Next-Gen Wireless Tech Could Redefine Mobile communication

By Amit Malewar Published: August 27, 2025

Collected at: https://www.techexplorist.com/next-gen-wireless-tech-redefine-mobile-communication/100773/

Communication tech has evolved rapidly in recent years. One major leap is semantic communication; instead of sending raw data, systems now focus on transmitting meaning. For example, when sending images, it’s more important to preserve what the image represents than every pixel detail.

Deep learning has helped push this forward, but to work with today’s digital networks, we need better ways to convert meaning into digital signals. That’s where things get tricky; most current systems still struggle with efficient digitization.

Enter ConcreteSC, a new method from SeoulTech researchers. It ditches bulky codebooks and uses temperature-controlled concrete distributions to streamline the process. The result? Up to 3 times better image quality and 39 times faster processing than older techniques, making wireless communication smarter, faster, and more efficient.

Dr. Dong Jin Ji, an Associate Professor at the Department of Semiconductor Engineering at Seoul National University of Science and Technology, Republic of Korea, said, “Unlike vector quantization (VQ), a state-of-the-art digitization technique that suffers from channel noise and codebook divergence during training, our framework offers a fully differentiable solution to quantization, allowing end-to-end training even under channel noise. Notably, due to the nature of the ConcreteSC that directly generates the required bitstream, it is possible to train a multi-feedback-length model pair with a relatively simple masking scheme.”

To test their breakthrough, SeoulTech researchers ran simulations using ImageNet under two real-world wireless conditions: Rayleigh and Rician fading, which mimic how signals behave in cluttered or partially transparent environments.

ConcreteSC consistently outperforms traditional vector quantization (VQ) methods in key image quality metrics such as structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). What sets it apart is its ability to digitize semantic meaning without relying on massive codebooks, making it both lightweight and efficient.

It integrates seamlessly into existing semantic communication frameworks, enhancing quantization quality while significantly reducing computational demands. Unlike conventional approaches, where complexity can grow exponentially, ConcreteSC scales linearly with bit length, making it a smarter and more scalable solution for next-generation wireless systems.

ConcreteSC is robust, flexible, and future-ready, a significant step toward efficient semantic communication in 6G and beyond.

Dr Ji said, “Sixth-generation wireless communication systems, where semantic communication technologies are expected to be one of the key enablers, are expected to be a major area of application for our technology. These include smart factories, where ultra-dense machine-type communications are prevalent.”

This technology opens the door to transformative applications across industries and daily life. Imagine a fully autonomous factory where every piece of equipment, from robotic arms to tiny sensors, is equipped with embedded AI and communicates wirelessly, eliminating the need for complex cabling.

Semantic communication makes this possible by allowing devices to exchange meaningful information efficiently, even in noisy or bandwidth-limited environments. On the human side, it enables comprehensive lifecare systems for seniors and toddlers, powered by low-energy IoT devices that continuously monitor health and behavior.

These systems rely on large AI models to interpret data and respond intelligently, something that would be unfeasible without the kind of advanced semantic communication framework demonstrated by ConcreteSC.

Journal Reference:

1. Dr. Dong Jin Ji. Fully Learnable Multi-Rate Quantization for Digital Semantic Communication Systems. IEEE Wireless Communications Letters. DOI: 10.1109/LWC.2025.3581374