Network-assisted full-duplex (NAFD) cell-free (CF) massive MIMO systems enable simultaneous uplink and downlink transmissions, where interference suppression and beamforming are critical for improving spectral efficiency and system performance. However, the asymmetric time-varying properties of current network traffic, coupled with the interference problems associated with complex network topologies, make existing resource allocation and interference management strategies difficult to handle, and unable to satisfy the low-latency, high-reliability Quality-of-Service (QoS) requirements of the growing number of terminal devices (TDs). To address these challenges, we propose a novel access method based on decoupled uplink-downlink multi-connectivity transmission to achieve flexible access selection and formulate an optimization problem that maximizes the cumulative fair spectral efficiency by simultaneously optimizing power allocation and link scheduling. To solve this mixed-integer nonlinear programming (MINLP) problem, we propose an optimized transfer scheme that reduces the dimensionality of the action and constraint spaces. Then, we characterize the network states as heterogeneous graph structures and employ node-level and metapath-level attention mechanisms for message passing and aggregation, and obtain graph-level scheduling policy via the heterogeneous graph neural network (HGNN). Finally, in light of the superior performance of Deep Reinforcement Learning (DRL) in exploration-based tasks, we design a holistic updating mechanism using environmental feedback and advantage state-action function, named as HGN-DRL for this end-to-end learning framework. Simulation results demonstrated the effectiveness and scalability of HGN-DRL in large-scale cell-free scenarios.

In the digital divide regions, the edge computing can improve the performance of application services for the Internet of Things (IoT) devices. However, the lagging of information and communication technology (ICT) results in congested access spectrum and imbalanced computational load. Moreover, the mobility of IoT devices further exacerbates the fluctuating quality of communication links and the frequent changing of access positions. So, how to realize the reliable service requirements of devices in a heterogeneous environment with multiscale constraints should be considered appropriately and comprehensively. In this article, we model a relay-assisted multiaccess edge computing (MEC) framework, employing multihop transmission to enable the cross-domain service coverage. Under this framework, we formulate a quantitative model to characterize communication and computation processes within task migration, and derive analytical results for service latency. To improve the access resource efficiency, we adopt a joint nonorthogonal multiple access (NOMA) scheme to extend the transmission dimension, and employ proportional fairness to dynamically allocate resources. Besides, we propose a multiagent deep reinforcement learning (DRL) for optimizing the long-term task offloading scheduling, address the optimization problem of maximizing the system throughput efficiency. And we improve the action exploration and output dimensions of DRL to achieve convergence and performance enhancement. Simulation and analytical results show that our proposed algorithm outperforms the comparison algorithms in the key performance indicators.