CIF: Small: Power Consumption in Communication

CIF：小：通信功耗

• 批准号: 0917212
• 负责人: Anant Sahai
• 金额: $ 29.66万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0917212&HistoricalAwards=false
• 关键词: CIF; Small; Power; Consumption; Communication

Power consumption is an increasingly important issue across society.For communication, as the ranges of links in wireless networkscontinue to shrink, the power consumed in the encoding and decodingbecomes a decidedly nontrivial factor in the choice of systemarchitecture. This is particularly important in settings such aswireless patient monitoring, personal area networks, sensor networks,etc. Shannon's classical information theory only established thetradeoff between rate and transmit power as the probability of errorgoes to zero and the block-length goes to infinity. This research isabout giving new conceptual tools for reasoning about the powerconsumption in encoding and decoding as well. The core idea is that inthe age of billion transistor chips, the proper metric for complexityis the power consumed by the implementation.Just as simplified channel models have enabled sophisticated analysisthat has revealed deep insights into error correction and transmitpower, this research develops simplified implementation models thatare amenable to analysis. This reveals the fundamental tradeoffsunderlying the interplay between transmission and processingpowers. Crucially, the models developed are compatible with modernapproaches to iterative and "turbo" decoding by massively parallelASICs, while also not being limited to just the currently knownfamilies of sparse-graph codes. By developing a unified mathematicalframework, this research allows us to understand the total power costof meeting performance objectives like high rate, low distortion, lowdelay and low probability of error. This in turn leads to anunderstanding of how to better engineer wireless systems as a whole:opening up avenues for collaboration between circuit designers,communication theorists, and networking researchers working at higherlayers.

电力消耗是一个越来越重要的社会问题。对于通信来说，随着无线网络中的链路范围不断缩小，编码和解码所消耗的功率成为选择系统架构时一个决定性的重要因素。这在无线病人监测、个人区域网络、传感器网络等环境中尤为重要。香农的经典信息理论只在错误概率趋近于零和块长度趋近于无穷大时，才建立了速率和传输功率之间的权衡。本研究旨在为编码和解码的功耗推理提供新的概念性工具。核心思想是，在十亿晶体管芯片的时代，衡量复杂性的适当指标是实现所消耗的功率。正如简化的信道模型使复杂的分析成为可能，揭示了对纠错和发射功率的深刻见解，本研究开发了简化的实现模型，便于分析。这揭示了传输能力和处理能力之间相互作用的基本权衡。至关重要的是，所开发的模型与大规模并行elasic的迭代和“涡轮”解码的现代方法兼容，同时也不局限于目前已知的稀疏图代码族。通过开发统一的数学框架，本研究使我们能够了解满足高速率，低失真，低延迟和低错误概率等性能目标的总功耗。这反过来又导致了对如何更好地设计整个无线系统的理解：为电路设计师、通信理论家和在高层工作的网络研究人员之间的合作开辟了道路。