# A Very Brief Introduction to Machine Learning With Applications to Communication Systems

arXiv: https://arxiv.org/abs/1808.02342 · HTML: https://mlsanity.com/paper/1808.02342/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2018.

## Built on by (most cited first, 30 of 35)

- [An Introduction to Bi-level Optimization: Foundations and Applications in Signal Processing and Machine Learning](https://mlsanity.com/paper/2308.00788/) (2023)
- [Machine Learning for Large-Scale Optimization in 6G Wireless Networks](https://mlsanity.com/paper/2301.03377/) (2023)
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- [Modular Meta-Learning for Power Control via Random Edge Graph Neural Networks](https://mlsanity.com/paper/2108.13178/) (2021)
- [Semantic Information Recovery in Wireless Networks](https://mlsanity.com/paper/2204.13366/) (2022)
- [Self-Supervised RF Signal Representation Learning for NextG Signal Classification with Deep Learning](https://mlsanity.com/paper/2207.03046/) (2022)
- [In-Context Learning for MIMO Equalization Using Transformer-Based Sequence Models](https://mlsanity.com/paper/2311.06101/) (2023)
- [Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications](https://mlsanity.com/paper/2207.00300/) (2022)
- [Leveraging Large Language Models for Wireless Symbol Detection via In-Context Learning](https://mlsanity.com/paper/2409.00124/) (2024)
- [Fast Power Control Adaptation via Meta-Learning for Random Edge Graph Neural Networks](https://mlsanity.com/paper/2105.00459/) (2021)
- [A Hybrid Architecture for Federated and Centralized Learning](https://mlsanity.com/paper/2105.03288/) (2021)
- [How to Evaluate Semantic Communications for Images with ViTScore Metric?](https://mlsanity.com/paper/2309.04891/) (2023)
- [Cellular Network Capacity and Coverage Enhancement with MDT Data and Deep Reinforcement Learning](https://mlsanity.com/paper/2202.10968/) (2022)
- [FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning](https://mlsanity.com/paper/2204.07773/) (2022)
- [CMDNet: Learning a Probabilistic Relaxation of Discrete Variables for Soft Detection with Low Complexity](https://mlsanity.com/paper/2102.12756/) (2021)
- [Defensive Distillation based Adversarial Attacks Mitigation Method for Channel Estimation using Deep Learning Models in Next-Generation Wireless Networks](https://mlsanity.com/paper/2208.10279/) (2022)
- [Systematic Review on Reinforcement Learning in the Field of Fintech](https://mlsanity.com/paper/2305.07466/) (2023)
- [Decision Feedback In-Context Symbol Detection over Block-Fading Channels](https://mlsanity.com/paper/2411.07600/) (2024)
- [Distributed Associative Memory via Online Convex Optimization](https://mlsanity.com/paper/2509.22321/) (2025)
- [Deep Learning-based List Sphere Decoding for Faster-than-Nyquist (FTN) Signaling Detection](https://mlsanity.com/paper/2204.07569/) (2022)
- [Deep Learning-Based Detection for Marker Codes over Insertion and Deletion Channels](https://mlsanity.com/paper/2401.01155/) (2024)
- [On the Robustness of Deep Learning-aided Symbol Detectors to Varying Conditions and Imperfect Channel Knowledge](https://mlsanity.com/paper/2401.12645/) (2024)
- [Machine Learning in Short-Reach Optical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2405.09557/) (2024)
- [Data-Driven Symbol Detection for Intersymbol Interference Channels with Bursty Impulsive Noise](https://mlsanity.com/paper/2405.10814/) (2024)
- [Simplicity is Key: An Unsupervised Pretraining Approach for Sparse Radio Channels](https://mlsanity.com/paper/2505.13055/) (2025)
- [AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy Gradient](https://mlsanity.com/paper/2210.06888/) (2022)
- [Calibrating AI Models for Wireless Communications via Conformal Prediction](https://mlsanity.com/paper/2212.07775/) (2022)
- [Disturbance-based Discretization, Differentiable IDS Channel, and an IDS-Correcting Code for DNA-based Storage](https://mlsanity.com/paper/2407.18929/) (2024)
- [Online Optimization for Learning to Communicate over Time-Correlated Channels](https://mlsanity.com/paper/2409.00575/) (2024)
- [An explainable machine learning approach for energy forecasting at the household level](https://mlsanity.com/paper/2410.14416/) (2024)

## Abstract (arXiv)

Given the unprecedented availability of data and computing resources, there is widespread renewed interest in applying data-driven machine learning methods to problems for which the development of conventional engineering solutions is challenged by modelling or algorithmic deficiencies. This tutorial-style paper starts by addressing the questions of why and when such techniques can be useful. It then provides a high-level introduction to the basics of supervised and unsupervised learning. For both supervised and unsupervised learning, exemplifying applications to communication networks are discussed by distinguishing tasks carried out at the edge and at the cloud segments of the network at different layers of the protocol stack.