美国康普推出新型无线基础设施解决方案
近日，美国康普旗下的企业解决方案部门宣布推出新型Wired for Wireless^TM解决方案，该解决方案有助于在建设阶段即消除室内无线通信的信号盲区或死区，为新建筑和园区业主及开发商提前布局提...美国康普推出新型无线基础设施解决方案
近日，美国康普旗下的企业解决方案部门宣布推出新型Wired for Wireless^TM解决方案，该解决方案有助于在建设阶段即消除室内无线通信的信号盲区或死区，为新建筑和园区业主及开发商提前布局提供必要的基础设施。展开更多
It is extensively approved that Channel State Information(CSI) plays an important role for synergetic transmission and interference management. However, pilot overhead to obtain CSI with enough precision is a signific...It is extensively approved that Channel State Information(CSI) plays an important role for synergetic transmission and interference management. However, pilot overhead to obtain CSI with enough precision is a significant issue for wireless communication networks with massive antennas and ultra-dense cell. This paper proposes a learning- based channel model, which can estimate, refine, and manage CSI for a synergetic transmission system. It decomposes the channel impulse response into multiple paths, and uses a learning-based algorithm to estimate paths' parameters without notable degradation caused by sparse pilots. Both indoor measurement and outdoor measurement are conducted to verify the feasibility of the proposed channel model preliminarily.展开更多
基金supported by National Basic Research Program of China (NO 2012CB316002)China’s 863 Project (NO 2014AA01A703)+2 种基金National Major Projec (NO. 2014ZX03003002-002)Program for New Century Excellent Talents in University (NCET-13-0321)Tsinghua University Initiative Scientific Research Program (2011THZ02-2)
文摘It is extensively approved that Channel State Information(CSI) plays an important role for synergetic transmission and interference management. However, pilot overhead to obtain CSI with enough precision is a significant issue for wireless communication networks with massive antennas and ultra-dense cell. This paper proposes a learning- based channel model, which can estimate, refine, and manage CSI for a synergetic transmission system. It decomposes the channel impulse response into multiple paths, and uses a learning-based algorithm to estimate paths' parameters without notable degradation caused by sparse pilots. Both indoor measurement and outdoor measurement are conducted to verify the feasibility of the proposed channel model preliminarily.
