期刊文献+
共找到1篇文章
< 1 >
每页显示 20 50 100
Performance Analysis of Sparse Array based Massive MIMO via Joint Convex Optimization 被引量:2
1
作者 Mengting Lou Jing Jin +5 位作者 Hanning Wang Dan Wu Liang Xia Qixing Wang Yifei Yuan Jiangzhou Wang 《China Communications》 SCIE CSCD 2022年第3期88-100,共13页
Massive multiple-input multiple-output(MIMO)technology enables higher data rate transmission in the future mobile communications.However,exploiting a large number of antenna elements at base station(BS)makes effective... Massive multiple-input multiple-output(MIMO)technology enables higher data rate transmission in the future mobile communications.However,exploiting a large number of antenna elements at base station(BS)makes effective implementation of massive MIMO challenging,due to the size and weight limits of the masssive MIMO that are located on each BS.Therefore,in order to miniaturize the massive MIMO,it is crucial to reduce the number of antenna elements via effective methods such as sparse array synthesis.In this paper,a multiple-pattern synthesis is considered towards convex optimization(CO).The joint convex optimization(JCO)based synthesis is proposed to construct a codebook for beamforming.Then,a criterion containing multiple constraints is developed,in which the sparse array is required to fullfill all constraints.Finally,extensive evaluations are performed under realistic simulation settings.The results show that with the same number of antenna elements,sparse array using the proposed JCO-based synthesis outperforms not only the uniform array,but also the sparse array with the existing CO-based synthesis method.Furthermore,with a half of the number of antenna elements that on the uniform array,the performance of the JCO-based sparse array approaches to that of the uniform array. 展开更多
关键词 B5G 6G sparse array joint convex optimization massive MIMO system-level simulation
下载PDF
上一页 1 下一页 到第
使用帮助 返回顶部