下一代无线通信系统中的高清晰稀疏信道估计方法研究

Using wireless techniques to promote the rapid development of the national economy but at the same time the rapid increasing number of wireless users pose many challenges for current communication systems, such as inadequate communications techniques, scarce spectrum resource and limited energy efficiency. Next-generation wireless communication systems are expected to solve these problems to achieve higher data rate transmission, higher reliability, higher energy efficiency, lower cost communication and support higher speed mobile communication as well. To achieve these targets, compressive sensing (CS) based high-resolution sparse channel estimation is one of most promising techniques. Considering the limitations of existing wireless communication systems, we investigate the CS-based high-resolution sparse channel estimation techniques from three aspects: 1) low-speed analogy-to-digital converter (ADC) is adopted in the next generation wireless communication systems to reduce the communication cost; 2) propose CS-based sparse learning algorithm to fully exploit sparse structure information in doubly selective fading channels, so that it can improve spectrum resource utilization and energy efficiency ; 3) propose high resolution sparse channel estimation methods to estimate the high mobility channels and hence greatly improve the communication reliability.

无线通信技术的不断革新促进了国民经济的高速发展，但无线产品的普及和无线用户的快速增长也给目前的无线通信系统提出了很多挑战：技术局限性、资源紧缺和降低成本。下一代无线通信系统有望解决这些难题实现更高的通信码速率，高可靠性，高能量效率，低通信成本和支持高速移动通信。为了实现这些指标，基于压缩感知理论（CS）的高清晰稀疏信道方法有望成为候选解决技术之一。基于现有的无线通信系统的局限性，本课题主要从三方面着手来研究基于CS理论的高清晰稀疏信道估计技术。在下一代无线通信系统中采用CS采样来降低模数转换器（ADC）采样率要求从而可以降低成本；利用CS稀疏学习算法来深度挖掘双选择性衰落信道的稀疏结构信息，提高频谱资源利用率和能量效率；采用CS高清晰稀疏信道估计方法来有效估计高速移动信道，大大地提高通信系统的可靠性。

无线通信技术的不断革新促进了国民经济的高速发展，但无线产品的普及和无线用户的快速增长也给目前的无线通信系统提出了很多挑战：技术局限性、资源紧缺和降低成本。下一代无线通信系统有望解决这些难题实现更高的通信码速率，高可靠性，高能量效率，低通信成本和支持高速移动通信。为了实现这些指标，基于压缩感知(Compressive Sensing, CS)理论的高清晰稀疏信道方法有望成为候选解决技术之一。基于现有的无线通信系统的局限性，本课题主要从三方面着手来研究基于CS理论的高清晰稀疏信道估计技术。在下一代无线通信系统中采用CS采样来降低模数转换器（ADC）采样率要求从而可以降低成本；利用CS稀疏学习算法来深度挖掘双选择性衰落信道的稀疏结构信息，提高频谱资源利用率和能量效率；采用CS高清晰稀疏信道估计方法来有效估计高速移动信道，极大地提高了通信系统的可靠性。