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

信道参数估计是任何无线通信系统的核心任务。事实上，它允许系统通过对其传播通道的了解，在完全了解其环境的情况下正常运行。在这项发现拨款研究计划的框架内，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)LTE-A(LTE-A)无线网络和更高(LTE-B)以及5G。其主要目标是在存在各种类型的分集的情况下，开发先进的信道识别和参数估计技术以及Cramer Rao下限(CRLB)，最好是以封闭形式存在；即多径传播、多接收天线和结合高阶调制的多载波通信(M-QAM)。我们将采用最大似然(ML)方法，充分发挥联合利用这些类型的多样性的好处。为了显著降低复杂性，我们将借助于新的强大的重要性抽样技术和全局优化原则来实现ML方法。为了提高频谱效率，我们将优先开发需要最小导频信号开销的非数据辅助(NDA)估计器或替代数据辅助(DA)版本。我们还将研究用于Turbo参数估计的新码辅助(CA)设计。为了应对非持续调度中非连续分组交换传输中的短观测记录，例如在LTE-A的上行链路上，我们将求助于新的强大的压缩感知(CS)技术。

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.