An automated optimization and machine learning method for a deep-learning-based network controller of a heterogenous network, comprising: obtaining (S405) sets of input data and an objective, wherein the sets of input data include at least one of channel gains, measurements of received power, measurements of interference, power-related information, an available bandwidth, quality of service, QoS, parameters, congestion and load information , wherein the objective is a QoS objective including at least one of targeted data rates, a latency, a reliability, a data loss rate, an out-of-order delivery and round-trip time; generating (S410) sets of output data for the sets of input data in accordance with the objective, using high performance machine learning, ML, techniques, in particular Genetic Algorithms; generating (S415) a mapping of the sets of input data to the sets of output data, wherein the mapping is used to carry out online quality network optimization based on unseen data, including estimated SINR values during operation of the heterogenous network; and offline training (S420) the deep-learning-based network controller using the mapping, characterized in that the deep-learning-based network controller comprises a plurality of Feed Forward Neural Networks, wherein each Feed Forward Neural Networks is trained on the set of input data and one individual bit of the output data, wherein an individual bit is a schedulable element, which is a representation of an actual resource element in an available signal bandwidth. where 1 indicates using the schedulable element and 0 indicates not using the schedulable element for a particular user. obtaining (S405) sets of input data and an objective, wherein the sets of input data include at least one of channel gains, measurements of received power, measurements of interference, power-related information, an available bandwidth, quality of service, QoS, parameters, congestion and load information , wherein the objective is a QoS objective including at least one of targeted data rates, a latency, a reliability, a data loss rate, an out-of-order delivery and round-trip time; generating (S410) sets of output data for the sets of input data in accordance with the objective, using high performance machine learning, ML, techniques, in particular Genetic Algorithms; generating (S415) a mapping of the sets of input data to the sets of output data, wherein the mapping is used to carry out online quality network optimization based on unseen data, including estimated SINR values during operation of the heterogenous network; and offline training (S420) the deep-learning-based network controller using the mapping, characterized in that the deep-learning-based network controller comprises a plurality of Feed Forward Neural Networks, wherein each Feed Forward Neural Networks is trained on the set of input data and one individual bit of the output data, wherein an individual bit is a schedulable element, which is a representation of an actual resource element in an available signal bandwidth. where 1 indicates using the schedulable element and 0 indicates not using the schedulable element for a particular user. <EOS>