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.

频谱的稀缺性，尤其是在低6-GHz频率范围内，已融合了频谱有效的大规模多输入多输出（MMIMO）通信。但是，使用具有多个高功率射频（RF）发射器的大型和密集的天线阵列会引起天线 - 放大器阻抗不匹配，效率降解和急剧温度升高的技术挑战。该项目的总体目标是将发射机操作的范式从“静态和模型驱动”转移到“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光谱效率。 RF发射器阵列的基于AI的重新配置框架可以应用于许多其他可重新配置的RF电路和子系统，例如，具有动态空间过滤，可调过滤器，天线调谐器和RF信号处理器的MMIMO接收器，使得真正明智的辐射。除了无线通信之外，这项研究的结果还可能会影响其他各种天线阵列系统，例如主动分阶段阵列雷达，无线成像和灵敏度以及无线功率传递。此外，提议的基于学习的方法用于解决这种高度动态和非平稳问题的方法可以推广到其他复杂的实时系统，包括机器人控制，智能运输系统和下一代无线网络。通过以下综合教育工作将进一步扩大该项目的影响：a）通过适当的计划吸引和保留代表性不足的学生； b）通过适当的课程与本科生一起吸引； c）在佛罗里达大学的研究生和本科课程中整合研究结果； D）向当地社区推广。 RF功率放大器（PA）通常是在静态/准静态负载阻抗和环境温度的假设下设计和部署的。然而，由于强烈的天线和热耦合，这些假设对于多Antenna MMIMO系统是无效的，导致系统水平下的光谱和能量效率降解。为了应对这一基本挑战，该项目旨在将尖端的AI/机器学习（ML）技术转变为以硬件为中心的RF发射器设计。具体而言，提出了一种新型的双输入杂交负载调制的平衡放大器（DI-HLMBA），提供无与伦比的效率，带宽和线性性。更重要的是，在数字和模拟域中，DI-HLMBA的高度可重构性质使动态闭环控制能够抵消MMIMO操作期间的天线不匹配和温度上升，这可以推广为加强学习（RL）过程。此外，将通过基于非组织马尔可夫决策过程的RL框架和基于元稳定性的硬件实现策略进行动态优化DI-HLMBA的问题，并使用可重新配置的现场可编程栅极阵列（FPGA）技术，并紧密耦合，以实现实时优化实时的低延迟。此外，通过宽带分形天线阵列的唯一设计方法，将将AI辅助操作以及多波段多体标能力从单个PA/发射器扩展到MMIMO阵列。总体而言，这项研究基于数字后端，RF前端，天线阵列，感应，AI算法，FPGA加速和模块间接口的整体整合建立了跨学科设计方法，并建立了能源和频谱的MMIMO系统。影响审查标准。