  With the large number of antennas and subcarriers the overhead due to pilot
transmission for channel estimation can be prohibitive in wideband massive
multiple-input multiple-output (MIMO) systems. This can degrade the overall
spectral efficiency significantly, and as a result, curtail the potential
benefits of massive MIMO. In this paper, we propose a neural network (NN)-based
joint pilot design and downlink channel estimation scheme for frequency
division duplex (FDD) MIMO orthogonal frequency division multiplex (OFDM)
systems. The proposed NN architecture uses fully connected layers for
frequency-aware pilot design, and outperforms linear minimum mean square error
(LMMSE) estimation by exploiting inherent correlations in MIMO channel matrices
utilizing convolutional NN layers. Our proposed NN architecture uses a
non-local attention module to learn longer range correlations in the channel
matrix to further improve the channel estimation performance. We also propose
an effective pilot reduction technique by gradually pruning less significant
neurons from the dense NN layers during training. This constitutes a novel
application of NN pruning to reduce the pilot transmission overhead. Our
pruning-based pilot reduction technique reduces the overhead by allocating
pilots across subcarriers non-uniformly and exploiting the inter-frequency and
inter-antenna correlations in the channel matrix efficiently through
convolutional layers and attention module.
