A Low-Complexity Deep Neural Network for Signal-to-Interference-Plus-Noise Ratio Estimation
Resumo
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.
Referências
Baumgartner, S., Hirtz, G., and Baumgartner, A. (2014). A modified maximum likelihood In 2014 IEEE International method for SNR estimation in OFDM based systems. Conference on Consumer Electronics (ICCE), pages 155–158.
Bin Li, DiFazio, R., and Zeira, A. (2002). A low bias algorithm to estimate negative SNRs in an AWGN channel. IEEE Communications Letters, 6(11):469–471.
Khan, A. M., Jeoti, V., Rehman, M., and Jilani, M. (2017). Noise power estimation for broadcasting OFDM systems. In 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), pages 1–6.
Malik, S., Portugal, S., Seo, C., Kim, C., and Hwang, I. (2011). Proposal and performance analysis of a novel preamble-based SNR estimation algorithm. In 2011 34th International Conference on Telecommunications and Signal Processing, pages 100–103.
Ngo, T., Kelley, B., and Rad, P. (2020). Deep learning based prediction of signal-tonoise ratio for LTE and 5G systems. In 2020 8th International Conference on Wireless Networks and Mobile Communications (WINCOM), pages 1–6.
