In frequency-division duplexing (FDD) massive multiple-input multiple-output
(MIMO) systems, downlink channel state information (CSI) needs to be sent from
users back to the base station (BS), which causes prohibitive feedback
overhead. In this paper, we propose a lightweight and adaptive deep
learning-based CSI feedback scheme by capitalizing on deep equilibrium models.
Different from existing deep learning-based approaches that stack multiple
explicit layers, we propose an implicit equilibrium block to mimic the process
of an infinite-depth neural network. In particular, the implicit equilibrium
block is defined by a fixed-point iteration and the trainable parameters in
each iteration are shared, which results in a lightweight model. Furthermore,
the number of forward iterations can be adjusted according to the users'
computational capability, achieving an online accuracy-efficiency trade-off.
Simulation results will show that the proposed method obtains a comparable
performance as the existing benchmarks but with much-reduced complexity and
permits an accuracy-efficiency trade-off at runtime.Comment: submitted to IEEE for possible publicatio