# Online Optimization for Learning to Communicate over Time-Correlated Channels

arXiv: https://arxiv.org/abs/2409.00575 · HTML: https://mlsanity.com/paper/2409.00575/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 23 of its 61 references that are in the map.
- First posted on arXiv 1 Sep 2024.

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

- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [More Adaptive Algorithms for Adversarial Bandits](https://mlsanity.com/paper/1801.03265/) (2018)
- [Deep Learning-Based Channel Estimation](https://mlsanity.com/paper/1810.05893/) (2018)
- [Deep Learning Methods for Improved Decoding of Linear Codes](https://mlsanity.com/paper/1706.07043/) (2017)
- [Hyper-Graph-Network Decoders for Block Codes](https://mlsanity.com/paper/1909.09036/) (2019)
- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [A Very Brief Introduction to Machine Learning With Applications to Communication Systems](https://mlsanity.com/paper/1808.02342/) (2018)
- [Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions](https://mlsanity.com/paper/1904.09673/) (2019)
- [ViterbiNet: A Deep Learning Based Viterbi Algorithm for Symbol Detection](https://mlsanity.com/paper/1905.10750/) (2019)
- [Optimistic Online Mirror Descent for Bridging Stochastic and Adversarial Online Convex Optimization](https://mlsanity.com/paper/2302.04552/) (2023)
- [Deep Learning for Wireless Communications: An Emerging Interdisciplinary Paradigm](https://mlsanity.com/paper/2007.05952/) (2020)
- [An Iterative BP-CNN Architecture for Channel Decoding](https://mlsanity.com/paper/1707.05697/) (2017)
- [Unsupervised Linear and Nonlinear Channel Equalization and Decoding using Variational Autoencoders](https://mlsanity.com/paper/1905.08795/) (2019)
- [Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding](https://mlsanity.com/paper/1906.04448/) (2019)
- [On the Necessity of Collaboration for Online Model Selection with Decentralized Data](https://mlsanity.com/paper/2404.09494/) (2024)
- [Deep Learning based Channel Estimation Algorithm over Time Selective Fading Channels](https://mlsanity.com/paper/1908.11013/) (2019)
- [Generalization Bounds for Neural Belief Propagation Decoders](https://mlsanity.com/paper/2305.10540/) (2023)
- [Fast Beam Alignment via Pure Exploration in Multi-armed Bandits](https://mlsanity.com/paper/2210.12625/) (2022)
- [Asymptotically optimal pilot allocation over Markovian fading channels](https://mlsanity.com/paper/1608.08413/) (2016)
- [Fast Change Identification in Multi-Play Bandits and its Applications in Wireless Networks](https://mlsanity.com/paper/2205.10366/) (2022)
- [Information-Theoretic Generalization Analysis for Topology-aware Heterogeneous Federated Edge Learning over Noisy Channels](https://mlsanity.com/paper/2310.16407/) (2023)
- [Topology Learning for Heterogeneous Decentralized Federated Learning over Unreliable D2D Networks](https://mlsanity.com/paper/2312.13611/) (2023)

## Abstract (arXiv)

Machine learning techniques have garnered great interest in designing communication systems owing to their capacity in tackling with channel uncertainty. To provide theoretical guarantees for learning-based communication systems, some recent works analyze generalization bounds for devised methods based on the assumption of Independently and Identically Distributed (I.I.D.) channels, a condition rarely met in practical scenarios. In this paper, we drop the I.I.D. channel assumption and study an online optimization problem of learning to communicate over time-correlated channels. To address this issue, we further focus on two specific tasks: optimizing channel decoders for time-correlated fading channels and selecting optimal codebooks for time-correlated additive noise channels. For utilizing temporal dependence of considered channels to better learn communication systems, we develop two online optimization algorithms based on the optimistic online mirror descent framework. Furthermore, we provide theoretical guarantees for proposed algorithms via deriving sub-linear regret bound on the expected error probability of learned systems. Extensive simulation experiments have been conducted to validate that our presented approaches can leverage the channel correlation to achieve a lower average symbol error rate compared to baseline methods, consistent with our theoretical findings.