Hybrid Sensitivity and Residual Subspace Refinement-Based Scheduling in Massive IoT

Abstract

With the exponential increase in Internet of Things (IoT) deployments, advanced resource scheduling is needed to address massive connectivity and strict quality of service requirements. In this paper, we propose a hybrid sensitivity and residual subspace refinement (HSRSR) method for protocol-aware coordination between uplink control information reporting and downlink control information scheduling. HSRSR integrates sensitivity driven subspace selection for feedback compression and residual based subspace determination for dynamic scheduling. Sensitivity analysis extracts dominant eigenmodes from channel covariance matrices to preserve mutual information for modulation and coding scheme adaptation. Residual based prioritization adapts resource block group allocations and beamforming by selecting active constraints according to their Lagrange multiplier magnitudes. Compared with codebook based scheduling and heuristic interference coordination based scheduling, HSRSR achieves a sum rate gain of up to 46% under moderate delay constraints with a channel quality indicator quantization granularity of zero point one bits per hertz. Additionally, HSRSR reduces computational complexity from cubic to linear scaling with the number of devices, enabling real-time scheduling in massive IoT networks. Furthermore, it outperforms these baselines while maintaining protocol-compliant resource allocation.