# Learning at the Speed of Wireless: Online Real-Time Learning for AI-Enabled MIMO in NextG

arXiv: https://arxiv.org/abs/2403.02651 · HTML: https://mlsanity.com/paper/2403.02651/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 7 of 7)

- [Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6G](https://mlsanity.com/paper/1907.07862/) (2019)
- [Deep Transfer Learning Based Downlink Channel Prediction for FDD Massive MIMO Systems](https://mlsanity.com/paper/1912.12265/) (2019)
- [Detect to Learn: Structure Learning with Attention and Decision Feedback for MIMO-OFDM Receive Processing](https://mlsanity.com/paper/2208.09287/) (2022)
- [Channel Estimation via Successive Denoising in MIMO OFDM Systems: A Reinforcement Learning Approach](https://mlsanity.com/paper/2101.10300/) (2021)
- [Model-based Deep Learning Receiver Design for Rate-Splitting Multiple Access](https://mlsanity.com/paper/2205.00849/) (2022)
- [Learning to Estimate: A Real-Time Online Learning Framework for MIMO-OFDM Channel Estimation](https://mlsanity.com/paper/2305.13487/) (2023)
- [Towards Explainable Machine Learning: The Effectiveness of Reservoir Computing in Wireless Receive Processing](https://mlsanity.com/paper/2310.04956/) (2023)

## Built on by (most cited first, 3 of 3)

- [Generative Diffusion Receivers: Achieving Pilot-Efficient MIMO-OFDM Communications](https://mlsanity.com/paper/2506.18419/) (2025)
- [Adaptive End-to-End Transceiver Design for NextG Pilot-Free and CP-Free Wireless Systems](https://mlsanity.com/paper/2510.25416/) (2025)
- [Contrastive Predictive Coding with Compression for Enhanced Channel State Feedback in Wireless Networks](https://mlsanity.com/paper/2607.05419/) (2026)

## Abstract (arXiv)

Integration of artificial intelligence (AI) and machine learning (ML) into the air interface has been envisioned as a key technology for next-generation (NextG) cellular networks. At the air interface, multiple-input multiple-output (MIMO) and its variants such as multi-user MIMO (MU-MIMO) and massive/full-dimension MIMO have been key enablers across successive generations of cellular networks with evolving complexity and design challenges. Initiating active investigation into leveraging AI/ML tools to address these challenges for MIMO becomes a critical step towards an AI-enabled NextG air interface. At the NextG air interface, the underlying wireless environment will be extremely dynamic with operation adaptations performed on a sub-millisecond basis by MIMO operations such as MU-MIMO scheduling and rank/link adaptation. Given the enormously large number of operation adaptation possibilities, we contend that online real-time AI/ML-based approaches constitute a promising paradigm. To this end, we outline the inherent challenges and offer insights into the design of such online real-time AI/ML-based solutions for MIMO operations. An online real-time AI/ML-based method for MIMO-OFDM channel estimation is then presented, serving as a potential roadmap for developing similar techniques across various MIMO operations in NextG.