# Deep-Neural-Network based Fall-back Mechanism in Interference-Aware Receiver Design

## TL;DR

This paper proposes a DNN-based fall-back mechanism for interference-aware receivers that dynamically switches between interference mitigation methods to optimize performance and computational efficiency.

## Contribution

It introduces a novel DNN approach to determine when to activate SLIC or fall back to eIRC based on MF detection reliability, outperforming traditional methods.

## Key findings

- DNN-based mechanism reduces error rate compared to traditional methods.
- The approach saves computational cost by selectively activating complex interference cancellation.
- The proposed method effectively improves receiver performance in interference scenarios.

## Abstract

In this letter, we consider designing a fall-back mechanism in an interference-aware receiver. Typically, there are two different manners of dealing with interference, known as enhanced interference-rejection-combining (eIRC) and symbol-level interference-cancellation (SLIC). Although SLIC performs better than eIRC, it has higher complexity and requires the knowledge of modulation-format (MF) of interference. Due to potential errors in MF detection, SLIC can run with a wrong MF and render limited gains. Therefore, designing a fall-back mechanism is of interest that only activates SLIC when the detected MF is reliable. Otherwise, a fall-back happens and the receiver turns to eIRC. Finding a closed-form expression of an optimal fall-back mechanism seems difficult, and we utilize deep-neural-network (DNN) to design it which is shown to be effective and performs better than a traditional Bayes-risk based design in terms of reducing error-rate and saving computational-cost.