Dynamic Resource Allocation With RAN Slicing and Scheduling for uRLLC and eMBB Hybrid Services

Abstract

To cope with the limited radio and power resources, designing energy-and cost-efficient resource allocation strategy with RAN slicing and scheduling is important in ensuring the extreme QoS of differentiated Internet of things (IoT) services. In this regard, we focus on guaranteeing the latency and reliability of sporadic uRLLC uplink traffic while improving the quality of continuous eMBB services (e.g., quality of the video) together in this paper. Firstly, a dynamic optimization model considering power consumption and service quality is used to construct the cost function in both time domain and frequency bandwidth for heterogeneous services, subject to the latency constraint. Secondly, given its complexity, a novel two-timescale algorithm with employing Lyapunov optimization is designed, including two sub-algorithms: long-timescale bandwidth allocation and short-timescale service control. In further, the theoretical optimality is analyzed according to the relationships between control parameters and service performances. The utility of our approach and its hard latency guarantee are also illustrated through simulation results under tolerable power consumption. INDEX TERMS 5G, network slicing, radio access network, resource allocation, heterogeneous services.

Latency-Aware Dynamic Resource Allocation Scheme for Multi-Tier 5G Network: A Network Slicing-Multitenancy Scenario

“…In [32], the authors proposed a dynamic resource allocation scheme for eMBB and URLLC slice-use cases. The proposed scheme is based on optimal power control for latency-aware resource allocation.…”

A Classification of the Enabling Techniques for Low Latency and Reliable Communications in 5G and Beyond: AI-Enabled Edge Caching

“…In [22], it was deliberated that techniques such as dynamic multiplexing, latency sensitive scheduling schemes, and grant-free access can be effective at reducing latency in 5G. The work in [23], [29] promoted the use of network slicing technique to effectively allocate network resources to provide performance guarantees for URLLC services. In [9], [24], [25], [27], [28], [30] it was discussed that 5G networks would experience a shift from the conventional cloud computing setting to edge computing systems.…”

“…Also, due to the different scheduling granularity, eMBB and URLLC services need to be dynamically multiplexed in the time domain. This is because URLLC traffic is characterized by small and sporadic packets which require low latency and high reliability while eMBB services are featured with large payloads, high peak data rates, or other high bandwidth, to guarantee sufficiently high and stable quality of data such as image or voice contents [23].…”

“…Network slicing refers to the process of slicing a physical network into logical subnetworks optimized for specific applications and services. It aims to allocate dedicated network resources for verticals of interest [23], [29], [45], [64], [66]. Network slicing is an effective technique for meeting the diverse deployment requirements of URLLC, eMBB, and mMTC services in a multi-tenant 5G network.…”

On the Multiplexing of Data and Metadata for Ultra-Reliable Low-Latency Communications in 5G

“…For the baseline single transmission, the minimum number of required resources n 1 is calculated based on (3) such that E(n 1 , γ, M + D) = P tar out . For equal constant BLEP with retransmission, the error probabilities are obtained from (15)…”