【作者】 李宁；

【导师】 周小平；

【作者基本信息】 上海师范大学 ， 电子与通信工程专业（专业学位）， 2019， 硕士

【摘要】 通信行业的发展可谓在引领时代的发展,而5G网络的发展占据着至关重要的地位,伴随着大量资源的投入,5G网络的运用得到飞速发展。5G在运用的过程中,具有明显的优势,比如,减低端到端延迟;整体网络速率得到极大的提升。那么5G给我们带来的是超越光纤的传输速率,超越工业总线的实时能力以及全空间的连接。移动网络正在使得全行业数字化,成为基础的生产力。为了提升网络速率,降低时延,提高用户服务质量,无线网络中小区密度不断增加,小区半径不断缩小。因此,在未来的5G研究中,将small cell密集密集部署在大规模MIMO(Multiple Input Multiple Output,MIMO)系统构成大规模MIMO双层系统,可以解决未来无线通信系统发展中面临的问题。其具体内容和结论如下:随着高速无线数据接入业务与用户数量的迅速增长,对于超密集大规模MIMO双层系统中宏基站的部署和小蜂窝基站的放置,研究一种适合实际场景的聚类方案,从而在保证一定的通信质量下提高系统的吞吐量。首先,针对高密度用户大规模MIMO双层系统的场景,本文利用角度和距离为相似性度量的特性提出一种基于局部鉴别投影的用户分组算法,提高了用户分组效果,降低了高维信道矩阵计算复杂度,减少了小区间的干扰。通过仿真结果表明本文所提的算法提高系统容量和吞吐量。其次,为了降低用户组间干扰和用户间干扰,本文提出一种双层最小均方误差(Minimum Mean Squareerror Estimation,MMSE)预编码。其外层预编码其利用统计信道状态信息(Channel State Information,CSI)来降低用户组间和层间干扰,而内部预编码其利用瞬时有效信道信息来减轻用户组内干扰。通过仿真分析可以看出本文所提的双层MMSE预编码性能优于其他预编码方案。最后,针对大规模MIMO双层系统中的复杂的干扰信道的信道状态信息获取,本文提出一种三阶段数据辅助信道估计方法。为了实现数据辅助方案,假设系统中没有错误且无延迟,如果在小小区基站完成数据检测和解码数据序列通过有线回程发送到宏基站。由于宏基站在用户分组后的信道具有稀疏性,从而在宏基站处的下行链路信道利用解码后的上行链路数据和已知的训练序列情况下,提出一种基于最优块正交匹配追踪(Optimal BlockOrthogonal Matching Pursuit algorithm,OBOMP)的信道估计算法。通过仿真结果表明本文所提数据辅助方法能有效地提高信道的估计精度。更多还原

【Abstract】 The development of the communication industry can be said to lead the development of the times,and the development of 5G networks occupies a vital position.With the investment of a large number of resources,the use of 5G networks has developed rapidly.In the process of using 5G,5G has obvious advantages,such as reducing end-to-end delay;the overall network speed is greatly improved.Then 5G brings us the transmission rate beyond the fiber,beyond the real-time capabilities of the industrial bus and the connection of the whole space.Mobile networks are digitizing the entire industry and becoming the underlying productivity.In order to improve the network speed,reduce the delay,and improve the user service quality,the cell density in the wireless network is continuously increasing,and the cell radius is continuously reduced.Therefore,in the future 5G research,small cells are densely and densely deployed in a massive Multiple Input Multiple Output(MIMO)system to form a massive MIMO two-tier system,which can solve the problems faced in the future development of wireless communication systems.The specific content and conclusions are as follows:With the rapid growth of high-speed wireless data access services and users,for the deployment of macro base stations and the placement of small cell base stations in ultra-dense massive MIMO two-tier systems,a clustering scheme suitable for actual scenarios is studied,thus ensuring improve the throughput of the system with a certain communication quality.Firstly,for the scenario of massive MIMO two-tier systems for high-density users,this paper proposes a user grouping algorithm based on local discriminant projection using angle and distance as the characteristics of similarity measure.The algorithm improves the user grouping effect,reduces the computational complexity of the high-dimensional channel matrix,and reduces the cell-interference.The simulation results show that the proposed algorithm improves system capacity and throughput.Secondly,in order to reduce user inter-group interference and inter-user interference,this paper proposes a minimum Mean Square Mean Square Error Estimation(MMSE)precoding.Its outer precoding uses statistical channel state information(CSI)to reduce inter-group and inter-layer interference,while internal precoding uses instantaneous effective channel information to mitigate intra-group interference.The simulation analysis shows that the proposed two-layer MMSE precoding performance is better than other precoding schemes.Finally,for the acquisition of channel state information of complex interference channels in massive MIMO two-tier systems,this paper proposes a three-stage data-assisted channel estimation method.In order to implement the data assist scheme,it is assumed that there is no error in the system and no delay,if the data detection and decoding data sequence is completed in the small cell base station and transmitted to the macro base station through the wired backhaul.Since the channel of the macro base station after user grouping is sparse,and thus the downlink channel at the macro base station utilizes the decoded uplink data and the known training sequence,an optimal block orthogonal matching is proposed.Channel estimation algorithm of Optimal Block Orthogonal Matching Pursuit algorithm(OBOMP).The simulation results show that the data assisted method proposed in this paper can effectively improve the channel estimation accuracy.更多还原