Deep-Neural-Network based Fall-back Mechanism in Interference-Aware Receiver Design
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.
Keywords
Cite
@article{arxiv.1905.10890,
title = {Deep-Neural-Network based Fall-back Mechanism in Interference-Aware Receiver Design},
author = {Sha Hu and Dzevdan Kapetanovic and Neng Wang and Wenquan Hu},
journal= {arXiv preprint arXiv:1905.10890},
year = {2019}
}
Comments
5 pages, 7 figures, submitted