Dynamic Hybrid Resource Utilisation and MCS-based Intelligent Layering

TL;DR

This paper introduces a joint optimization framework for resource allocation in 5G networks, integrating hybrid resource utilization with intelligent layering to meet diverse QoS needs efficiently.

Contribution

It presents a novel mixed-integer linear programming model that jointly optimizes bandwidth, power, and MCS indices considering realistic channel effects and adaptability modes.

Findings

Achieves energy efficiency above 10^7 kb/J in Baseline Mode.

Attains sub-millisecond latency with near-ideal throughput in Ideal-Chaser Mode.

Outperforms recent methods in delay, fairness, and reliability.

Abstract

The coexistence of heterogeneous service classes in 5G Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communication (URLLC), and Massive Machine-Type Communication (mMTC) poses major challenges for meeting diverse Quality-of-Service (QoS) requirements under limited spectrum and power resources. Existing radio access network (RAN) slicing schemes typically optimise isolated layers or objectives, lacking physical-layer realism, slot-level adaptability, and interpretable per-slice performance metrics. This paper presents a joint optimisation framework that integrates Dynamic Hybrid Resource Utilisation with MCS-Based Intelligent Layering, formulated as a mixed-integer linear program (MILP) that jointly allocates bandwidth, power, and modulation and coding scheme (MCS) indices per slice. The model incorporates finite blocklength effects, channel misreporting, and correlated fading to ensure realistic operation. Two modes are implemented: a Baseline Mode that ensures resource-efficient QoS feasibility, and an Ideal-Chaser Mode that minimises deviation from ideal per-slice rates. Simulation results show that the proposed approach achieves energy efficiencies above $10^7$~kb/J in Baseline Mode and sub-millisecond latency with near-ideal throughput in Ideal-Chaser Mode, outperforming recent optimisation and learning-based methods in delay, fairness, and reliability. The framework provides a unified, interpretable, and computationally tractable solution for dynamic cross-layer resource management in 5G and beyond networks.