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Device-to-device (D2D) links scheduling for avoiding excessive interferenceis critical to the success of wireless D2D communications. Most of thetraditional scheduling schemes only consider the maximum throughput or fairnessof the system and do not consider the freshness of information. In this paper,we propose a novel D2D links scheduling scheme to optimize an age ofinformation (AoI) and throughput jointly scheduling problem when D2D linkstransmit packets under the last-come-first-serve policy with packet-replacement(LCFS-PR). It is motivated by the fact that the maximum throughput schedulingmay reduce the activation probability of links with poor channel conditions,which results in terrible AoI performance. Specifically, We derive theexpression of the overall average AoI and throughput of the network under thespatio-temporal interfering queue dynamics with the mean-field assumption.Moreover, a neural network structure is proposed to learn the mapping from thegeographic location to the optimal scheduling parameters under a stationaryrandomized policy, where the scheduling decision can be made without estimatingthe channel state information(CSI) after the neural network is well-trained. Toovercome the problem that implicit loss functions cannot be back-propagated, wederive a numerical solution of the gradient. Finally, numerical results revealthat the performance of the deep learning approach is close to that of a localoptimal algorithm which has a higher computational complexity. The trade-offcurve of AoI and throughput is also obtained, where the AoI tends to infinitywhen throughput is maximized.

Age of Information-based Scheduling for Wireless D2D Systems with a Deep Learning Approach