# Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium Propagation

arXiv: https://arxiv.org/abs/2110.00414 · HTML: https://mlsanity.com/paper/2110.00414/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 1 Oct 2021.

## Builds on (most cited first, 15 of 15)

- [Meta-Learning with Implicit Gradients](https://mlsanity.com/paper/1909.04630/) (2019)
- [Optimizing Millions of Hyperparameters by Implicit Differentiation](https://mlsanity.com/paper/1911.02590/) (2019)
- [Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagation](https://mlsanity.com/paper/1602.05179/) (2016)
- [Information-Theoretic Generalization Bounds for Meta-Learning and Applications](https://mlsanity.com/paper/2005.04372/) (2020)
- [Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning](https://mlsanity.com/paper/1908.09049/) (2019)
- [Massive MIMO Channel Prediction: Kalman Filtering vs. Machine Learning](https://mlsanity.com/paper/2009.09967/) (2020)
- [A Brief Introduction to Machine Learning for Engineers](https://mlsanity.com/paper/1709.02840/) (2017)
- [A contrastive rule for meta-learning](https://mlsanity.com/paper/2104.01677/) (2021)
- [Meta-Learning to Communicate: Fast End-to-End Training for Fading Channels](https://mlsanity.com/paper/1910.09945/) (2019)
- [MIND: Model Independent Neural Decoder](https://mlsanity.com/paper/1903.02268/) (2019)
- [Deep HyperNetwork-Based MIMO Detection](https://mlsanity.com/paper/2002.02750/) (2020)
- [End-to-End Fast Training of Communication Links Without a Channel Model via Online Meta-Learning](https://mlsanity.com/paper/2003.01479/) (2020)
- [Embedding Model Based Fast Meta Learning for Downlink Beamforming Adaptation](https://mlsanity.com/paper/2109.09086/) (2021)
- [Meta-ViterbiNet: Online Meta-Learned Viterbi Equalization for Non-Stationary Channels](https://mlsanity.com/paper/2103.13483/) (2021)
- [Predictive Quality of Service (PQoS): The Next Frontier for Fully Autonomous Systems](https://mlsanity.com/paper/2109.09376/) (2021)

## Built on by (most cited first, 4 of 4)

- [An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learning](https://mlsanity.com/paper/2308.00788/) (2023)
- [Energy-based learning algorithms for analog computing: a comparative study](https://mlsanity.com/paper/2312.15103/) (2023)
- [Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark](https://mlsanity.com/paper/2312.09857/) (2023)
- [Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition](https://mlsanity.com/paper/2203.12715/) (2022)

## Abstract (arXiv)

Predicting fading channels is a classical problem with a vast array of applications, including as an enabler of artificial intelligence (AI)-based proactive resource allocation for cellular networks. Under the assumption that the fading channel follows a stationary complex Gaussian process, as for Rayleigh and Rician fading models, the optimal predictor is linear, and it can be directly computed from the Doppler spectrum via standard linear minimum mean squared error (LMMSE) estimation. However, in practice, the Doppler spectrum is unknown, and the predictor has only access to a limited time series of estimated channels. This paper proposes to leverage meta-learning in order to mitigate the requirements in terms of training data for channel fading prediction. Specifically, it first develops an offline low-complexity solution based on linear filtering via a meta-trained quadratic regularization. Then, an online method is proposed based on gradient descent and equilibrium propagation (EP). Numerical results demonstrate the advantages of the proposed approach, showing its capacity to approach the genie-aided LMMSE solution with a small number of training data points.