Ultra-Reliable and Low-Latency Wireless Communication: Tail, Risk and Scale
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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.
Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Jan 2018.
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Citing papers per year: 2021: 7; 2022: 1; 2023: 3; 2024: 2; 2025: 4; 2026: 5.
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- 2021Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systemscited by 45
- 2021Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoffcited by 30
- 2026Agentic AI Empowered Intent-Based Networking for 6Gcited by 6
- 2023Reliable and Efficient Data Collection in UAV-based IoT Networkscited by 2
- 2021Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Predictioncited by 1
- 2021Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Informationcited by 1
- 2022BSAC-CoEx: Coexistence of URLLC and Distributed Learning Services via Device Selectioncited by 1
- 2023Data-Driven Latency Probability Prediction for Wireless Networks: Focusing on Tail Probabilitiescited by 1
- 2024LoLaFL: Low-Latency Federated Learning via Forward-only Propagationcited by 1
- 2025Beamforming and Resource Allocation for Delay Minimization in RIS-Assisted OFDM Systemscited by 1
- 2025Resilient-native and Intelligent NextG Systemscited by 1
- 2021Computer Vision Aided URLL Communications: Proactive Service Identification and Coexistencecited by 0
10 more
- 2021Uncertainty Principles in Risk-Aware Statistical Estimationcited by 0
- 2021Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoTcited by 0
- 2023A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoTcited by 0
- 2024Cooperation and Federation in Distributed Radar Point Cloud Processingcited by 0
- 2025GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networkscited by 0
- 2025Ultra-Reliable Risk-Aggregated Sum Rate Maximization via Model-Aided Deep Learningcited by 0
- 2026Logic-Driven Semantic Communication for Resilient Multi-Agent Systemscited by 0
- 2026A Safety-Constrained Reinforcement Learning Framework for Reliable Wireless Autonomycited by 0
- 2026Real-Time Hard Peak Age-of-Information Safety with No-Regret Learningcited by 0
- 2026Physics-informed VAE-EVT for Tail Aware Radio Map Predictioncited by 0
- 2026Physics-informed VAE-EVT for Tail Aware Radio Map Prediction15 Aug 2026
- 2026Real-Time Hard Peak Age-of-Information Safety with No-Regret Learning30 Jul 2026
- 2026A Safety-Constrained Reinforcement Learning Framework for Reliable Wireless Autonomy12 Jan 2026
- 2026Logic-Driven Semantic Communication for Resilient Multi-Agent Systems11 Jan 2026
- 2026Agentic AI Empowered Intent-Based Networking for 6G10 Jan 2026
- 2025Ultra-Reliable Risk-Aggregated Sum Rate Maximization via Model-Aided Deep Learning30 Sep 2025
- 2025Resilient-native and Intelligent NextG Systems15 Jun 2025
- 2025Beamforming and Resource Allocation for Delay Minimization in RIS-Assisted OFDM Systems4 Jun 2025
- 2025GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networks8 May 2025
- 2024LoLaFL: Low-Latency Federated Learning via Forward-only Propagation19 Dec 2024
- 2024Cooperation and Federation in Distributed Radar Point Cloud Processing3 May 2024
- 2023A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT21 Nov 2023
10 more
- 2023Reliable and Efficient Data Collection in UAV-based IoT Networks9 Nov 2023
- 2023Data-Driven Latency Probability Prediction for Wireless Networks: Focusing on Tail Probabilities20 Jul 2023
- 2022BSAC-CoEx: Coexistence of URLLC and Distributed Learning Services via Device Selection22 Dec 2022
- 2021Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Information27 Aug 2021
- 2021Federated Learning with Correlated Data: Taming the Tail for Age-Optimal Industrial IoT17 Aug 2021
- 2021Uncertainty Principles in Risk-Aware Statistical Estimation29 Apr 2021
- 2021Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Prediction16 Apr 2021
- 2021Computer Vision Aided URLL Communications: Proactive Service Identification and Coexistence18 Mar 2021
- 2021Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoff18 Feb 2021
- 2021Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systems9 Jan 2021
- 2026Agentic AI Empowered Intent-Based Networking for 6Gcited by 6
- 2026Agentic AI Empowered Intent-Based Networking for 6G10 Jan 2026
- 2021Opportunities of Federated Learning in Connected, Cooperative and Automated Industrial Systemscited by 45
- 2021Vision-Aided 6G Wireless Communications: Blockage Prediction and Proactive Handoffcited by 30
- 2023Reliable and Efficient Data Collection in UAV-based IoT Networkscited by 2
- 2021Split Learning Meets Koopman Theory for Wireless Remote Monitoring and Predictioncited by 1
- 2021Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Informationcited by 1
- 2022BSAC-CoEx: Coexistence of URLLC and Distributed Learning Services via Device Selectioncited by 1
- 2023Data-Driven Latency Probability Prediction for Wireless Networks: Focusing on Tail Probabilitiescited by 1
- 2024LoLaFL: Low-Latency Federated Learning via Forward-only Propagationcited by 1
- 2025Beamforming and Resource Allocation for Delay Minimization in RIS-Assisted OFDM Systemscited by 1
- 2025Resilient-native and Intelligent NextG Systemscited by 1
- 2021Computer Vision Aided URLL Communications: Proactive Service Identification and Coexistencecited by 0
- 2021Uncertainty Principles in Risk-Aware Statistical Estimationcited by 0
12 most cited of 21 All 21 (JSON)
- 2026Physics-informed VAE-EVT for Tail Aware Radio Map Prediction15 Aug 2026
- 2026Real-Time Hard Peak Age-of-Information Safety with No-Regret Learning30 Jul 2026
- 2026A Safety-Constrained Reinforcement Learning Framework for Reliable Wireless Autonomy12 Jan 2026
- 2026Logic-Driven Semantic Communication for Resilient Multi-Agent Systems11 Jan 2026
- 2025Ultra-Reliable Risk-Aggregated Sum Rate Maximization via Model-Aided Deep Learning30 Sep 2025
- 2025Resilient-native and Intelligent NextG Systems15 Jun 2025
- 2025Beamforming and Resource Allocation for Delay Minimization in RIS-Assisted OFDM Systems4 Jun 2025
- 2025GFlowNets for Active Learning Based Resource Allocation in Next Generation Wireless Networks8 May 2025
- 2024LoLaFL: Low-Latency Federated Learning via Forward-only Propagation19 Dec 2024
- 2024Cooperation and Federation in Distributed Radar Point Cloud Processing3 May 2024
- 2023A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT21 Nov 2023
- 2023Reliable and Efficient Data Collection in UAV-based IoT Networks9 Nov 2023
12 newest of 21 All 21 (JSON)