标题： 半盲信道估计在大规模MIMO系统中的克拉默-拉奥下界的基本限制 显示英文标题

标题： Fundamental limits via CRB of semi-blind channel estimation in Massive MIMO systems

摘要： 本文研究了大规模多输入多输出（MIMO）系统中半盲信道估计的确定性和随机Cramér-Rao界（CRB）的渐近行为。 我们推导并分析了在各种渐近条件下两种度量的数学上易于处理的表达式，这些条件决定了天线数量、用户数量、训练序列长度和传输块长度的增长速率。 与现有工作不同，我们的结果表明，当传输块长度增加时，CRB可以变得任意小，但仅当训练序列长度以相同速率增长且用户数量保持固定时才成立。 然而，如果训练序列数量与用户数量成比例，则信道估计误差始终由一个非消失常数下限限制。 数值结果用于支持我们的发现，并展示了半盲信道估计在减少所需训练序列数量方面的优势。 显示英文摘要

摘要： This paper investigates the asymptotic behavior of the deterministic and stochastic Cram\'er-Rao Bounds (CRB) for semi-blind channel estimation in massive multiple-input multiple-output (MIMO) systems. We derive and analyze mathematically tractable expressions for both metrics under various asymptotic regimes, which govern the growth rates of the number of antennas, the number of users, the training sequence length, and the transmission block length. Unlike the existing work, our results show that the CRB can be made arbitrarily small as the transmission block length increases, but only when the training sequence length grows at the same rate and the number of users remains fixed. However, if the number of training sequences remains proportional to the number of users, the channel estimation error is always lower-bounded by a non-vanishing constant. Numerical results are presented to support our findings and demonstrate the advantages of semi-blind channel estimation in reducing the required number of training sequences.