A situational awareness-based framework for wireless network management: Innovations and applications

Wireless networks support numerous terminals, manage large data volumes, and provide diverse services, but the vulnerability to environmental changes leads to increased complexity and costs. Situational awareness has been widely applied in network management, but existing methods fail to find optimal solutions due to the high heterogeneity of base stations, numerous metrics, and complex intercell dependencies. To address this gap, this paper proposes a specialized framework for wireless networks, integrating an evaluation model and control approach. The framework expands the indicator set into four key areas, introduces an evaluation method, and proposes the indicator perturbation greedy (IPG) algorithm and the adjustment scheme selection method based on damping coefficient (DCSS) for effective network optimization. A case study in an urban area demonstrates the framework's ability to balance and improve network performance, enhancing situational awareness and operational efficiency under dynamic conditions.