Service-Aware User Association and Resource Allocation in Integrated Terrestrial and Non-Terrestrial Networks: A Genetic Algorithm Approach

In 6G networks and beyond, multiple radio access networks (RANs), including; the satellite, high altitude platforms, low altitude platforms, and the terrestrial network, will co-exist. These networks are characterized by different capabilities and limitations in meeting the envisioned 6G contrasting user requirements. Therefore, associating users with the appropriate radio access network (RAN) in such an integrated network is rigorous and complex. In this work, the user association and resource allocation problem is formulated as a multi-objective optimization problem (MOOP), aiming to maximize data rate while minimizing mobility-induced handoff in the integrated network. Moreover, the problem is formulated in such a way as to prioritize the service provisioning of mission-critical users. The weighted sum method is adopted to simplify and transform the MOOP into a single-objective optimization problem (SOOP). In order to solve the formulated NP-hard SOOP, a genetic algorithm (GA) whose fitness value is based on the user's service group is proposed. The performance of the proposed algorithm is evaluated by comparing it to the optimal solution, the greedy signal-to-interference-plus-noise ratio (SINR) based association, and the random user association algorithms. Simulation results show that as the number of access nodes in the network increases, the GA's spectrum efficiency (SE) remains within 0.4% of the optimal solution. Moreover, the GA outperforms all three schemes in user acceptance ratio (AR) and handoff probability.