Convolutional Neural Networks for Space-Time Block Coding Recognition

TL;DR

This paper introduces a convolutional neural network-based algorithm for recognizing space-time block codes in radio signals, capable of operating without prior channel information and effective at low SNRs.

Contribution

It presents a novel CNN-based identification method for space-time block coding that does not require prior channel or noise information, suitable for noncooperative scenarios.

Findings

Performs well at low SNRs

No prior channel information needed

Effective in noncooperative contexts

Abstract

We apply the latest advances in machine learning with deep neural networks to the tasks of radio modulation recognition, channel coding recognition, and spectrum monitoring. This paper first proposes an identification algorithm for space-time block coding of a signal. The feature between spatial multiplexing and Alamouti signals is extracted by adapting convolutional neural networks after preprocessing the received sequence. Unlike other algorithms, this method requires no prior information of channel coefficients and noise power, and consequently is well-suited for noncooperative contexts. Results show that the proposed algorithm performs well even at a low signal-to-noise ratio