EFFICIENT TESTING AND POST-MANUFACTURE TUNING OF BEAMFORMING MIMO WIRELESS COMMUNICATION SYSTEMS: ALGORITHMS AND INFRASTRUCTURE

波束赋形 MIMO 无线通信系统的高效测试和制造后调整：算法和基础设施

• 批准号: 1815653
• 负责人: Abhijit Chatterjee
• 金额: $ 36万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815653&HistoricalAwards=false
• 关键词: EFFICIENT; TESTING; POST; MANUFACTURE; TUNING

Efficient Parallel Testing and Tuning of Beamforming MIMO Wireless SystemsThere has been a revolution in the use of multiple-input multiple-output (MIMO) wireless communication systems over the last decade. It is expected that mobile data traffic will increase by up to 1000X by 2020 as compared to 2010. Future 5G wireless systems (communication data rates 50Gbps) will deploy massive MIMO systems with large numbers of transmit and receive antennas and novel RF transceiver architectures that admit RF beamforming. Research on 5G massive MIMO systems is moving forward at an electrifying pace. It will be possible to point electromagnetic beams towards moving targets while simultaneously communicating at extremely high speeds and minimizing interference with other users. Downloading a high definition film will be possible in less than a second. However, with the dramatically increasing levels of circuit complexity and higher operating speeds, the underlying electronics will be highly susceptible to manufacturing process variations, electrical degradation and defects. At high data rates of communication, the effects of device non-idealities on RF system performance can be dramatic. Power consumption will be a major issue since a large number of transmitter and receiver chains will be involved. Such massive MIMO systems will need to be tested extensively and tuned for quality prior to sale. In the extreme, such systems will need to possess built-in self-testing and self-tuning capability to automatically compensate for field wear and tear due to electrical, thermal and mechanical stress.The first problem to solve is efficient low-cost manufacturing production test of a range of MIMO systems with the capacity to handle 5G systems with 10-100 RF chains. State of the art test methods require that test signals to individual RF chains have frequency separation for the individual RF chain non-linearities to be assessed independent of nonlinearities in other chains. Conversely, given a set of frequencies that can be generated, only a certain maximum number of RF chains can be tested in parallel. A key goal is to design "frequency-efficient" tests and back-end response analysis algorithms that do not require such frequency separation allowing large numbers of RF chains to be tested in parallel. A second key goal is parallel gain and phase tuning of as many RF chains as possible using intelligent testing and response-analysis algorithms. One way to speed up the tuning procedure is to use the parallel testing procedure described above, to perform parallel tuning of the MIMO system well. Such parallel tuning can be supported by machine learning algorithms that predict the best tuning knob configurations for each chain based on specific time and frequency domain response features extracted from parallel testing techniques. To enable testing and tuning, high-speed signals need to be captured and analyzed for signal fidelity. To this end, the use of proposed incoherent undersampling for acquisition of test response signals provides a significant avenue for reducing testing costs by significantly simplifying the hardware required for high-speed device testing and characterization. Overall, the use of the proposed techniques will allow massive MIMO systems to be tested and tuned, post-manufacture and in the field, without the need for complex test instrumentation in 10s of ms test time, significantly reducing test cost while increasing product yield and field reliability.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.

波束成形 MIMO 无线系统的高效并行测试和调谐过去十年中，多输入多输出 (MIMO) 无线通信系统的使用发生了一场革命。预计到 2020 年，移动数据流量将比 2010 年增加 1000 倍。未来 5G 无线系统（通信数据速率 50Gbps）将部署大规模 MIMO 系统，该系统具有大量发射和接收天线以及新颖的 RF 收发器架构，可容纳射频波束形成。 5G 大规模 MIMO 系统的研究正在以惊人的速度向前推进。可以将电磁波束指向移动目标，同时以极高的速度进行通信，并最大限度地减少对其他用户的干扰。下载高清电影将在不到一秒的时间内完成。然而，随着电路复杂性的急剧增加和运行速度的提高，底层电子设备将非常容易受到制造工艺变化、电气退化和缺陷的影响。在高数据通信速率下，设备非理想性对射频系统性能的影响可能非常显着。由于涉及大量发射器和接收器链，功耗将是一个主要问题。此类大规模 MIMO 系统在销售前需要进行广泛的测试和质量调整。 在极端情况下，此类系统将需要具备内置的自测试和自调节功能，以自动补偿由于电、热和机械应力引起的现场磨损。首先要解决的问题是高效、低成本的制造生产测试一系列 MIMO 系统，能够处理具有 10-100 个射频链的 5G 系统。最先进的测试方法要求到各个射频链的测试信号具有频率间隔，以便独立于其他链中的非线性来评估各个射频链的非线性。相反，给定一组可以生成的频率，只能并行测试一定最大数量的射频链。一个关键目标是设计“频率高效”测试和后端响应分析算法，不需要这种频率分离，从而可以并行测试大量射频链。 第二个关键目标是使用智能测试和响应分析算法对尽可能多的射频链进行并行增益和相位调整。加速调谐过程的一种方法是使用上述并行测试过程，以良好地执行MIMO系统的并行调谐。这种并行调谐可以得到机器学习算法的支持，该算法根据从并行测试技术提取的特定时域和频域响应特征来预测每个链的最佳调谐旋钮配置。为了进行测试和调谐，需要捕获高速信号并分析信号保真度。为此，使用所提出的非相干欠采样来采集测试响应信号，通过显着简化高速器件测试和表征所需的硬件，为降低测试成本提供了重要途径。总体而言，使用所提出的技术将允许在制造后和现场对大规模 MIMO 系统进行测试和调整，而无需在 10 毫秒的测试时间内使用复杂的测试仪器，从而显着降低测试成本，同时提高产品良率和该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Self-Aware MIMO Beamforming Systems : Dynamic Adaptation to Channel Conditions and Manufacturing Variability

自我感知 MIMO 波束成形系统：动态适应信道条件和制造变异性

• 发表时间: 2022-03
• 期刊: Design Automation and Test in Europe
• 作者: S. Kommaraju and A. Chatterjee

Reinforcement Learning Based Power-Optimal Usage of Beamforming Antenna Array for Multi-Way Wireless Communication in Vehicular Traffic Environments

基于强化学习的波束成形天线阵列功率优化使用，用于车辆交通环境中的多路无线通信

• 发表时间: 2022-08
• 期刊: Midwest Symposium on Circuits and Systems
• 作者: Suhasini Kommarraju and Abhijit Chatterjee

Fast EVM Tuning of MIMO Wireless Systems Using Collaborative Parallel Testing and Implicit Reward Driven Learning

使用协作并行测试和隐式奖励驱动学习对 MIMO 无线系统进行快速 EVM 调整

• DOI: 10.1109/itc44778.2020.9325270
• 发表时间: 2020-11
• 期刊: International Test Conference
• 作者: Komarraju, Suhasini;Chatterjee, Abhijit
• 通讯作者: Chatterjee, Abhijit

Dynamic Test Stimulus Adaptation for Analog/RF Circuits Using Booleanized Models Extracted from Hardware

使用从硬件中提取的布尔模型对模拟/射频电路进行动态测试激励自适应

• DOI: 10.1109/tcad.2019.2948902
• 发表时间: 2019-10
• 期刊: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
• 影响因子: 2.9
• 作者: Deyati, Sabyasachi;Muldrey, Barry J.;Chatterjee, Abhijit
• 通讯作者: Chatterjee, Abhijit