Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks

Abstract: Emerging 5G systems will need to efficiently support both enhanced mobile broadband traffic (eMBB) and ultra-low-latency communications (URLLC) traffic. In these systems, time is divided into slots which are further sub-divided into minislots. From a scheduling perspective, eMBB resource allocations occur at slot boundaries, whereas to reduce latency URLLC traffic is pre-emptively overlapped at the minislot timescale, resulting in selective superposition/puncturing of eMBB allocations. This approach enables minimal URLLC latency at a potential rate loss to eMBB traffic. We study joint eMBB and URLLC schedulers for such systems, with the dual objectives of maximizing utility for eMBB traffic while immediately satisfying URLLC demands. For a linear rate loss model (loss to eMBB is linear in the amount of URLLC superposition/puncturing), we derive an optimal joint scheduler. Somewhat counter-intuitively, our results show that our dual objectives can be met by an iterative gradient scheduler for eMBB traffic that anticipates the expected loss from URLLC traffic, along with an URLLC demand scheduler that is oblivious to eMBB channel states, utility functions and allocation decisions of the eMBB scheduler. Next we consider a more general class of (convex/threshold) loss models and study optimal online joint eMBB/URLLC schedulers within the broad class of channel state dependent but minislot-homogeneous policies. A key observation is that unlike the linear rate loss model, for the convex and threshold rate loss models, optimal eMBB and URLLC scheduling decisions do not de-couple and joint optimization is necessary to satisfy the dual objectives. We validate the characteristics and benefits of our schedulers via simulation.

• The paper presents a joint scheduling framework that balances eMBB throughput and URLLC latency using linear, convex, and threshold models.
• The study employs iterative gradient-based and minislot-homogeneous policies to mitigate eMBB rate loss from URLLC puncturing.
• Simulations validate the proposed models, demonstrating enhanced resource allocation and robust performance under dynamic 5G traffic conditions.

Joint Scheduling of URLLC and eMBB Traffic in 5G Wireless Networks

This paper investigates joint scheduling of Ultra-Reliable Low-Latency Communications (URLLC) and Enhanced Mobile Broadband (eMBB) traffic in 5G networks, addressing one of the core challenges in the design of these next-generation wireless systems. The authors propose various approaches for simultaneously maximizing utility for eMBB traffic while meeting the stringent latency requirements of URLLC.

Overview

The paper presents a framework where time is divided into slots, which are further subdivided into minislots. eMBB traffic allocations occur at slot boundaries while allowing URLLC traffic with its low-latency requirements to be dynamically superimposed on eMBB allocations at the minislot timescale. This results in a selective superposition/puncturing approach leading to potential eMBB rate losses.

Key Contributions

1. Linear Model for eMBB Rate Loss: The authors derive an optimal joint scheduler using a linear rate loss model that is counter-intuitive in its simplicity: URLLC can be randomly placed irrespective of channel states or utility functions, while eMBB scheduling accounts for anticipated URLLC losses through an iterative gradient-based scheduler.
2. Convex Model for Loss: For convex loss functions, which reflect more general and realistic scenarios, the authors explore minislot-homogeneous scheduling policies. They demonstrate that joint optimization of eMBB and URLLC scheduling is crucial for these models, as indicated by the non-decomposability of the problem.
3. Threshold Model: This model applies a binary (0-1) structure to rate loss, where eMBB transmissions can either be perfect or entirely lost, depending on the degree of puncturing. The authors introduce Resource Proportional (RP) and Threshold Proportional (TP) placement policies, the latter minimizing the probability of eMBB losses and providing implementation simplicity.
4. Simulation and Validation: The authors validate their proposed schedulers through simulations that underscore their effectiveness over simplistic models. This validation assures the frameworks' capabilities in real-world applications with diverse traffic and load conditions.

Implications

The paper's significance lies in its implications for designing effective schedulers that can handle diverse and dynamic traffic types without severely compromising performance. The proposed methods offer potential routes for achieving efficient allocation of scarce wireless resources, directly contributing to the reliable and fast service requirements proposed by 5G standards.

Future Considerations

Future work may involve extending these models to incorporate diverse network architectures, heterogeneous devices, non-i.i.d. channel models, and the variability of user mobility patterns. Such extensions could further establish effective joint scheduling under dynamic and realistic network conditions. Additionally, exploring machine learning-based techniques could potentially yield adaptive and context-aware schedulers with enhanced performance and efficiency.

In summary, this paper rigorously addresses the critical intersection of URLLC and eMBB scheduling in 5G. Through detailed models, algorithmic insights, and comprehensive simulation, it lays a foundation for future research and practical implementations in next-generation wireless networks.