Task Delay Minimization for Mobile Edge Generation in D2D Underlaying Cellular Network

A novel mobile edge generation (MEG) framework is proposed to support latency-sensitive generation tasks in the social-aware device-to-device (D2D) underlaying cellular network. Within this framework, user devices (UDs) and base station (BS) are organized into socially cohesive communities based on content preference and spatial proximity, enabling cooperative generation tasks via both cellular and intra-community D2D communications. A joint seed-and-content based BS-D2D (JSCB) transmission protocol is proposed to dynamically orchestrate the transmission mode between seed acquisition with local generation and direct content sharing across multiple consecutive task rounds, incorporating the spillover mechanism for handling overdue transmissions. Based on this protocol, an average task delay minimization problem is formulated to jointly optimize the UD association between cellular and D2D communication, transmission mode, D2D pairing, and BS-side beamforming. To efficiently solve the hybrid and temporally coupled problem, a joint matching and proximal policy optimization (JMPPO) algorithm is developed, where the discrete and continuous actions are decoupled with specialized modules though a hierarchical deep reinforcement learning and matching design. Numerical results validate that 1) the JSCB protocol reduces delay through adaptive transmission scheduling and cellular/D2D coordination; 2) the JMPPO algorithm outperforms both learning-based and traditional baselines in terms of average delay under the spillover and hybrid action scenarios; 3) the proposed schemes demonstrate robustness across diverse network and system conditions.