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

TL;DR

This paper introduces a deep learning-based method to calibrate reciprocity in Cell Free TDD Massive MIMO systems, enabling accurate downlink channel estimation from limited uplink data without antenna cooperation.

Contribution

It proposes a cascaded deep neural network approach to estimate downlink channels across all subcarriers from uplink pilot data, eliminating the need for antenna cooperation calibration.

Findings

Effective downlink channel estimation from limited uplink data.

Scalable deep learning method for reciprocity calibration.

Reduces dependency on antenna cooperation in Cell Free Massive MIMO.

Abstract

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.