arXiv:2509.01641v3 Announce Type: replace-cross Abstract: We propose a novel diffusion model, termed the non-identical diffusion model, and investigate its application to wireless orthogonal frequency division multiplexing (OFDM) channel generation. Unlike the standard diffusion model that uses a scalar-valued time index to represent the global noise level, we extend this notion to an element-wise time indicator to capture local error variations more accurately. Non-identical diffusion enables us to characterize the reliability of each element (e.g., subcarriers in OFDM) within the noisy input
The continuous evolution of AI models like diffusion models is driving innovation in various fields, with advancements in capturing granular data variations being a key development.
This development could significantly improve the accuracy and efficiency of wireless communication systems by better modeling complex channel conditions, leading to more robust networks.
The ability to characterize local error variations in wireless channels more precisely using non-identical diffusion models offers a pathway to more reliable and higher-performing communication technologies.
- · Telecommunications companies
- · AI researchers
- · Wireless infrastructure providers
- · Legacy channel modeling techniques
More accurate wireless channel generation could lead to improved network planning and optimization.
This might enable the deployment of more sophisticated wireless technologies (e.g., 6G) with higher reliability and data rates.
Enhanced wireless performance could accelerate the development and adoption of other AI-driven applications that rely on robust connectivity.
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Read at arXiv cs.LG