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The cooperative network, eg relay network and RIS network, is known to improve significantly the reliability in wireless communications. However, the upcoming Sixth Generation (6G) wireless networks requires not only high reliability, but also low-latency and high security. This challenge brings high-complexity in designing a proper optimization for future cooperative networks. As an emerging technology, deep learning plays an important role in solving complicated optimization problems in wireless communications. Compared with the conventional approaches, deep learning has remarkable power to deal with complicated optimization problems with low-complexity. Therefore, this thesis utilizes deep learning technologies to design the proper resource allocation such as relay selection, reconfigurable intelligent surfaces (RIS) coefficients optimization, power allocation and hybrid orthogonal multiple access (OMA)/non-orthogonal multiple access (NOMA) selection in cooperative networks.

First, to maximize the throughput with delay and secrecy constraint, deep reinforcement learning (DRL) is used to optimize the relay selection strategy in bufferaided relay networks. To further improve the outage performance, a novel decisionassisted DRL method is proposed based on the a-priori information in the bufferaided relay system. Compared with traditional DRL methods, the proposed novel algorithm reduces the exploration dimension and the impact of bad actions in the training. Moreover, the proposed algorithm can be used to solve more complex problems such as joint relay selection and power allocation, joint relay selection and OMA/NOMA …