Тег: radio access network management

Аннотация: (English) The rapid coexistence of legacy and modern radio technologies has transformed contemporary cellular infrastructure into a layered communication environment where heterogeneous systems such as GSM, UMTS, and LTE operate within a shared spectral and interference space. Under these circumstances, traditional optimization techniques that treat individual base stations or isolated network segments as independent control units increasingly reveal their limitations. Fragmented parameter tuning frequently leads to local improvements that inadvertently propagate inefficiencies elsewhere in the network topology. Addressing this challenge requires analytical frameworks capable of perceiving the radio access network as an interconnected organism whose performance emerges from the collective behavior of clusters of cells rather than from the configuration of a single node. This study proposes an integrated multi-layer optimization approach that synchronizes cluster-level network analysis with cell-level parameter adjustment within a unified feedback-driven control structure. The suggested method introduces a mathematical model that simultaneously evaluates key performance indicators, including throughput, latency, resource utilization, and user-perceived service quality, while dynamically adapting operational parameters such as transmission power, antenna configuration, and load distribution across neighboring cells. Unlike conventional optimization strategies that operate within a single analytical scale, the proposed framework interweaves macro-level traffic equilibrium with micro-level radio control decisions. To investigate the practical viability of the model, a dedicated analytical software environment was developed using Python and applied to realistic signal measurements from the Cellular Network Analysis Dataset. The experimental evaluation demonstrates that the integrated optimization process enables a more balanced redistribution of network load, improves the stability of key performance indicators, and enhances overall service quality across heterogeneous radio layers. The results indicate that coupling cluster-oriented analytics with adaptive cell-level control creates a synergistic optimization effect that cannot be achieved through traditional single-layer methods. The proposed approach contributes to the ongoing development of intelligent radio access network management by offering a structured pathway toward automated optimization of heterogeneous cellular infrastructures, particularly during transitional phases where legacy technologies coexist with newer broadband systems.