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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=665927
• 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.

信道参数估计是任何无线通信系统的核心任务。事实上，它允许系统通过其传播信道的知识在完全了解其环境的情况下正确操作。在这项发现资助研究计划的框架内，我们将考虑各种系统配置和信令方案的无线信道参数估计问题，这些系统配置和信令方案面向当前和未来的通信系统，例如新的第四代（4G）高级LTE（LTE-A）无线网络和超越（LTE-B）以及5G。主要目标是开发先进的信道识别和参数估计技术和克拉美-拉奥下界（CRLB），最好是在封闭的形式，在存在各种类型的dilution，即多径传播，多个接收天线，多载波通信结合高阶调制（M-QAM）。我们将采用最大似然（ML）方法来充分利用这些类型的多样性。为了显着降低复杂性，我们将采用新的强大的重要性采样技术和全局优化原理来实现ML方法。为了提高频谱效率，我们将优先开发非数据辅助（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。我们还将研究新的码辅助（CA）设计的涡轮参数估计。为了科普诸如LTE-A的上行链路上的非持久调度中的非连续分组交换传输中的短观测记录，我们将求助于新的强大的压缩感测（CS）技术。