# 5G New Radio: Unveiling the Essentials of the Next Generation Wireless Access Technology

arXiv: https://arxiv.org/abs/1806.06898 · HTML: https://mlsanity.com/paper/1806.06898/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2018.

## Built on by (most cited first, 16 of 16)

- [Overview of Deep Learning-based CSI Feedback in Massive MIMO Systems](https://mlsanity.com/paper/2206.14383/) (2022)
- [Channelformer: Attention based Neural Solution for Wireless Channel Estimation and Effective Online Training](https://mlsanity.com/paper/2302.04368/) (2023)
- [Joint Optimization of Age of Information and Energy Consumption in NR-V2X System based on Deep Reinforcement Learning](https://mlsanity.com/paper/2407.08458/) (2024)
- [Over-the-Air Computation Based on Balanced Number Systems for Federated Edge Learning](https://mlsanity.com/paper/2210.07012/) (2022)
- [Artificial Intelligence in 3GPP 5G-Advanced: A Survey](https://mlsanity.com/paper/2305.05092/) (2023)
- [LIDAR and Position-Aided mmWave Beam Selection with Non-local CNNs and Curriculum Training](https://mlsanity.com/paper/2104.14579/) (2021)
- [Bit Error and Block Error Rate Training for ML-Assisted Communication](https://mlsanity.com/paper/2210.14103/) (2022)
- [A Demonstration of Over-the-Air Computation for Federated Edge Learning](https://mlsanity.com/paper/2209.09954/) (2022)
- [A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing](https://mlsanity.com/paper/2110.02736/) (2021)
- [Deep Reinforcement Learning for Dynamic Spectrum Sharing of LTE and NR](https://mlsanity.com/paper/2102.11176/) (2021)
- [IIFNet: A Fusion based Intelligent Service for Noisy Preamble Detection in 6G](https://mlsanity.com/paper/2204.07854/) (2022)
- [QoS-Aware Scheduling in New Radio Using Deep Reinforcement Learning](https://mlsanity.com/paper/2107.06570/) (2021)
- [DEFORM: A Practical, Universal Deep Beamforming System](https://mlsanity.com/paper/2203.09727/) (2022)
- [Wireless Federated k-Means Clustering with Non-coherent Over-the-Air Computation](https://mlsanity.com/paper/2308.06371/) (2023)
- [A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT](https://mlsanity.com/paper/2401.06135/) (2023)
- [AI-RAN on NPUs: Baseband Processing Without Baseband Chips](https://mlsanity.com/paper/2607.04224/) (2026)

## Abstract (arXiv)

The 5th generation (5G) wireless access technology, known as new radio (NR), will address a variety of usage scenarios from enhanced mobile broadband to ultra-reliable low-latency communications to massive machine type communications. Key technology features include ultra-lean transmission, support for low latency, advanced antenna technologies, and spectrum flexibility including operation in high frequency bands and inter-working between high and low frequency bands. This article provides an overview of the essentials of the state of the art in 5G wireless technology represented by the 3GPP NR technical specifications, with a focus on the physical layer. We describe the fundamental concepts of 5G NR, explain in detail the design of physical channels and reference signals, and share the various design rationales influencing standardization.