Robust URLLC-eMBB coexistence in 5G networks: an attention-enhanced meta-reinforcement learning framework

The simultaneous presence of URLLC and eMBB services in 5G brings about a problem of resource allocation. On the one hand, URLLC requires guaranteed low latency and reliability, whereas on the other hand, eMBB needs guaranteed high throughput and fairness. This paper addresses the problem of dynamic scheduling of orthogonal resource blocks in a single-cell environment, in order to decrease URLLC violations and increase eMBB throughput and fairness under the conditions of time-varying channels, bursty traffic and traffic distribution changes. Attention-enhanced meta-graph reinforcement learning algorithm is proposed. Dynamic contention graph represents competition for the resources at the MAC layer level; permutation-equivariant GATv2 encoder learns users’ interactions, and model-agnostic meta-learning allows to adapt the model quickly to new traffic. The scheduler selects the most prioritized user, and the rest of the resources are allocated using proportional-fair scheduling algorithm. Under the nominal evaluation condition, the proposed method achieves a URLLC violation rate of 63.11%, compared with 90.54% for DQN and 79.52% for PPO, while attaining a Jain’s fairness index of 0.945.