# Predicting Multi-Antenna Frequency-Selective Channels via Meta-Learned Linear Filters based on Long-Short Term Channel Decomposition

arXiv: https://arxiv.org/abs/2203.12715 · HTML: https://mlsanity.com/paper/2203.12715/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 59 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 23 of 23)

- [Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML](https://mlsanity.com/paper/1909.09157/) (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)
- [Tensor Decomposition for Signal Processing and Machine Learning](https://mlsanity.com/paper/1607.01668/) (2016)
- [Meta-Learning without Memorization](https://mlsanity.com/paper/1912.03820/) (2019)
- [Distributed Multi-agent Meta Learning for Trajectory Design in Wireless Drone Networks](https://mlsanity.com/paper/2012.03158/) (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)
- [Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems](https://mlsanity.com/paper/1912.12265/) (2019)
- [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)
- [Information-Theoretic Analysis of Epistemic Uncertainty in Bayesian Meta-learning](https://mlsanity.com/paper/2106.00252/) (2021)
- [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)
- [Neural Augmentation of Kalman Filter with Hypernetwork for Channel Tracking](https://mlsanity.com/paper/2109.12561/) (2021)
- [Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium Propagation](https://mlsanity.com/paper/2110.00414/) (2021)
- [Learning to Broadcast for Ultra-Reliable Communication with Differential Quality of Service via the Conditional Value at Risk](https://mlsanity.com/paper/2112.02007/) (2021)
- [Predicting Future CSI Feedback For Highly-Mobile Massive MIMO Systems](https://mlsanity.com/paper/2202.02492/) (2022)
- [Machine Learning Prediction of Time-Varying Rayleigh Channels](https://mlsanity.com/paper/2103.06131/) (2021)

## Built on by (most cited first, 2 of 2)

- [Deep Unsupervised Domain Adaptation for Time Series Classification: a Benchmark](https://mlsanity.com/paper/2312.09857/) (2023)
- [Reverse Ordering Techniques for Attention-Based Channel Prediction](https://mlsanity.com/paper/2302.00341/) (2023)

## Abstract (arXiv)

An efficient data-driven prediction strategy for multi-antenna frequency-selective channels must operate based on a small number of pilot symbols. This paper proposes novel channel prediction algorithms that address this goal by integrating transfer and meta-learning with a reduced-rank parametrization of the channel. The proposed methods optimize linear predictors by utilizing data from previous frames, which are generally characterized by distinct propagation characteristics, in order to enable fast training on the time slots of the current frame. The proposed predictors rely on a novel long-short-term decomposition (LSTD) of the linear prediction model that leverages the disaggregation of the channel into long-term space-time signatures and fading amplitudes. We first develop predictors for single-antenna frequency-flat channels based on transfer/meta-learned quadratic regularization. Then, we introduce transfer and meta-learning algorithms for LSTD-based prediction models that build on equilibrium propagation (EP) and alternating least squares (ALS). Numerical results under the 3GPP 5G standard channel model demonstrate the impact of transfer and meta-learning on reducing the number of pilots for channel prediction, as well as the merits of the proposed LSTD parametrization.