Efficient Approximation of SINR and Throughput in 5G NR via Sparsity and Interference Aggregation

This paper presents a novel approach to scheduling resources in a multi-beam next-generation Node B (gNB) that enables efficient resource reuse across beams within a transmission time interval (TTI). Unlike traditional medium access control (MAC) scheduling, which focuses on resource allocation within a single beam, our approach considers the simultaneous scheduling of multiple beams. We leverage a recently introduced sparse model and propose an algorithm that avoids exhaustive Monte Carlo (MC) simulation while approximating signal-to-interference-plus-noise ratio (SINR) and achievable throughput parameters in snapshot-based simulations. This approximation significantly reduces computational complexity while maintaining negligible error. We validate our approach through extensive simulations, demonstrating its effectiveness in approximating SINR and achievable throughput.