ITR: Superimposed Training for Wireless Fading Channels

ITR：无线衰落信道的叠加训练

• 批准号: 0218778
• 负责人: Guotong Zhou
• 金额: $ 29万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-15 至 2006-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0218778&HistoricalAwards=false
• 关键词: ITR; Superimposed; Training; Wireless; Fading

G. Tong ZhouGeorgia Tech Research CorpA major challenge in wireless communications is that the channel changes, sometimes rapidly; thus channel state information needs to be acquired frequently before the symbol recovery stage. Two prevalent approaches for channel estimation are training based and self-recovering (i.e., blind) ones. Pilot symbol assisted modulation (PSAM) is a popular training method, in which known pilots are periodically inserted into the symbol stream prior to transmission. These pilot symbols occupy time slots and impose a recurring transmission overhead. Blind approaches on the other hand, are completely data-driven with no loss of information rate but a significant increase in complexity. For fast fading channels, blind algorithms may require too many data to converge or converge too slowly to be practical.This research investigates a class of superimposed training algorithms that combine the best of both worlds. Instead of inserting pilots as in PSAM, known periodic pilots are superimposed onto the symbol stream, prior to waveform modulation. The channel estimation algorithm may appear to be blind, but is superior to blind approaches because simple first-order, and no more than second-order statistical methods, are used for channel estimation. The superimposed training approach can also be viewed as a watermarking technique with the pilot sequence serving as the marker. It is shown to be applicable to a variety of wireless channels: frequency selective, time selective, and doubly selective fading channels, as well as nonlinear channels. Issues investigated include trade-offs among bit error rate, transmission power, information rate and capacity, as well as optimization of pilot strength, transmission power allocation, and the effect of the added pilots on nonlinear power amp!lifiers.

在无线通信中的主要挑战是信道的变化，有时是快速的；因此，在符号恢复阶段之前，需要频繁地获取信道状态信息。目前流行的两种信道估计方法是基于训练的方法和自恢复(即盲)方法。导频符号辅助调制(PSAM)是一种流行的训练方法，其中已知导频在传输之前被周期性地插入到符号流中。这些导频符号占用时隙并施加重复的传输开销。另一方面，盲目方法完全是数据驱动的，不会损失信息率，但会显著增加复杂性。对于快衰落信道，盲算法可能需要太多的数据才能收敛，或者收敛速度太慢而不能实用。不是像在PSAM中那样插入导频，而是在波形调制之前将已知的周期性导频叠加到符号流上。该信道估计算法可能看起来是盲的，但由于使用简单的一阶且不超过二阶统计方法来进行信道估计，因此优于盲方法。叠加训练方法也可以被视为以导频序列作为标记的水印技术。它适用于多种无线信道：频率选择性、时间选择性、双选择性衰落信道，以及非线性信道。研究的问题包括误码率、发射功率、信息率和容量之间的权衡，以及导频强度的优化、发射功率的分配以及增加的导频对非线性功率放大器的影响。