A Deep Learning Framework for Semantic Communication in Task-Oriented IoT Networks (Comprehensive Journal of Science, 2025)
The exponential growth of the Internet of Things (IoT) imposes unprecedented demands on wireless network resources. Traditional communication systems, designed to achieve bit-level fidelity, are inherently inefficient for many IoT applications where the goal is to convey meaning rather than perfect data reconstruction. This paper proposes a paradigm shift from classical bit-oriented to emerging meaning-oriented communication. We introduce a novel deep learning framework for a task-oriented semantic communication system, which we name DeepSC-IoT. Our proposed system employs a convolutional autoencoder architecture, trained end-to-end to extract, compress, and transmit only the essential semantic information required for a specific task at the receiver. We evaluate our system on a visual classification task using the MNIST dataset, simulating a network of IoT cameras. Simulation results demonstrate that our semantic approach achieves a significant reduction in bandwidth usage compared to a traditional separation-based scheme (JPEG compression + LDPC channel coding + QPSK modulation) while maintaining superior task accuracy, especially in low signal-to-noise ratio (SNR) regimes. This work validates the potential of semantic communication to enable scalable and ultra-efficient massive IoT deployments.
Keywords: Semantic Communication, Deep Learning, Internet of Things (IoT), 6G, Joint Source-Channel Coding, Bandwidth Efficiency.