What question did this study set out to answer?

This work aims to develop a deep learning framework for enhancing semantic communication in wireless systems.

May 11, 2026Open Access

Intelligent Wireless Communication Using A Semantic-Aware Deep Learning Approach

1S1Miss. A. G. Salve DAD. U. Adokar DPDr. P. M. Patil

Key Points

This work aims to develop a deep learning framework for enhancing semantic communication in wireless systems.
Integrated NLP and semantic encoding with MQTT for IoT actuation.
Utilized Sentence-BERT for generating embeddings, compressed using a lightweight autoencoder.
Simulated channel conditions with Gaussian noise.
Achieved cosine similarity >0.95 for paraphrases and >0.90 under noise conditions.
Intent recognition accuracy of 0.9886 for 'Light On' and 0.9782 for 'Light Off'.
Demonstrated reliable low-latency control in real-time validation using MQTT and ESP32.

Abstract

The evolution of intelligent wireless systems requires moving from bit-level transmission to semantic communication. This work proposes a semantic-aware deep learning framework integrating NLP, semantic encoding, MQTT communication, and IoT actuation. Sentence-BERT generates 384-dimensional embeddings, compressed to a 32-dimensional latent space (12:1 ratio) using a lightweight autoencoder. Gaussian noise (σ = 0.1, 0.2) simulates realistic channel conditions. The model is compact (395 KB), with 98,688 parameters and 106,496 FLOPs, enabling efficient edge deployment. Results show high semantic preservation, with cosine similarity >0.95 for paraphrases and >0.90 under noise. Intent recognition achieves high confidence (0.9886 for "Light On" and 0.9782 for "Light Off"). Real-time validation using MQTT and ESP32 confirms reliable, low-latency control. The framework offers scalability, noise robustness, and spectral efficiency, making it suitable for 6G semantic communication systems.

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