Open AccessA Low-Complexity Deep Neural Network for Signal-to-Interference-Plus-Noise Ratio Estimation
Author(s) -
Roberto Kagami,
Luciano Leonel Mendes
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/w6g.2021.17227
Subject(s) - computer science , artificial neural network , reliability (semiconductor) , signal to noise ratio (imaging) , quality of service , interference (communication) , throughput , key (lock) , electronic engineering , channel (broadcasting) , real time computing , power (physics) , computer network , telecommunications , artificial intelligence , engineering , wireless , physics , quantum mechanics , computer security
Mobile network technology has been driven by a huge demand for throughput and reliability to support new emerging services. The quality of service is based on measurements of indicators with a high level of precision. Accurate controlling of parameters to fulfil the quality requirements will be essential for future applications. In LTE and 5G standards, the Channel Quality Indicator can be calculated using different algorithms. It is key to determine the best coding and modulation as well as the power control. Thus, it depends on the exact signal-to-noise ratio estimation. MSE based on hard-decision has a very low computational cost, however, it can insert non-linearities. This paper proposes a neural network to estimate an SINR from a modified MSE function.