Adaptive Routing Protocols for QoS-Aware Communication in 5G Mobile Networks: A Review and Research Perspectives

Authors: Seema Rani

Country: India

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Abstract: The rapid deployment of fifth-generation (5G) mobile networks has created new requirements for efficient, reliable, and adaptive routing. Unlike previous generations of mobile networks, 5G is expected to simultaneously support enhanced Mobile Broadband (eMBB), Ultra-Reliable Low-Latency Communications (URLLC), and massive Machine-Type Communications (mMTC). These services impose substantially different requirements in terms of bandwidth, latency, reliability, packet loss, energy consumption, and network availability. Consequently, conventional routing protocols based primarily on hop count or shortest-path criteria are often insufficient for heterogeneous and highly dynamic 5G environments. This paper presents a review of adaptive and QoS-aware routing approaches for 5G mobile networks. The review focuses on multi-parameter routing, stable route selection, congestion-aware routing, reinforcement-learning-based routing, and QoS prediction-based route selection. Recent developments from 2021 to 2025 are analyzed to identify the major approaches, performance objectives, and limitations of existing methods. A comparative framework is presented based on routing metrics, adaptation mechanisms, supported QoS requirements, and application scenarios. The review further identifies important research gaps related to real-time QoS prediction, intelligent route selection, network slicing, mobility, scalability, routing overhead, and energy efficiency. Finally, an adaptive QoS-aware routing framework is proposed that combines real-time network monitoring, multi-criteria route evaluation, service-aware routing, and machine learning-based prediction. The study provides a foundation for developing intelligent routing protocols for 5G and future beyond-5G mobile networks.

Keywords: 5G mobile networks, adaptive routing, QoS-aware routing, intelligent routing, machine learning, reinforcement learning, network slicing, route selection, 5G QoS

Paper Id: 233200

Published On: 2026-09-06

Published In: Volume 14, Issue 5, September-October 2026

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