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Combining Training and Quantized Feedback in Multiantenna Reciprocal Channels

机译:在多天线互惠信道中结合训练和量化反馈

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摘要

The communication between a multiple-antenna transmitter and multiple receivers (users) with either a single or multiple-antenna each can be significantly enhanced by providing the channel state information at the transmitter (CSIT) of the users, as this allows for scheduling, beamforming and multiuser multiplexing gains. The traditional view on how to enable CSIT has been as follows: In time-division duplexed (TDD) systems, uplink (UL) and downlink (DL) channel reciprocity allows the use of a training sequence in the UL direction, which is exploited to obtain an UL channel estimate. This estimate is in turn recycled in the next downlink transmission. In frequency-division duplexed (FDD) systems, which lack the UL and DL reciprocity, the CSIT is provided via the use of a dedicated feedback link of limited capacity between the receivers and the transmitter. In this paper, we focus on TDD systems and put their classical approach in question. We show that the traditional TDD setup above fails to fully exploit the channel reciprocity in its true sense. In fact, we show that the system can benefit from a combined CSIT acquisition strategy mixing the use of limited feedback and that of a training sequence. This combining gives rise to a very interesting joint estimation and detection problem for which we propose two iterative algorithms. An outage rate based framework is also developed which gives the resource split between training and feedback. We demonstrate the potential of this hybrid combining in terms of the improved CSIT quality under a global training and feedback resource constraint.
机译:通过在用户的发射器(CSIT)提供信道状态信息,可以显着增强多天线发射器与具有单个或多个天线的多个接收器(用户)之间的通信,因为这样可以进行调度,波束成形和多用户复用增益。关于如何启用CSIT的传统观点如下:在时分双工(TDD)系统中,上行链路(UL)和下行链路(DL)信道互易性允许在UL方向上使用训练序列,该序列可用于获得UL信道估计。该估计值又在下一次下行链路传输中循环使用。在缺少UL和DL互易性的频分双工(FDD)系统中,通过使用接收器和发送器之间容量有限的专用反馈链路来提供CSIT。在本文中,我们将重点放在TDD系统上,并对它们的经典方法提出了疑问。我们表明,上面的传统TDD设置无法完全利用其真实意义上的通道互惠性。实际上,我们表明该系统可以从混合使用有限反馈和训练序列的CSIT采集策略中受益。这种结合引起了一个非常有趣的联合估计和检测问题,为此我们提出了两种迭代算法。还开发了基于中断率的框架,该框架在培训和反馈之间分配了资源。我们在全球培训和反馈资源约束下,根据改进的CSIT质量论证了这种混合组合的潜力。

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