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

波束成形MIMO无线系统的有效并行测试和调谐在过去十年中，多输入多输出（MIMO）无线通信系统的使用发生了革命。与2010年相比，预计到2020年，移动的数据流量将增长高达1000倍。未来的5G无线系统（通信数据速率50 Gbps）将部署具有大量发射和接收天线的大规模MIMO系统以及允许RF波束成形的新型RF收发器架构。5G大规模MIMO系统的研究正在以惊人的速度向前发展。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。下载高清电影将可能在不到一秒钟。然而，随着电路复杂性水平的显著增加和更高的操作速度，底层电子器件将非常容易受到制造工艺变化、电气退化和缺陷的影响。在高数据速率的通信中，器件非理想性对RF系统性能的影响可能是巨大的。功耗将是一个主要问题，因为将涉及大量的发射机和接收机链。这种大规模MIMO系统在销售之前需要进行广泛的测试和质量调整。 在极端情况下，此类系统需要具备内置的自测试和自调整功能，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对一系列MIMO系统进行高效低成本的制造生产测试，这些系统能够处理具有10-100个RF链的5G系统。现有技术的测试方法要求到各个RF链的测试信号具有用于独立于其它链中的非线性来评估的各个RF链非线性的频率分离。相反，给定可以生成的一组频率，只能并行测试某个最大数量的RF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，这些算法不需要这样的频率分离，允许并行测试大量RF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RF链进行并行增益和相位调谐。加速调谐过程的一种方式是使用上述并行测试过程，以很好地执行MIMO系统的并行调谐。这种并行调谐可以由机器学习算法支持，该机器学习算法基于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。为了实现测试和调谐，需要捕获和分析高速信号以实现信号保真度。为此，使用所提出的用于获取测试响应信号的非相干欠采样提供了通过显著简化高速器件测试和表征所需的硬件来降低测试成本的显著途径。总的来说，使用所提出的技术将允许在制造后和现场测试和调整大规模MIMO系统，而不需要在10毫秒的测试时间内使用复杂的测试仪器，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进行评估，更广泛的影响审查标准。

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.