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.
The fast-moving evolution of new technologies is driving the future mobile networks to a challenging pursuit for high throughput and reliability. Emerging services will need precise indicators to address contrasting requisites and link adaptation is a cardinal element. The received signal measurements must provide complete information to perform appropriate and timely decisions. Most communication systems employ statistics based on error rates, but it is not sufficient to accomplish all requirements. Additional information is necessary to define the best parametrization for each application. This paper indicates an approach to achieve this goal by monitoring processes directly into the physical layer decoding.
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