# The Next Decade of Telecommunications Artificial Intelligence

arXiv: https://arxiv.org/abs/2101.09163 · HTML: https://mlsanity.com/paper/2101.09163/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 128 references that are in the map.
- First posted on arXiv 19 Jan 2021.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Deep MIMO Detection](https://mlsanity.com/paper/1706.01151/) (2017)
- [Artificial Intelligence-Enabled Cellular Networks: A Critical Path to Beyond-5G and 6G](https://mlsanity.com/paper/1907.07862/) (2019)
- [An Iterative BP-CNN Architecture for Channel Decoding](https://mlsanity.com/paper/1707.05697/) (2017)
- [Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding](https://mlsanity.com/paper/1906.04448/) (2019)
- [Performance Evaluation of Channel Decoding With Deep Neural Networks](https://mlsanity.com/paper/1711.00727/) (2017)
- [Artificial Intelligence (AI) Methods in Optical Networks: A Comprehensive Survey](https://mlsanity.com/paper/1801.01704/) (2018)
- [Utility Fair Optimisation of Antenna Tilt Angles in LTE Networks](https://mlsanity.com/paper/1310.1015/) (2013)
- [MIST: A Novel Training Strategy for Low-latency Scalable Neural Net Decoders](https://mlsanity.com/paper/1905.08990/) (2019)

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

- [RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN](https://mlsanity.com/paper/2111.06978/) (2021)
- [6G Network Business Support System](https://mlsanity.com/paper/2307.10004/) (2023)
- [Machine Learning & Wi-Fi: Unveiling the Path Towards AI/ML-Native IEEE 802.11 Networks](https://mlsanity.com/paper/2405.11504/) (2024)

## Abstract (arXiv)

It has been an exciting journey since the mobile communications and artificial intelligence were conceived 37 years and 64 years ago. While both fields evolved independently and profoundly changed communications and computing industries, the rapid convergence of 5G and deep learning is beginning to significantly transform the core communication infrastructure, network management and vertical applications. The paper first outlines the individual roadmaps of mobile communications and artificial intelligence in the early stage, with a concentration to review the era from 3G to 5G when AI and mobile communications started to converge. With regard to telecommunications artificial intelligence, the paper further introduces in detail the progress of artificial intelligence in the ecosystem of mobile communications. The paper then summarizes the classifications of AI in telecom ecosystems along with its evolution paths specified by various international telecommunications standardization bodies. Towards the next decade, the paper forecasts the prospective roadmap of telecommunications artificial intelligence. In line with 3GPP and ITU-R timeline of 5G & 6G, the paper further explores the network intelligence following 3GPP and ORAN routes respectively, experience and intention driven network management and operation, network AI signalling system, intelligent middle-office based BSS, intelligent customer experience management and policy control driven by BSS and OSS convergence, evolution from SLA to ELA, and intelligent private network for verticals. The paper is concluded with the vision that AI will reshape the future B5G or 6G landscape and we need pivot our R&D, standardizations, and ecosystem to fully take the unprecedented opportunities.