CIF: Small: Collaborative Research: Next Generation Communications with Low-Resolution ADCs: Fundamentals and Practical Design

CIF：小型：协作研究：采用低分辨率 ADC 的下一代通信：基础知识和实用设计

• 批准号: 1527079
• 负责人: Robert Heath
• 金额: $ 24.9万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1527079&HistoricalAwards=false
• 关键词: CIF; Small; Collaborative; Research; Next

As communication systems embrace ever wider bandwidths, analog-to-digital converters (ADCs) struggle to meet rate, resolution, and power requirements. The problem is exacerbated by the massive antenna arrays under consideration for next-generation wireless, which imply tens or even hundreds of receive channels. In response, this project develops new communication systems that operate with very low-resolution (e.g., 1-3 bit) ADCs. In particular, the investigators derive fundamental limits on quantized many-antenna communication and design advanced communications strategies to approach those limits.The use of low-resolution ADCs radically changes both the theory and practice of communication, motivating a thorough re-examination of capacity bounds, modulation and coding designs, receiver processing algorithms, and limited-feedback strategies. The investigators address these topics in the context of multiple-input multiple-output systems with massive arrays. In particular, they derive theoretical capacity (or achievable rate) bounds that account for coarse ADC quantization, imperfect channel state information (CSI) at the receiver, partial CSI at the transmitter, and multi-user interference. They also characterize the mean-squared error achievable for sparse-channel estimation and the symbol-error rate achievable via equalization, both in the large-system limit. In addition, they develop limited-feedback strategies to provide transmitter CSI to the transmitter, transmitter-precoding designs that exploit that CSI, and optimized training sequences. Furthermore, they develop channel-estimation algorithms that learn and exploit channel sparsity; equalization (and/or multi-user detection) algorithms that take channel-estimation error into account; and efficient strategies for joint channel-estimation, equalization, and decoding. Finally, they develop optimization and adaptation strategies for the ADC thresholds, and blind calibration strategies that account for ADC imperfections. The research methodology leverages recent results from wireless communication theory, one-bit compressed sensing, and approximate message passing.

随着通信系统涵盖更广泛的带宽，模数转换器（ADC）努力满足速率，分辨率和功率要求。下一代无线的大量天线阵列加剧了问题，这意味着几十甚至数百个接收通道。作为回应，该项目开发了新的通信系统，这些系统以非常低分辨率（例如1-3位）ADC运行。尤其是，研究人员对量化多个安德纳沟通和设计高级通信策略的基本限制来实现这些限制。低分辨率ADC的使用从根本上改变了沟通的理论和实践，激发了能力界限，模式和编码设计，接收器处理算法的彻底重新检查，并进行了限制反应策略。研究人员在具有大量阵列的多输入多输出系统的背景下解决了这些主题。特别是，它们得出了理论能力（或可实现的速率）界限，以说明粗略的ADC量化，不完美的通道状态信息（CSI），在接收器处，发射器处的部分CSI和多用户干扰。它们还表征了稀疏通道估计可实现的于点错误，并且在大型系统限制中都可以通过均衡来实现的符号率率。此外，他们开发了有限的反馈策略，以向发射器提供发射机CSI，发射器代码设计，以利用CSI和优化的训练序列。此外，他们开发了学习和利用频道稀疏性的渠道估计算法。均衡（和/或多用户检测）算法，这些算法考虑了频道估计误差；和有效的联合渠道估计，均衡和解码的策略。最后，他们针对ADC阈值制定了优化和适应策略，以及解释ADC缺陷的盲目校准策略。研究方法利用了无线通信理论，一位压缩感应和近似消息传递的最新结果。