Neural Network Signal Processing for LTE Receiver Application

Radio Digital Signal Processing (DSP) elements work well in the conditions for which they are designed, but degrade in edge cases outside of these conditions. In previous work we built individual physics-driven neural network (NN) models to replace standard DSP function models in an LTE-like intelligent receiver. This prevents performance degradation in the edge conditions while giving the same or better performance than the DSP models. In this work we extend these individual NN models to a more realistic LTE receiver with multiple antenna support and stitch together the individual NN models to form a complete end-to-end pipeline. We report results for these NN models as well as the complete pipeline, comparing with standard DSP element analogues using both channel bit error rate (CBER) and bit error rate (BER). The individual NN models perform equal or better than DSP blocks in the more realistic and difficult LTE receiver conditions and the end-to-end pipeline provides a much better performance improvement over a DSP pipeline, providing a modular architecture that can be used in other applications.

I. Introduction

Digital Signal Processing (DSP) elements in radios work well in the conditions for which they are designed, but degrade in edge cases outside of these conditions, e.g. a longer delay spread than expected. If the system has not been designed for the specific edge case, it causes degraded operation and the system has to go back to the design phase to handle the new requirements, as shown in top part of Figure 1 . Our solution, Signal Processing Intelligent Receiver with AI Learning (SPIRAL), developed under the DARPA SPiNN (Signal Processing in Neural Networks) program, uses physics driven Neural Networks (NN) models to alleviate this issue without compromising on desired performance, as shown in bottom part of Figure 1 . In previous work [18] we built individual physics-driven neural network (NN) models to replace standard DSP function models in an LTE-like intelligent receiver. This prevents performance degradation in the edge conditions while giving the same or better performance than the DSP models.