On the vector broadcast channel with alternating CSIT: A topological perspective
On the vector broadcast channel with alternating CSIT: A topological perspective
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交替 CSIT 的矢量广播信道：拓扑视角
DOI：
10.1109/isit.2014.6875300
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
S. Jafar
中科院分区：
文献类型：
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作者：
Jinyuan Chen;P. Elia;S. Jafar
在许多无线网络中，链路强度受到许多拓扑因素的影响，例如不同的距离、阴影和小区间干扰，从而导致一些链路通常比其他链路更强。从信息理论的角度来看，占这样的拓扑方面仍然在很大程度上未被探索，尽管强烈的迹象表明，这些方面可以至关重要地影响收发器和反馈设计，以及整体性能。这里的工作在探索拓扑结构、反馈和性能之间的相互作用方面迈出了一步。这是针对具有随机衰落的两个用户广播信道，在存在统计上强与弱链路的简单两状态拓扑设置的情况下，以及在存在发射机处的交替信道状态信息的实际三元反馈设置（交替CSIT）的情况下完成的，其中对于每个信道实现，该CSIT可以是完美的、延迟的或不可用的。在这种情况下，工作推导出广义自由度的界限和精确的表达式，捕获性能作为反馈统计和拓扑统计的函数。结果是基于新的拓扑信号管理（TSM）计划，占拓扑结构，以充分利用反馈。这是实现不同类别的反馈机制的实际重要性，从中我们确定特定的反馈机制，最适合不同的拓扑结构。这种方法提供了关于如何将渠道学习和反馈CSIT的努力分为强链接和弱链接的进一步见解。进一步的直觉提供了可能的收益从拓扑时空多样性，其中拓扑变化的时间和用户。
In many wireless networks, link strengths are affected by many topological factors such as different distances, shadowing and inter-cell interference, thus resulting in some links being generally stronger than other links. From an information theoretic point of view, accounting for such topological aspects has remained largely unexplored, despite strong indications that such aspects can crucially affect transceiver and feedback design, as well as the overall performance. The work here takes a step in exploring this interplay between topology, feedback and performance. This is done for the two user broadcast channel with random fading, in the presence of a simple two-state topological setting of statistically strong vs. weaker links, and in the presence of a practical ternary feedback setting of alternating channel state information at the transmitter (alternating CSIT) where for each channel realization, this CSIT can be perfect, delayed, or not available. In this setting, the work derives generalized degrees-of-freedom bounds and exact expressions, that capture performance as a function of feedback statistics and topology statistics. The results are based on novel topological signal management (TSM) schemes that account for topology in order to fully utilize feedback. This is achieved for different classes of feedback mechanisms of practical importance, from which we identify specific feedback mechanisms that are best suited for different topologies. This approach offers further insight on how to split the effort - of channel learning and feeding back CSIT - for the strong versus for the weaker link. Further intuition is provided on the possible gains from topological spatio-temporal diversity, where topology changes in time and across users.