Efficient Approximation of SINR and Throughput in 5G NR via Sparsity and Interference Aggregation
- Resource Type
- Conference
- Authors
- Rezaei, Amir; Schulz, Philipp; Ganesan, Rakash SivaSiva; Awada, Ahmad; Viering, Ingo; Fettweis, Gerhard
- Source
- 2023 IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC) Personal, Indoor and Mobile Radio Communications (PIMRC), 2023 IEEE 34th Annual International Symposium on. :1-7 Sep, 2023
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Robotics and Control Systems
Signal Processing and Analysis
Radio frequency
Monte Carlo methods
Processor scheduling
5G mobile communication
Interference
Throughput
Approximation algorithms
Multi-beam
scheduler
5G NR
Monte Carlo
linear programming
sparse solution
- Language
- ISSN
- 2166-9589
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.