A Cross‐Layer Spatial–Spectral Optimization Framework for Hybrid Routing and Load‐Balanced Channel Allocation in Intelligent Networks

Abstract

ABSTRACT Intelligent cross‐layer resource management mechanisms are required by the rapid development of 5G and new 6G networks, as they are heterogeneous in traffic requirements, ultra‐dense in deployment, and dynamic spectrum conditions. Traditional routing and fixed channel allocation schemes are independent and are usually associated with congestion hotspots, resource allocation imbalance, poor latency, and low reliability in multi‐hop wireless networks. A single cross‐layer framework is proposed in this paper that combines a Topology‐Optimized Hybrid Routing Protocol (TOHRP) and a Load‐Balanced Channel Allocation (LBCA) system in intelligent cellular networks that utilize the Dynamic Spectrum Access (DSA). The suggested method is a collaborative effort to optimize the spatial traffic and spectral resource allocation with the inclusion of congestion‐sensitive routing metrics, topology awareness, interference sensitivity, and adaptive channel selection. Multi‐metric routing cost function and load‐sensitive channel utility model are designed to optimize the throughput and fairness as well as stability in queues to meet the heterogeneous service needs of eMBB, URLLC, and mMTC traffic. Large‐scale NS‐3 simulations of the proposed framework show that the framework can achieve at most 30.5% throughput improvement, 53.6% lower latency, 15.8% increased packet delivery ratio, 74.8% reduced channel load fluctuation, and 38.5% increased energy efficiency in dense network conditions. The findings confirm the fact that spatial‐spectral optimization through coordination is a highly effective approach that can improve Quality of Service (QoS), scalability, and resilience, and therefore, the proposed framework can be used in the next generation of intelligent cellular, Internet of Things, and mission‐critical communication systems.