全维MIMO蜂窝系统中基于量子智能计算的信道估计与干扰协调技术研究

In full-dimensional MIMO (also known as 3D MIMO) system, three-dimensional beamforming technology is adopted to increase cell coverage, so as to prevent inter-cell interference more effectively and increase system throughput and improve spectrum efficiency. Several key technologies in 3D MIMO system will be deeply studied in this project from the point of view of channel modelling, channel estimation and interference coordination. The contents and aims of this project are as follows: ① We will focus our researches on 3D MIMO channel modeling, and try to reveal the critical factors and key problems that effected the channel model. Then the general channel model is set up and extended as a three-dimensional channel model based on multivariate statistical methods, so a 3D MIMO transceiver theoretical model is built. ② Based on the channel model, we will propose a new methods for high efficiently, rapidly and accurately deriving the spatial correlation matrix of channel model by exact and approximate analysis by defining the concepts as "the first correlation fitting" and "the second correlation fitting". Finally, we will establish the channel approximation model which is more applicable to simulate the various wireless environments. ③ We will analysis channel sparse features, we will propose a 3D MIMO channel sparse estimation method based on quantum immune clone algorithm and compressive sensing theory. ④ Based on the exploration of impacts of beamforming, down tilt, power allocation on spectral efficiency, we will propose a new interference coordination method based on quantum bacterial foraging algorithms for 3D MIMO.

全维MIMO(或称3D MIMO)系统通过采用三维波束赋形技术增大小区的垂直覆盖范围，从而更有效地避免小区间干扰、增加系统吞吐量、提高频谱效率。本项目拟从信道模型、信道估计、干扰协调的角度深入研究3D MIMO关键技术。具体研究内容及目标是：①揭示3D MIMO信道特征的诸多关键因素，基于多元统计方法将一般信道建模推广到三维空间域信道建模，建立3D MIMO收发理论模型；②定义一次相关性拟合和二次相关性拟合概念，针对多径衰落信道，基于函数逼近论提出信道衰落相关性矩阵系数的综合快速计算新方法，建立3D MIMO信道的近似模型；③分析信道稀疏特性，根据互相关最小化准则采用量子免疫克隆算法设计导频图案，提出基于量子免疫克隆算法的3D MIMO信道稀疏估计新方法；④采用量子菌群算法进行波束赋形权值优化、设计下倾角自适应调整算法和功率分配优化方案，提出基于量子菌群算法的3D MIMO干扰协调新方法。

本项目对3D MIMO系统的信道建模、信道估计、干扰协调等关键技术进行了深入研究，在有效避免小区间干扰、增加系统吞吐量、提高频谱效率等方面提出了新的理论模型和新的方法。主要解决的关键问题与创新点包括：.（1）针对3D MIMO信道建模问题，研究了3D MIMO信道建模方法，推导出3D MIMO信道发射端和接收端相关矩阵的闭合表达式，建立了新的适用于任意天线阵列的3D Kronecker信道模型，并进一步建立了分别基于均匀线形、圆形和矩形天线阵列的3D Kronecker信道模型；建立了3D MIMO信道的Weichselberger模型，分析了六种典型场景下该信道模型的空间耦合矩阵，提出了一种高准确度新型结构空间耦合矩阵模型；深入分析了3D MIMO系统的信道容量，基于函数逼近论提出了一种低复杂度的3D MIMO系统信道容量近似计算方法。.（2）针对3D MIMO信道估计问题，深入研究了3D MIMO信道在时域、空域和角度域的稀疏性结构，提出了①基于量子菌群算法的3D MIMO稀疏信道估计算法；②基于块结构压缩感知的3D MIMO稀疏信道BP-CoSaMP估计算法；③基于自适应滤波的3D MIMO稀疏信道估计算法；④基于天线相关性的低复杂度3D MIMO信道级联维纳滤波估计算法；⑤基于透镜的低复杂度3D毫米波大规模MIMO信道估计算法。这些方法从不同角度解决了3D MIMO信道估计问题，并有效提高了3D MIMO信道估计精度。.（3）针对3D MIMO干扰协调问题，重点研究了3D MIMO系统预编码和天线下倾角优化问题，提出了①基于3D MIMO信道发射相关和接收相关矩阵在水平维和垂直维的可分解性的有限反馈预编码方案和2种适用于3D MIMO信道的新码本；②基于导频的3D MIMO水平维、垂直维联合预编码方案；③基于CSI-RS的多数据流预编码方案；④基于统计信道信息的低复杂度3D MIMO系统多层预编码方案；⑤基于改进的量子菌群优化算法的天线下倾角优化方案。这些方法有效解决了3D MIMO系统干扰协调问题。.本项目圆满完成了预期研究，并取得了一系列国内外认可的成果，为5G移动通信的发展和广泛应用提供了理论依据和技术支持。