.png){kind=icon}
.png){kind=icon}
Return
Adaptive denoising diffusion null-space models for semantic communications over MIMO channels
.png){kind=icon}
PRE
AI
T
Z
D
W
Z
DOI：10.23919/jcc.fa.2025-0284.202604.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multiple-input multiple-output (MIMO) systems are essential for improving capacity and reliability in semantic communications. Existing methods mainly design the channel-aware neural networks but neglect the underlying signal distribution. In this paper, we develop a denoising diffusion null-space model-based module over MIMO channels (DDNM-MIMO), which is a plug-in module deployed at the receiver. By modeling the MIMO channel, precoding, and equalization as a linear transformation with additive noise, we design corresponding linear and scaling matrices to construct a sampling process for denoising the received signal. The DDNM-MIMO integrates channel state information (CSI) embedding, supporting both closed-loop MIMO with CSI at the transmitter and open-loop MIMO with CSI at the receiver, thereby improving channel adaptability across various noise levels. As a plug-in, the DDNM-MIMO module operates independently of the joint source-channel coding (JSCC) coder structure, offering flexible integration into diverse systems. Experimental results show that DDNM-MIMO effectively reduces the mean square errors (MSE) between the encoded and equalized signals. Consequently, the proposed DDNM-MIMO semantic communication system achieves superior image reconstruction performance compared to existing JSCC-based semantic communication method.
Keywords:
channel state information (CSI)
diffusion model (DM)
multiple-input multiple-output (MIMO)
semantic communications
Journal
IF:
3.1
Papers:
1.8K
Citations:
5.0K
Organization
Cited Papers