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Predictive Coding of Bit Loading for Time-Correlated MIMO Channels With a Decision Feedback Receiver

机译:具有决策反馈接收器的与时间相关的MIMO信道的比特加载的预测编码

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In this paper, we consider variable-rate transmission over a slowly varying multiple-input multiple-output (MIMO) channel with a decision feedback receiver. The transmission rate is adapted to the channel by dynamically assigning bits to the subchannels of the MIMO system. Predictive quantization is used for the feedback of bit loading to take advantage of the time correlation inherited from the temporally correlated channel. Due to the use of decision feedback at the receiver, the bit loading is related to the Cholesky decomposition of the channel Gram matrix. Assuming the channel is modeled by a slowly varying Gauss–Markov process, we show that the nested submatrices generated during the process of Cholesky decomposition can be updated as time evolves. Based on the update, we derive the optimal predictor of the next bit loading for predictive quantization. Furthermore, we derive the statistics of the prediction error, which are then exploited to design the quantizer to achieve a smaller quantization error. Simulations are given to demonstrate that the proposed predictive quantization gives a good approximation of the desired transmission rate with a low feedback rate.
机译:在本文中,我们考虑在具有决策反馈接收器的缓慢变化的多输入多输出(MIMO)信道上进行可变速率传输。通过将比特动态分配给MIMO系统的子信道,可以使传输速率适应信道。预测量化用于位负载的反馈,以利用从时间相关通道继承的时间相关性。由于在接收器上使用了决策反馈,因此位负载与信道Gram矩阵的Cholesky分解有关。假设通道是通过缓慢变化的高斯-马尔可夫过程建模的,我们表明在Cholesky分解过程中生成的嵌套子矩阵可以随着时间的变化而更新。基于此更新,我们可以得出下一个比特加载的最佳预测器,以进行预测量化。此外,我们导出了预测误差的统计信息,然后将其用于设计量化器以实现较小的量化误差。通过仿真可以证明所提出的预测量化能够以较低的反馈率很好地逼近所需的传输率。

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