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In frequency-division duplexing (FDD) massive multiple-input multiple-output(MIMO) systems, downlink channel state information (CSI) needs to be sent backto the base station (BS) by the users, which causes prohibitive feedbackoverhead. In this paper, we propose a lightweight and flexible deeplearning-based CSI feedback approach by capitalizing on deep equilibriummodels. Different from existing deep learning-based methods that stack multipleexplicit layers, we propose an implicit equilibrium block to mimic the behaviorof an infinite-depth neural network. In particular, the implicit equilibriumblock is defined by a fixed-point iteration and the trainable parameters indifferent iterations are shared, which results in a lightweight model.Furthermore, the number of forward iterations can be adjusted according tousers' computation capability, enabling a flexible accuracy-efficiencytrade-off. Simulation results will show that the proposed design obtains acomparable performance as the benchmarks but with much-reduced complexity andpermits an accuracy-efficiency trade-off at runtime.

Lightweight and Flexible Deep Equilibrium Learning for CSI Feedback in FDD Massive MIMO