This letter deals with the calibration of Time Division Duplexing (TDD) reciprocity in an Orthogonal Frequency Division Multiplexing (OFDM) based Cell Free Massive MIMO system where the responses of the (Radio Frequency) RF chains render the end to end channel non-reciprocal, even though the physical wireless channel is reciprocal. We further address the non-availability of the uplink channel estimates at locations other than pilot subcarriers and propose a single-shot solution to estimate the downlink channel at all subcarriers from the uplink channel at selected pilot subcarriers. We propose a cascade of two Deep Neural Networks (DNN) to achieve the objective. The proposed method is easily scalable and removes the need for relative reciprocity calibration based on the cooperation of antennas, which usually introduces dependency in Cell Free Massive MIMO systems.

中文翻译：

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超越 5G：使用级联深度学习利用 Cell Free TDD Massive MIMO

这封信涉及在基于正交频分复用 (OFDM) 的无蜂窝大规模 MIMO 系统中时分双工 (TDD) 互易性的校准，其中（射频）RF 链的响应使端到端信道非互易，即使物理无线信道是互惠的。我们进一步解决了在导频子载波以外的位置上行链路信道估计不可用的问题，并提出了一种单次解决方案，以从选定导频子载波上的上行链路信道估计所有子载波上的下行链路信道。我们提出了两个深度神经网络 (DNN) 的级联来实现目标。所提出的方法易于扩展，并且无需基于天线的协作进行相对互易校准，