Advanced Channel Parameter Estimation Techniques and Bounds for Next-Generation Wireless Networks

下一代无线网络的先进信道参数估计技术和界限

• 批准号: RGPIN-2015-06013
• 负责人: Stéphenne, Alex
• 金额: $ 1.6万
• 依托单位: Institut national de la recherche scientifique
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=613882
• 关键词: Advanced; Channel; Parameter; Estimation; Techniques

Channel parameter estimation is a crucial task that lies at the heart of any wireless communication system. In fact, it allows the system to properly operate in complete awareness of its environment through the knowledge of its propagation channel. Within the framework of this discovery grant research program, we will consider the problem of wireless channel parameter estimation for various system configurations and signalling schemes that are geared toward current and future communication systems such as the new 4th-generation (4G) LTE-Advanced (LTE-A) wireless networks and beyond (LTE-B) as well as 5G. The key goal is to develop advanced channel identification and parameter estimation techniques and Cramer Rao lower bounds (CRLBs), preferably in closed-form, in the presence of various types of diversities; namely multi-path propagation, multiple receiving antennas, and multi-carrier communications combined with higher-order modulations (M-QAM). We will adopt a maximum likelihood (ML) approach to rip the full benefits of jointly exploiting these types of diversity. To significantly reduce complexity, we will recur to the new powerful importance sampling technique and global optimization principle to implement the ML approach. To increase spectrum efficiency, we will preferably develop non-data-aided (NDA) estimators or alternative data-aided (DA) versions that require the least pilot signal overhead. We will also investigate novel code-aided (CA) designs for turbo parameter estimation. To cope with short observation records in non-continuous packet-switched transmissions in non-persistent scheduling such as on the uplink of LTE-A, we will recur to the new powerful compressive sensing (CS) techniques.

信道参数估计是一项至关重要的任务，是任何无线通信系统的核心。事实上，它允许系统通过对其传播通道的了解，在完全了解其环境的情况下正确运行。在这项发现拨款研究计划的框架内，我们将考虑针对当前和未来通信系统（如新的第4代（4G） LTE-Advanced （LTE-A）无线网络及以后（LTE-B）以及5G）的各种系统配置和信令方案的无线信道参数估计问题。关键目标是开发先进的信道识别和参数估计技术和Cramer Rao下界（crlb），最好是在各种类型的多样性存在下的封闭形式；即多路径传播、多接收天线和结合高阶调制（M-QAM）的多载波通信。我们将采用最大可能性（ML）方法来充分利用共同开发这些类型的多样性的好处。为了显著降低复杂性，我们将使用新的强大的重要性采样技术和全局优化原理来实现机器学习方法。为了提高频谱效率，我们最好开发非数据辅助（NDA）估计器或需要最少导频信号开销的替代数据辅助（DA）版本。我们还将研究用于涡轮参数估计的新型代码辅助（CA）设计。为了解决LTE-A上行链路等非持久调度中非连续分组交换传输的短观测记录问题，我们将重新考虑新的强大的压缩感知（CS）技术。