Proactive Uplink Access Scheduling With Differently Outdated States Information in IoT Networks

This paper aims to develop an effective uplink access scheduling strategy for massive Internet-of-Things (IoT) networks. To better reap the benefits of uplink resources, the BS has to adjust the uplink resources relies on the network states available at the BS. However, in massive IoT networks, the acquisition of network states, including traffic arrivals, channel conditions, and energy supply rate, are typically obtained through in-band feedback from devices. Therefore, the network states available at the BS are differently outdated across devices, as the staleness depends on the time elapsed since each device was last scheduled. This motivates us to develop a proactive scheduling scheme that enables the BS to schedule uplink access under differently outdated states information. To combat the performance loss caused by the outdated states information, we propose a novel primal-dual online learning framework. This framework leverages mini-batch gradient descent for dual updates and employs Online Convex Optimization for proactive primal updates, which effectively predicting current network states based on outdated knowledge. We evaluate the performance of the proposed proactive scheduling scheme against the offline optimum, which is optimized using prior knowledge of network states. The performance analysis shows that the proactive scheme asymptotically approaches to the offline optimum. Simulation results further validate the effectiveness of the proposed algorithm by comparing to other benchmarks.