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: 1527162
• 负责人: Philip Schniter
• 金额: $ 24.71万
• 依托单位: Ohio State University
• 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=1527162&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.

随着通信系统采用越来越宽的带宽，模数转换器（ADC）难以满足速率、分辨率和功耗要求。下一代无线网络考虑使用大量天线阵列，这意味着要有数十甚至数百个接收通道，从而加剧了这一问题。作为回应，该项目开发了以非常低的分辨率（例如，1-3位）ADC。特别是，研究人员推导出量化多天线通信的基本限制，并设计先进的通信策略来接近这些限制。低分辨率ADC的使用从根本上改变了通信的理论和实践，激发了对容量界限、调制和编码设计、接收机处理算法和有限反馈策略的彻底重新审视。研究人员在具有大规模阵列的多输入多输出系统的背景下解决了这些问题。特别是，他们得出的理论容量（或可实现的速率）的界限，占粗ADC量化，不完美的信道状态信息（CSI）在接收机，部分CSI在发射机，和多用户干扰。他们还描述了稀疏信道估计可实现的均方误差和通过均衡可实现的符号错误率，两者都在大系统限制下。此外，他们还开发了有限反馈策略，以向发射机提供发射机CSI，利用该CSI的发射机预编码设计以及优化的训练序列。此外，他们还开发了学习和利用信道稀疏性的信道估计算法;考虑信道估计误差的均衡（和/或多用户检测）算法;以及联合信道估计，均衡和解码的有效策略。最后，他们开发了ADC阈值的优化和自适应策略，以及考虑ADC缺陷的盲校准策略。该研究方法利用了无线通信理论，一位压缩感知和近似消息传递的最新成果。