# Ultra-Reliable and Low-Latency Wireless Communication: Tail, Risk and Scale

arXiv: https://arxiv.org/abs/1801.01270 · HTML: https://mlsanity.com/paper/1801.01270/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jan 2018.

## Built on by (most cited first, 22 of 22)

- [Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems](https://mlsanity.com/paper/2101.03367/) (2021)
- [Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/2102.09527/) (2021)
- [Agentic AI Empowered Intent-Based Networking for 6G](https://mlsanity.com/paper/2601.06640/) (2026)
- [Reliable and Efficient Data Collection in UAV-based IoT Networks](https://mlsanity.com/paper/2311.05303/) (2023)
- [Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Prediction](https://mlsanity.com/paper/2104.08109/) (2021)
- [Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Information](https://mlsanity.com/paper/2108.12198/) (2021)
- [BSAC-CoEx: Coexistence of URLLC and Distributed Learning Services via Device Selection](https://mlsanity.com/paper/2212.11805/) (2022)
- [Data-Driven Latency Probability Prediction for Wireless Networks: Focusing on Tail Probabilities](https://mlsanity.com/paper/2307.10648/) (2023)
- [LoLaFL: Low-Latency Federated Learning via Forward-only Propagation](https://mlsanity.com/paper/2412.14668/) (2024)
- [Beamforming and Resource Allocation for Delay Minimization in RIS-Assisted OFDM Systems](https://mlsanity.com/paper/2506.03586/) (2025)
- [Resilient-native and Intelligent NextG Systems](https://mlsanity.com/paper/2506.12795/) (2025)
- [Computer Vision Aided URLL Communications: Proactive Service Identification and Coexistence](https://mlsanity.com/paper/2103.10419/) (2021)
- [Uncertainty Principles in Risk-Aware Statistical Estimation](https://mlsanity.com/paper/2104.14283/) (2021)
- [Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT](https://mlsanity.com/paper/2108.07504/) (2021)
- [A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT](https://mlsanity.com/paper/2401.06135/) (2023)
- [Cooperation and Federation in Distributed Radar Point Cloud Processing](https://mlsanity.com/paper/2405.01995/) (2024)
- [GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networks](https://mlsanity.com/paper/2505.05224/) (2025)
- [Ultra-Reliable Risk-Aggregated Sum Rate Maximization via Model-Aided Deep Learning](https://mlsanity.com/paper/2509.26311/) (2025)
- [Logic-Driven Semantic Communication for Resilient Multi-Agent Systems](https://mlsanity.com/paper/2601.06733/) (2026)
- [A Safety-Constrained Reinforcement Learning Framework for Reliable Wireless Autonomy](https://mlsanity.com/paper/2602.13207/) (2026)
- [Real-Time Hard Peak Age-of-Information Safety with No-Regret Learning](https://mlsanity.com/paper/2607.27626/) (2026)
- [Physics-informed VAE-EVT for Tail Aware Radio Map Prediction](https://mlsanity.com/paper/2608.15314/) (2026)

## Abstract (arXiv)

Ensuring ultra-reliable and low-latency communication (URLLC) for 5G wireless networks and beyond is of capital importance and is currently receiving tremendous attention in academia and industry. At its core, URLLC mandates a departure from expected utility-based network design approaches, in which relying on average quantities (e.g., average throughput, average delay and average response time) is no longer an option but a necessity. Instead, a principled and scalable framework which takes into account delay, reliability, packet size, network architecture, and topology (across access, edge, and core) and decision-making under uncertainty is sorely lacking. The overarching goal of this article is a first step to fill this void. Towards this vision, after providing definitions of latency and reliability, we closely examine various enablers of URLLC and their inherent tradeoffs. Subsequently, we focus our attention on a plethora of techniques and methodologies pertaining to the requirements of ultra-reliable and low-latency communication, as well as their applications through selected use cases. These results provide crisp insights for the design of low-latency and high-reliable wireless networks.