论文标题
FDD大规模MIMO中的渠道状态获取：“模拟”反馈的率延伸和有效性
Channel State Acquisition in FDD Massive MIMO: Rate-Distortion Bound and Effectiveness of "Analog" Feedback
论文作者
论文摘要
我们考虑了通过下行链路（DL）训练和上行链路（UL）反馈在宽带FDD大量MIMO系统中估算通道褪色系数（以相关的高斯向量）的估计问题。使用速率延伸理论，我们根据DL（$β_{TR} $）中的训练飞行员数量和UL（$β_{FB} $）的反馈维度的训练飞行员数量以及随机，随机，空间同位素型试验器的反馈维度来得出最佳的界限。结果表明，当培训飞行员的数量超过频道协方差等级（$ r $）时，最佳的利率 - 延迟反馈策略在估计通道状态下达到了$θ（snr^{ - α}）$的估计错误衰减，其中$α= min = min = min = min（β_{fb}/r，1）$是$ scaling scaledennect。我们还讨论了一种“模拟”反馈策略，表明它可以实现最佳的质量缩放指数，以实现广泛的培训和反馈维度，而没有通道协方差知识和用户方面的简单信号处理。我们的发现得到了数值模拟的支持，该数值模拟比较了通道状态均衡误差和DL中可实现的Ergodic Sum-rate的各种策略，并具有零效的预编码。
We consider the problem of estimating channel fading coefficients (modeled as a correlated Gaussian vector) via Downlink (DL) training and Uplink (UL) feedback in wideband FDD massive MIMO systems. Using rate-distortion theory, we derive optimal bounds on the achievable channel state estimation error in terms of the number of training pilots in DL ($β_{tr}$) and feedback dimension in UL ($β_{fb}$), with random, spatially isotropic pilots. It is shown that when the number of training pilots exceeds the channel covariance rank ($r$), the optimal rate-distortion feedback strategy achieves an estimation error decay of $Θ(SNR^{-α})$ in estimating the channel state, where $α= min (β_{fb}/r , 1)$ is the so-called quality scaling exponent. We also discuss an "analog" feedback strategy, showing that it can achieve the optimal quality scaling exponent for a wide range of training and feedback dimensions with no channel covariance knowledge and simple signal processing at the user side. Our findings are supported by numerical simulations comparing various strategies in terms of channel state mean squared error and achievable ergodic sum-rate in DL with zero-forcing precoding.
