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

By Arjun Anand, Gustavo De Veciana, Sanjay Shakkottai. IEEE Conference on Computer Communications, 2017

387 citations recorded by the source corpus.

Emerging 5G systems will need to efficiently support both broadband traffic (eMBB) and ultra-low-latency (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 satisfying instantaneous URLLC demands. For a linear rate loss model (loss to eMBB is linear in the amount of 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 allocations decisions of the eMBB scheduler. Next we consider a more general class of (convex) loss models and study optimal online joint eMBB/URLLC schedulers within the broad class of channel state dependent but time-homogeneous policies. We validate the characteristics and benefits of our schedulers via simulation.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/615bcd1cc5713f2eda36bbfa02137ee05169a0cd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

Citations in the corpus

Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach (2020, background): “In [11], the authors studied the impact of URLLC traffic on eMBB transmissions modeling the loss of eMBB data rate associated with URLLC traffic as a linear, convex, or threshold model.”
5G Wireless Network Slicing for eMBB, URLLC, and mMTC: A Communication-Theoretic View (2018, methodology): “of H-NOMA with puncturing is currently being considered within the 5G community [10].”
Ultrareliable and Low-Latency Wireless Communication: Tail, Risk, and Scale (2018, background): “A recent work towards this direction can be found in [47] as well as a recent application in the context of virtual reality [48].”
User-Centric Cell-Free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions (2021, background): “However, to perform channel estimation, the authors assume knowledge of the phase shifts at the access point although it changes per coherence block.”
A1 Resonators in 128° Y-cut Lithium Niobate with Electromechanical Coupling of 46.4% (2020, background): “More specifically, the enhanced mobile broadband (eMBB), where a 1000-fold increase in mobile data volume is envisioned [3], requires wide bandwidth at higher center frequencies.”
A Comprehensive Overview on 5G-and-Beyond Networks With UAVs: From Communications to Sensing and Intelligence (2020, methodology): “[165] Introducing mini-time slot Shortening Tttt Achieving low latency Protocol update is needed [179] Short error-correcting codes Reducing Tproc and Tsig Striking a balance between latency and error rate Difficult to design efficient code structure and decoder”