Resource Allocation for Urllc and Embb Traffic in Uplink Wireless Networks

Lee, Duan-Shin; Chang, Cheng-Shang; Zhang, Ruhui; Lee, Mao-Pin

doi:10.2139/ssrn.4220800

Cited by 1 publication

(1 citation statement)

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“…In [13], a deep learning technique is adopted and tuned to solve the mixed traffic problem for the eMBB and URLLC mixed traffic in the UL direction with puncturing for eMBB traffic. In [14], The authors use game-theoretic approaches to divide the UL network resources into two pools, one for eMBB traffic only and the other pool is shared between URLLC devices and eMBB traffic. In [15], the UL multiple access techniques are discussed for overlapping traffic between URLLC devices and eMBB users.…”

Section: A Related Workmentioning

confidence: 99%

Machine Learning-Based Uplink Scheduling Approaches for Mixed Traffic in Cellular Systems

2023

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The problem of the uplink resource allocation of mixed-traffic types in cellular networks is a challenging problem that has not been addressed sufficiently in the literature. In this paper, we consider the 5G uplink scheduling for Ultra-Reliable and Low Latency Communications and enhanced Mobile Broad-Band (eMBB) traffic types. There are three main scheduling techniques to be considered, namely, the grantbased (GB), the semi-persistent, and the grant-free (GF) techniques. Furthermore, there are three different schemes used in GF scheduling, namely, the reactive, the k-repetitions, and the proactive schemes. We devise a mathematical model for the GF services using the k-repetitions scheme as the first model to define such traffic in a single cell. In addition, the GB scheduling model for eMBB traffic is adapted to fit our problem. We formulate the scheduling problem as a mixed-integer non-linear programming optimization problem. We introduce a complete system model that includes GF and GB subsystems. We introduce a novel mixed scheduler that combines the advantages of two well-known schedulers in the literature. We introduce novel machine-learning-based scheduling algorithms and evaluate them in comparison to well-known algorithms in the literature in addition to the optimal bound that we also derive. The results show that the proposed algorithms produce near-optimal results in real time.

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Section: A Related Workmentioning

confidence: 99%