Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach

By Madyan Alsenwi, Nguyen H. Tran, Medhi Bennis, Shashi Raj Pandey, Anupam Kumar Bairagi, Choong Seon Hong. IEEE Transactions on Wireless Communications, 2020

307 citations recorded by the source corpus.

In this paper, we study the resource slicing problem in a dynamic multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). While eMBB services focus on high data rates, URLLC is very strict in terms of latency and reliability. In view of this, the resource slicing problem is formulated as an optimization problem that aims at maximizing the eMBB data rate subject to a URLLC reliability constraint, while considering the variance of the eMBB data rate to reduce the impact of immediately scheduled URLLC traffic on the eMBB reliability. To solve the formulated problem, an optimization-aided Deep Reinforcement Learning (DRL) based framework is proposed, including: 1) eMBB resource allocation phase, and 2) URLLC scheduling phase. In the first phase, the optimization problem is decomposed into three subproblems and then each subproblem is transformed into a convex form to obtain an approximate resource allocation solution. In the second phase, a DRL-based algorithm is proposed to intelligently distribute the incoming URLLC traffic among eMBB users. Simulation results show that our proposed approach can satisfy the stringent URLLC reliability while keeping the eMBB reliability higher than 90%.

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/3e3479d644e9a950ffd9383abb8bf7dea8baf426

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In Electrical and Electronic Engineering

Citations in the corpus

Holistic Network Virtualization and Pervasive Network Intelligence for 6G (2023, methodology): “DRL has been widely used for network optimization [134], resource allocation [19], [118], [135], and user association [116], [121], [123] in wireless networks.”
Survey on 6G Frontiers: Trends, Applications, Requirements, Technologies and Future Research (2021, background): “[164] discussed another enabling technology that would be useful in uplink grant-free structures and in reducing the transmission latency.”
Artificial Intelligence for the Metaverse: A Survey (2022, background): “(eMBB) and uRLLC [110], in which the complicated patterns”