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arXiv 1003.4764cs.ITmath.IT
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干扰网络中基于双向训练的自适应波束形成

Adaptive Beamforming in Interference Networks via Bi-Directional Training

Changxin Shi, Randall A. Berry, Michael L. Honig

摘要:

我们研究分布式算法调整波束形成矢量和接收机滤波器在多输入多输出(MIMO)干扰网络,假设每个用户使用一个单一的波束和线性滤波器在接收机。在这样的设置中,已经研究了几种分布式算法,用于在发射机和接收机处假设完美的信道状态信息(CSI)来最大化和速率或和效用。本文的重点是研究时变信道下的自适应算法,而不需要假设发射端或接收端的信道状态信息。具体来说,我们认为最近的最大信干噪比算法的时分双工系统的自适应版本。该算法使用一段时间的双向训练,然后是一个数据传输块。使用当前波束形成器发送前向方向上的训练,并用于调整接收滤波器。使用当前接收滤波器作为波束发送反向训练,并用于调整发射波束成形器。接收滤波器和波束形成器的自适应都是使用当前块的最小二乘目标来完成的。为了提高性能时,训练数据是有限的,我们还考虑使用指数加权数据从以前的块。数值结果比较在不同的设置中的算法的性能。

英文摘要:

We study distributed algorithms for adjusting beamforming vectors and receiver filters in multiple-input multiple-output (MIMO) interference networks, with the assumption that each user uses a single beam and a linear filter at the receiver. In such a setting there have been several distributed algorithms studied for maximizing the sum-rate or sum-utility assuming perfect channel state information (CSI) at the transmitters and receivers. The focus of this paper is to study adaptive algorithms for time-varying channels, without assuming any CSI at the transmitters or receivers. Specifically, we consider an adaptive version of the recent Max-SINR algorithm for a time-division duplex system. This algorithm uses a period of bi-directional training followed by a block of data transmission. Training in the forward direction is sent using the current beam-formers and used to adapt the receive filters. Training in the reverse direction is sent using the current receive filters as beams and used to adapt the transmit beamformers. The adaptation of both receive filters and beamformers is done using a least-squares objective for the current block. In order to improve the performance when the training data is limited, we also consider using exponentially weighted data from previous blocks. Numerical results are presented that compare the performance of the algorithms in different settings.

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