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

arXiv: https://arxiv.org/abs/2211.15079 · HTML: https://mlsanity.com/paper/2211.15079/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 18 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Universal Transformers](https://mlsanity.com/paper/1807.03819/) (2018)
- [Deep Equilibrium Models](https://mlsanity.com/paper/1909.01377/) (2019)
- [Multiscale Deep Equilibrium Models](https://mlsanity.com/paper/2006.08656/) (2020)
- [JFB: Jacobian-Free Backpropagation for Implicit Networks](https://mlsanity.com/paper/2103.12803/) (2021)
- [AI Benchmark: Running Deep Neural Networks on Android Smartphones](https://mlsanity.com/paper/1810.01109/) (2018)
- [Neural Network Inference on Mobile SoCs](https://mlsanity.com/paper/1908.11450/) (2019)
- [Five Disruptive Technology Directions for 5G](https://mlsanity.com/paper/1312.0229/) (2013)
- [Lightweight Convolutional Neural Networks for CSI Feedback in Massive MIMO](https://mlsanity.com/paper/2005.00438/) (2020)
- [Dilated Convolution based CSI Feedback Compression for Massive MIMO Systems](https://mlsanity.com/paper/2106.04043/) (2021)
- [Hybrid Far- and Near-Field Channel Estimation for THz Ultra-Massive MIMO via Fixed Point Networks](https://mlsanity.com/paper/2205.04944/) (2022)

## Abstract (arXiv)

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