Error Correction in Bluetooth Low Energy via Neural Network with Reject Option

TL;DR

This paper introduces a neural network-based error correction method for Bluetooth Low Energy that improves data transmission reliability and image quality.

Contribution

A novel error correction approach using a neural network with reject option for Bluetooth Low Energy, leveraging CRC redundancy without modifying the transmitter.

Findings

The method achieved 94–98% correction rates for single-bit errors and 54–68% for double-bit errors.

It reduced packet retransmissions and data loss risks in wireless communication.

Image quality improved for signal-to-noise ratios between 9 and 11 dB.

Abstract

This paper presents an approach to error correction in wireless communication systems, with a focus on the Bluetooth Low Energy standard. Our method uses the redundancy provided by the cyclic redundancy check and leaves the transmitter unchanged. The approach has two components: an error-detection algorithm that validates data packets and a neural network with reject option that classifies signals received from the channel and identifies bit errors for later correction. This design localizes and corrects errors and reduces transmission failures. Extensive simulations were conducted, and the results demonstrated promising performance. The method achieved correction rates of 94–98% for single-bit errors and 54–68% for double-bit errors, which reduced the need for packet retransmissions and lowered the risk of data loss. When applied to images, the approach enhanced visual quality compared with baseline methods. In particular, we observed improvements in visual quality for signal-to-noise ratios between 9 and 11 dB. In many cases, these enhancements were sufficient to restore the integrity of corrupted images.

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