# 5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View

arXiv: https://arxiv.org/abs/1804.05057 · HTML: https://mlsanity.com/paper/1804.05057/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2018.

## Built on by (most cited first, 18 of 18)

- [Attention-based Open RAN Slice Management using Deep Reinforcement Learning](https://mlsanity.com/paper/2306.09490/) (2023)
- [Guaranteed Dynamic Scheduling of Ultra-Reliable Low-Latency Traffic via Conformal Prediction](https://mlsanity.com/paper/2302.07675/) (2023)
- [What If We Had Used a Different App? Reliable Counterfactual KPI Analysis in Wireless Systems](https://mlsanity.com/paper/2410.00150/) (2024)
- [Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul](https://mlsanity.com/paper/2211.07466/) (2022)
- [Intelligent Task Offloading: Advanced MEC Task Offloading and Resource Management in 5G Networks](https://mlsanity.com/paper/2501.06242/) (2025)
- [Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB traffic](https://mlsanity.com/paper/2103.01801/) (2021)
- [Throughput and Latency in the Distributed Q-Learning Random Access mMTC Networks](https://mlsanity.com/paper/2111.00299/) (2021)
- [Machine Learning Decoder for 5G NR PUCCH Format 0](https://mlsanity.com/paper/2209.07861/) (2022)
- [BSAC-CoEx: Coexistence of URLLC and Distributed Learning Services via Device Selection](https://mlsanity.com/paper/2212.11805/) (2022)
- [Generative AI for O-RAN Slicing: A Semi-Supervised Approach with VAE and Contrastive Learning](https://mlsanity.com/paper/2401.08861/) (2024)
- [Nearest neighbor Methods and their Applications in Design of 5G & Beyond Wireless Networks](https://mlsanity.com/paper/2107.07869/) (2021)
- [CNN based Channel Estimation using NOMA for mmWave Massive MIMO System](https://mlsanity.com/paper/2108.00367/) (2021)
- [Multi-Objective Provisioning of Network Slices using Deep Reinforcement Learning](https://mlsanity.com/paper/2207.13821/) (2022)
- [Multi-Power Level Q-Learning Algorithm for Random Access in NOMA mMTC Systems](https://mlsanity.com/paper/2301.05196/) (2023)
- [Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN](https://mlsanity.com/paper/2507.18111/) (2025)
- [Distributed Dynamic Associative Memory via Online Convex Optimization](https://mlsanity.com/paper/2511.23347/) (2025)
- [Post-Selection Distributional Model Evaluation](https://mlsanity.com/paper/2603.23055/) (2026)
- [Intelligent Radio Resource Slicing for 6G In-Body Subnetworks](https://mlsanity.com/paper/2603.28529/) (2026)

## Abstract (arXiv)

The grand objective of 5G wireless technology is to support three generic services with vastly heterogeneous requirements: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable low-latency communications (URLLC). Service heterogeneity can be accommodated by network slicing, through which each service is allocated resources to provide performance guarantees and isolation from the other services. Slicing of the Radio Access Network (RAN) is typically done by means of orthogonal resource allocation among the services. This work studies the potential advantages of allowing for non-orthogonal sharing of RAN resources in uplink communications from a set of eMBB, mMTC and URLLC devices to a common base station. The approach is referred to as Heterogeneous Non-Orthogonal Multiple Access (H-NOMA), in contrast to the conventional NOMA techniques that involve users with homogeneous requirements and hence can be investigated through a standard multiple access channel. The study devises a communication-theoretic model that accounts for the heterogeneous requirements and characteristics of the three services. The concept of reliability diversity is introduced as a design principle that leverages the different reliability requirements across the services in order to ensure performance guarantees with non-orthogonal RAN slicing. This study reveals that H-NOMA can lead, in some regimes, to significant gains in terms of performance trade-offs among the three generic services as compared to orthogonal slicing.