Sustainable Massive Connectivity Maximization Scheduling Scheme for 6G

This paper proposes a scalable and energy-efficient scheduling framework tailored for massive machine-type communications (mMTC) in future 6G networks. Motivated by the dual challenges of massive connectivity and strict energy constraints, we introduce a new cooperative multi-cell uplink scheme that jointly optimizes user scheduling, power allocation, and interference management under finite block length (FBL) constraints. While prior studies have explored energy efficiency (EE) or connectivity separately, they often neglect FBL effects and lack scalable, interference-aware coordination across multi-cell NOMA deployments. Leveraging non-orthogonal multiple access (NOMA), our framework addresses both intra- and intercell interference through a multi-objective optimization problem that simultaneously maximizes EE and connectivity. To solve the problem, we develop the Sustainable and Reliable Scheduling for Massive Machine Type Communication using NOMA (SRS-MTCN) algorithm, integrating channel-aware user clustering, interference-aware resource allocation, and iterative power optimization. Simulation results demonstrate that the proposed approach performs significantly better than state-of-the-art baselines in dense deployments, achieving up to 35 – 58% EE improvement in medium-density regimes (16 – 32 devices/m2), while ensuring robust scalability and reliability in ultra-dense scenarios exceeding 40 devices/m2. These results illustrate its suitability for sustainable 6G mMTC networks operating under FBL and energy constraints.