  Forward channel state information (CSI) often plays a vital role in
scheduling and capacity-approaching transmission optimization for massive
multiple-input multiple-output (MIMO) communication systems. In frequency
division duplex (FDD) massive MIMO systems, forwardlink CSI reconstruction at
the transmitter relies critically on CSI feedback from receiving nodes and must
carefully weigh the tradeoff between reconstruction accuracy and feedback
bandwidth. Recent studies on the use of recurrent neural networks (RNNs) have
demonstrated strong promises, though the cost of computation and memory remains
high, for massive MIMO deployment. In this work, we exploit channel coherence
in time to substantially improve the feedback efficiency. Using a Markovian
model, we develop a deep convolutional neural network (CNN)-based framework
MarkovNet to differentially encode forward CSI in time to effectively improve
reconstruction accuracy. Furthermore, we explore important physical insights,
including spherical normalization of input data and convolutional layers for
feedback compression. We demonstrate substantial performance improvement and
complexity reduction over the RNN-based work by our proposed MarkovNet to
recover forward CSI estimates accurately. We explore additional practical
consideration in feedback quantization, and show that MarkovNet outperforms
RNN-based CSI estimation networks at a fraction of the computational cost.
