Beyond 5G: Leveraging Cell Free TDD Massive MIMO using Cascaded Deep learning

Cell Free Massive MIMO is a solution for improving the spectral efficiency of next generation communication systems and a crucial aspect for realizing the gains of the technology is the availability of accurate Channel State Information (CSI). Time Division Duplexing (TDD) mode is popular for Cell Free Massive MIMO since the physical wireless channel's assumed reciprocity facilitates channel estimation. However, the availability of accurate CSI in the TDD mode is hindered by the non reciprocity of the end to end channel, due to the presence of RF components, as well as the non availability of CSI in the subcarriers that do not have reference signals. Hence, the prediction of the Downlink CSI in the subcarriers without reference signals becomes an even more complicated problem. In this work, we consider TDD non-reciprocity with limited availability of resource elements for CSI estimation and propose a deep learning based approach using cascaded Deep Neural Networks (DNNs) to attain a one shot prediction of the reverse channel across the entire bandwidth. The proposed method is able to estimate downlink CSI at all subcarriers from the uplink CSI at selected subcarriers and hence does not require downlink CSI feedback.