失配和温度升高，这可以概括为强化学习 (RL) 过程。此外，动态优化 DI-HLMBA 的问题将通过基于非平稳马尔可夫决策过程的强化学习框架和基于元稳定性的硬件实现策略以及可重构现场可编程门阵列（FPGA）技术来制定，紧密耦合以实现实时低延迟优化。此外，通过宽带分形天线阵列的独特设计方法，人工智能辅助操作以及多频段多标准能力将从单独的PA/发射机扩展到mMIMO阵列。总体而言，这项研究建立了一种跨学科的设计方法，基于数字后端、射频前端、天线阵列、传感、人工智能算法、FPGA加速和模块间接口的整体集成，形成节能和频谱高效的mMIMO系统。该奖项反映了NSF的法定使命，并通过使用基金会的智力和技术进行评估，认为值得支持。更广泛的影响审查标准。