  How to reduce the pilot overhead required for channel estimation? How to deal
with the channel dynamic changes and error propagation in channel prediction?
To jointly address these two critical issues in next-generation transceiver
design, in this paper, we propose a novel framework named channel deduction for
high-dimensional channel acquisition in multiple-input multiple-output
(MIMO)-orthogonal frequency division multiplexing (OFDM) systems. Specifically,
it makes use of the outdated channel information of past time slots, performs
coarse estimation for the current channel with a relatively small number of
pilots, and then fuses these two information to obtain a complete
representation of the present channel. The rationale is to align the current
channel representation to both the latent channel features within the past
samples and the coarse estimate of current channel at the pilots, which, in a
sense, behaves as a complementary combination of estimation and prediction and
thus reduces the overall overhead. To fully exploit the highly nonlinear
correlations in time, space, and frequency domains, we resort to learning-based
implementation approaches. By using the highly efficient complex-domain
multilayer perceptron (MLP)-mixer for crossing space-frequency domain
representation and the recurrence-based or attention-based mechanisms for the
past-present interaction, we respectively design two different channel
deduction neural networks (CDNets). We provide a general procedure of data
collection, training, and deployment to standardize the application of CDNets.
Comprehensive experimental evaluations in accuracy, robustness, and efficiency
demonstrate the superiority of the proposed approach, which reduces the pilot
overhead by up to 88.9% compared to state-of-the-art estimation approaches and
enables continuous operating even under unknown user movement and error
propagation.
