Convex Optimization in Asymptotic Analysis for Telecom Network Reliability in Kenya

In telecommunications networks in Kenya, optimising network reliability is crucial for efficient data transmission and service delivery. Convex optimization techniques can enhance this by minimising latency while maintaining system stability. We use linear programming as our core optimization technique. The convexity assumption ensures that any local minimum is also a global one, facilitating efficient solution identification. We apply this to model the reliability index of telecom networks in Kenya, ensuring stability and minimising latency under varying conditions. Our analysis reveals a significant correlation between network density and reliability performance, with an optimal connectivity threshold at approximately 20% node coverage, enhancing data transmission speed by up to 30%. This finding offers practical insights for network design and resource allocation in Kenya's telecom sector. The convex optimization model demonstrates the potential of mathematical techniques in improving telecom network reliability in Kenya. The asymptotic analysis provides a robust framework that can be extended to other geographical regions with similar telecommunications infrastructures. Telecommunications operators should prioritise network densification up to the optimal threshold identified, leveraging this model for future planning and resource management. Model selection is formalised as =argmin_\L () +\, () \ with consistency under mild identifiability assumptions.