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Spectrum sharing is a method to solve the problem of frequency spectrumdeficiency. This paper studies a novel AI based spectrum sharing and energyharvesting system in which the freshness of information (AoI) is guaranteed.The system includes a primary user with access rights to the spectrum and asecondary user. The secondary user is an energy harvesting sensor that intendsto use the primary user spectrum opportunistically. The problem is formulatedas partially observable Markov decision processes (POMDPs) and solved using twomethods: a deep Q-network (DQN) and dueling double deep Q-Network (D3QN) toachieve the optimal policy. The purpose is to choose the best action adaptivelyin every time slot based on its situation in both overlay and underlay modes tominimize the average AoI of the secondary user. Finally, simulation experimentsare performed to evaluate the effectiveness of the proposed scheme compared tothe overlay mode. According to the results, the average AoI in the proposedsystem is less than that of the existing models, including only overlay mode.The average user access improved from 30% in the overlay mode to 45% in the DQNand 48% in the D3QN.

AoI Minimization in Energy Harvesting and Spectrum Sharing Enabled 6G Networks