A Design and Development of Effective Ant-Based Optimization and Markov Decision Problem (MDP) Analysis in Satellite Communications

This technique simulates ants searching for food and the use of pheromones to find the best solutions; thus, by doing this, a balance of exploration and exploitation is achieved to solve optimization problems. The Effective Ant-based Optimisation and Markov Decision Problem (EAOMSC) model provides a complex framework for dynamic spectrum sharing and resource optimisation in Ka-band satellite communication systems. It can extract Integer Cohesion data from Geostationary (GSO) and Non-Geostationary (NGSO) satellites, along with terrestrial 5G networks. The communication environment is captured in the form of a Markov Decision Process (MDP), which signifies network states and makes it easier to make decisions concerning data transmissions. An Improved Ant Colony Optimisation (IACO) algorithm also helps in this by optimising resource scheduling through a pheromone updating mechanism to achieve the best load distribution and efficiency. The performance analysis is calculated packet error rate, SINR, average utilisation of contact time, satellite system capacity, and terrestrial system capacity.