NSF/ENG/ECCS-BSF: Sensing and Estimation under Energy and Communication Constraints

NSF/ENG/ECCS-BSF：能源和通信约束下的传感和估计

• 批准号: 1609695
• 负责人: Andrea Goldsmith
• 金额: $ 32.24万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1609695&HistoricalAwards=false
• 关键词: NSF; ENG; ECCS; BSF; Sensing

Many of the sophisticated electronic devices ubiquitous today have been enabled by digital signal processing (DSP) that converts the analog signals associated with the physical world to the digital domain. Moore's law allows such digitized signals to be processed and communicated with small, low-cost energy-efficient digital hardware. Yet fundamental questions regarding the performance limits and tradeoffs associated with the sampling, quantization, communication, and reconstruction of analog data with respect to both fidelity and overall energy consumption remain open. Better understanding of these performance limits and tradeoffs will significant enhance capabilities for collection, processing, and communication of analog sensor data beyond the current state of the art. These capabilities are particularly acute for emerging sensor network applications in health and wellness, security, energy-efficient infrastructures, and smart cities, where many low-cost low-energy analog sensors will be collecting large amounts of data and transmitting it to remote locations for processing. The proposed research will investigate the interplay between sampling, quantization, communication and reconstruction of analog signals under memory constraints, communication constraints, and energy constraints. The goal of the proposed work is to develop a fundamental rate-distortion theory for the sampling, quantization, and reconstruction of analog data subject to these constraints. Specific Shannon-theoretical limits for the performance of combined sampling and source coding - the tradeoff between the digitizer's sampling rate and quantization precision in terms of distortion will be investigated. In addition, the optimal fidelity in the reconstruction of analog data from a sequence of samples will be derived. Fundamental limits on communication under energy constraints, where an analog sensor must digitize and communication its data under finite energy constraints, will be determined. Methods used will draw from prior work in Shannon capacity, rate-distortion theory, joint source-channel coding and separation, sampling, estimation, and statistics. In particular, after extending existing results on rate-distortion theory to incorporate sub-Nyquist sampling, joint source-channel coding techniques applied to the sampled analog data transmitted over a rate-limited channel will be developed. Energy constraints will be incorporated based on information-theoretic models for computation energy along with recent results on finite-block-length codes and minimum energy-per-bit capacity.

如今，许多复杂的电子设备无处不在，已通过数字信号处理（DSP）启用，该数字信号处理（DSP）将与物理世界相关的模拟信号转换为数字域。摩尔定律允许处理这种数字化信号，并与小型低成本的能源有效的数字硬件进行处理和通信。 然而，与忠诚度和整体能源消耗相关的采样，量化，沟通和重建相关的绩效限制和权衡的基本问题仍然开放。更好地理解这些性能限制和权衡，将显着增强模拟传感器数据的收集，处理和通信能力，超出当前的最新状态。这些功能对于新兴传感器网络在健康和保健，安全性，节能基础设施和智能城市中的应用尤其重要，许多低成本的低成能模拟传感器将收集大量数据并将其传输到远程位置以供偏远地区传输到偏远地区加工。拟议的研究将研究在记忆约束，通信约束和能量限制下模拟信号的采样，量化，通信和重建之间的相互作用。 拟议的工作的目的是开发一种基本的利率统计理论，用于对受这些约束的模拟数据进行抽样，量化和重建。针对组合采样和源编码的性能的特定香农理论限制 - 数字化器的采样率和量化精度之间的权衡将在扭曲方面进行调查。另外，将得出从样本序列重建模拟数据中的最佳保真度。在能量限制下的通信的基本限制将确定模拟传感器必须在有限的能量限制下进行数字化并进行通信。所使用的方法将以香农的能力，速率理论，联合源通道编码和分离，采样，估计和统计数据从先前的工作中获取。特别是，在扩展了对速率理论的现有结果以纳入子nyquist抽样之后，将开发应用于对速率限制通道传输的采样模拟数据应用的联合源通道编码技术。能源约束将根据计算能量的信息理论模型以及有关有限块长度代码的最新结果和最小的每位能量容量来合并。