Beyond 5G: Leveraging Cell Free TDD Massive MIMO using Cascaded Deep learning
It addresses calibration challenges in 5G+ wireless networks for improved system efficiency, but is incremental as it builds on existing deep learning and MIMO techniques.
This paper tackles the calibration of TDD reciprocity in Cell Free Massive MIMO systems, where RF chain responses cause non-reciprocity, and proposes a cascaded deep learning method to estimate downlink channels from uplink pilots, achieving scalable performance without antenna cooperation.
This paper 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.