Digital Twin-Assisted Load-Balanced User Association and AoI-Aware Scheduling in IoT Networks

In this paper, we propose a digital twin (DT) assisted-user device (UD)-base station (BS) association for cellular-supported internet of things (IoT) networks, and scheduling of uplink transmission of these devices to their associated BSs considering the age of information (AoI). Unlike existing work, we consider the scenario of a main base station (MBS) and several smaller base stations (SBSs), which is suitable for 5G and beyond 5G technology. We propose a novel Q-learning-inspired algorithm for simultaneous uplink throughput maximization and load balancing among the different BSs to associate the UDs away from the MBS and distribute them evenly between the SBSs as much as possible. We further take into account the maximum UD-association limit and limited coverage radius of each BS, which makes it closer to a realistic scenario. Another contribution is the proposal and analysis of heuristics-based scheduling of the UDs transmission to their respective BSs with the aim to reduce the overall AoI. Scheduling is important as many applications in the IoT networks require near-time or near-real-time updates for taking timely decisions and maintaining acceptable performance and safety levels. The AoI is an effective metric for the timeliness of information at a receiver, and the computational simplicity of the heuristics-based approach ensures that the scheduling decisions are taken promptly. Simulations results and comparison with several benchmark methods show that the proposed solution can achieve load balancing with a slight degradation in the cumulative maximum achievable throughput achieved in the network. The results also provide valuable insights into the joint performance of the proposed model and the benchmark UD-BS association and scheduling solutions with different packet generation rates for transmission by the UDs.