  Massive multiple-input multiple-output (mMIMO) regime reaps the benefits of
spatial diversity and multiplexing gains, subject to precise channel state
information (CSI) acquisition. In the current communication architecture, the
downlink CSI is estimated by the user equipment (UE) via dedicated pilots and
then fed back to the gNodeB (gNB). The feedback information is compressed with
the goal of reducing over-the-air overhead. This compression increases the
inaccuracy of acquired CSI, thus degrading the overall spectral efficiency.
This paper proposes a computationally inexpensive machine learning (ML)-based
CSI feedback algorithm, which exploits twin channel predictors. The proposed
approach can work for both time-division duplex (TDD) and frequency-division
duplex (FDD) systems, and it allows to reduce feedback overhead and improves
the acquired CSI accuracy. To observe real benefits, we demonstrate the
performance of the proposed approach using the empirical data recorded at the
Nokia campus in Stuttgart, Germany. Numerical results show the effectiveness of
the proposed approach in terms of reducing overhead, minimizing quantization
errors, increasing spectral efficiency, cosine similarity, and precoding gain
compared to the traditional CSI feedback mechanism.
