LAUSR

Data collection and manipulation from real environments are two tasks that, in a well-organized way, can support the next generation of wireless networks (NGWNs) in resource allocation-related tasks such as spectrum frequency and transmission power. Due to the complexity of NGWNs, several metaheuristics have been proposed to help in these operations. However, a large number of these studies present solutions using only one algorithm or procedure, i.e., the research uses just one metaheuristic to resolve the radio resource allocation problem. This fact tends to lead to a loss of performance because, in this way, there is no considerable variability in the search strategies for better solutions. To tackle this problem, we propose a framework that maximizes the total throughput of an NGWN that implements the multi-carrier cell-less non-orthogonal multiple access (MC-CL-NOMA) architecture. The framework executes this activity using scenario information to feed an ensemble metaheuristic method. The empirical results show that the framework presents better performance than utilizing one metaheuristic individually. Moreover, the proposed method outperforms algorithms applicable in future 6G NOMA scenarios.