Ultra-Reliable and Low-Latency Wireless Communication: Tail, Risk and Scale (1801.01270v2)

Abstract: 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.

• The paper introduces a novel framework for URLLC that integrates tail distribution analysis and risk-sensitive decision making to boost performance.
• It employs advanced techniques such as Extreme Value Theory, stochastic network calculus, and risk-sensitive learning to meet stringent latency and reliability standards.
• Case studies in mmWave, virtual reality, and mobile edge computing illustrate significant gains in network reliability and efficiency.

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

Ensuring Ultra-Reliable and Low-Latency Communication (URLLC) within the evolving landscape of 5G and beyond necessitates a paradigm shift from traditional network design paradigms that rely on average metrics. The paper by Bennis, Debbah, and Poor advocates for a comprehensive framework emphasizing tail, risk, and scale to address the inherent demands of URLLC, focusing on delay, reliability, packet size, network architecture, and decision-making under uncertainty.

Key Concepts and Technical Insights

Fundamentally, URLLC aims to achieve stringent latency and reliability requirements, critical for next-generation applications like autonomous vehicles, remote surgery, virtual and augmented reality. Traditional approaches optimizing for average throughput or delay are inadequate for URLLC, driving the need for methodologies sensitive to the statistical extremes or the "tails" of latency distributions.

• Latency:
  • End-to-End (E2E) Latency: The total delay experienced from the source to the destination, including transmission, queuing, processing, and re-transmissions.
  • User Plane Latency: Defined by 3GPP as the one-way transit time for application layer packets under unloaded conditions, crucial for eMBB and URLLC, the latter requiring latencies as low as 1ms.
  • Control Plane Latency: Transition time from an idle to an active state, essential for battery-efficient operations within 20ms as mandated by 3GPP.

  Techniques to reduce latency span from a shorter Transmission Time Interval (TTI), edge computing, dynamic resource allocation, and sophisticated scheduling mechanisms.

• Reliability:

  • Physical Layer: Reliability assures the probability that a packet of given size is correctly delivered within a specific time, often required to be as high as $1-10^{-9}$ under varying scenarios.
  • Techniques include multi-connectivity, data replication, HARQ, and coordinated beamforming, all aimed at mitigating the diverse causes of packet loss in unreliable channels.
• Risk:
  • Integrating risk into decision-making processes acknowledges the uncertainties and variabilities in channel conditions. This shifts the focus from maximizing expected utilities to mitigating potential extreme losses, employing tools such as risk-sensitive reinforcement learning and various risk measures, including Value at Risk (VaR) and Conditional VaR (CVaR).
• Tail:
  • Capturing the behavior of latency distributions under extreme conditions necessitates mathematical tools like Extreme Value Theory (EVT), which characterizes the tail distributions essential for modeling rare, but impactful events.
  • Effective Bandwidth and Stochastic Network Calculus (SNC) provide mechanisms to handle non-asymptotic performance metrics by focusing on probabilistic bounds on queue lengths and delay distributions.
• Scale:
  • Dealing with massive systems requires tools from statistical physics and mean-field game theory that enable scalable solutions by approximating the collective behavior of large networks using simplified models. These methodologies allow the analysis of network performance without relying on extensive simulations, crucial for ultra-dense networks and massive machine-type communications.

Methodological Framework and Applications

The paper's methodological approach spans several fields:

1. Risk-Sensitive Learning: Ensures robust performance amidst channel variabilities by prioritizing strategies that mitigate worst-case losses.
2. Mathematical Finance: Employs financial risk measures to quantify and manage risks in network decision-making processes.
3. EVT and SNC: Provide a rigorous approach for tail management in latency distributions, crucial for meeting strict delay guarantees.
4. Statistical Physics and Mean-Field Games: Offer scalable, tractable models for managing resource allocation and user interactions in dense deployments.

Case Studies

Four case studies illustrate the framework's efficacy:

1. Millimeter-Wave Reliability: Employed risk-sensitive reinforcement learning to adjust beamwidth and power in mmWave communication, achieving over 80% reliability at 10Gbps despite high link variability.
2. Virtual Reality (VR): Demonstrated that proactive computing and multi-connectivity can markedly reduce VR session latencies and improve reliability, essential for immersive experiences.
3. Mobile Edge Computing (MEC): Utilized EVT to manage task queue lengths in MEC servers, ensuring delay bounds with a high degree of reliability.
4. Ultra-Dense Networks: Applied statistical physics to determine optimal base station (BS)-user equipment (UE) associations, validating the approach with significant reliability improvements in user-SNR distributions.

Implications and Future Directions

The proposed framework lays the groundwork for future developments in AI and machine learning, particularly in edge computing and massive network optimization. The shift towards incorporating risk and extreme event management into network design promises significant advances in enabling ultra-reliable, low-latency communication critical for emerging 5G applications and beyond. Future explorations may focus on further integration of robust AI methodologies, advanced statistical techniques, and scalable network protocols to meet the evolving URLLC requirements.