In this letter, for outage performance orientated large intelligent surfaces (LISs)-assisted point to point wireless systems with severely blocked direct link and Rayleigh fading channels,we first propose a jointly interleaved training and transmission design. Then a semi-closed form expression is derived for the average training overhead. And it is shown to be upper bounded by the minimum between the LIS size and a value explicitly dependent on the target receiver signal-to-noise-ratio (SNR). The upper bound gives the condition on the target SNR for achieving overhead saving compared to the full CSI scheme. And the overhead saving increases linearlywith the LIS size for constant target SNR. Non-negligible overhead saving is still available even though one increases the target SNR with larger LIS, e.g., as the square of the LIS size for fully exploiting the beamforming gain. Finally, we indicate the impact of practical phase quantization on the training and feedback overhead. Simulations verify these results and show that the proposed scheme can significantly reduce the training overhead without performance loss compared to the full CSI scheme.

中文翻译：

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智能表面辅助无线通信的交错训练

在这封信中，对于具有严重阻塞的直接链路和瑞利衰落信道的面向中断性能的大型智能表面 (LIS) 辅助点对点无线系统，我们首先提出了联合交错训练和传输设计。然后推导出平均训练开销的半封闭形式表达式。并且它显示为 LIS 大小和明确依赖于目标接收器信噪比 (SNR) 的值之间的最小值的上限。上限给出了与完整 CSI 方案相比实现开销节省的目标 SNR 的条件。对于恒定目标 SNR，开销节省随着 LIS 大小线性增加。即使使用更大的 LIS 增加目标 SNR，仍然可以使用不可忽略的开销节省，例如，作为充分利用波束形成增益的 LIS 大小的平方。最后，我们指出了实际相位量化对训练和反馈开销的影响。仿真验证了这些结果，并表明与完整的 CSI 方案相比，所提出的方案可以显着减少训练开销而不会造成性能损失。