Generative AI models focus on creating new data instances that resemble the training data, often from unstructured or partially structured inputs. They enable a more comprehensive exploration of data distribution, ultimately enabling the generation of high-fidelity data samples [6,7]. The existing generative AI models employ various methods to generate data, with two prominent methods being generative adversarial networks (GANs) [8] and variational autoencoders (VAEs) [9]. A GAN consists of two neural networks, namely, a generator and a discriminator. The generator aims to create data that closely resemble real data, while the discriminator tries to distinguish between actual and generated data. Through this competitive framework, GANs can produce highly realistic images. However, training GANs can be unstable and may suffer from issues like mode collapse, where the generator fails to produce diverse outputs [10,11]. In contrast, VAEs operate by learning a latent space to generate new data. They compress the input data into latent variables and then reconstruct them to create new samples. While VAEs can effectively capture complex distributions, the quality of the generated data can be limited, sometimes lacking fine detail [12]. Recently, an alternative approach known as the diffusion model, introduced by [PERSON] et al. [13], has garnered significant attention. Unlike GANs and VAEs, the diffusion model operates through a systematic process of progressively adding noise to the data. During the training phase, this noise addition process is learned in reverse to reconstruct the original data. One of the primary strengths of the diffusion model is its enhanced robustness in generating high-dimensional data, effectively mitigating the common mode collapse issue faced by GANs. Moreover, it is known to generate more realistic and higher quality samples than VAEs. The processes and characteristics of diffusion models can be particularly advantageous in applications such as data reconstruction in communication systems, where maintaining a high-quality data representation is crucial. Thanks to their strengths in restoring data, there have been various studies on applying diffusion models to improve signal fidelity and data recovery processes in wireless communications [14, 15, 16, 17]. In [14], it was demonstrated that a generative diffusion model can rapidly synthesize reliable wireless channel samples from limited data. The channel noise reduction diffusion model (CDDM) [15] has been used to effectively remove channel noise and improve the clarity and fidelity of the transmitted signal. The authors of [16] utilized a diffusion model for end-to-end channel coding. This model was integrated into the channel coding process to enhance data transmission and signal fidelity, thereby improving the robustness of communication systems against noise. In this paper, a conditional diffusion model (CDM) is proposed for the reconstruction of OFDM-based images by leveraging the ability of diffusion models to denoise and restore images. To mitigate the complexity of OFDM-based image reconstruction, the proposed CDM incorporates a conditional factor that reflects the degree of distortion in the received signal. In contrast to existing methods in which a fixed variance (FV) is used to simplify the loss function during learning, the proposed model utilizes a learnable variance (LV). Through this process, an optimal variance can be selected based on the modulation scheme of OFDM signals, even under low-SNR conditions. The main contributions of this paper can be summarized as follows:  
* A CDM that addresses distorted OFDM-based images in wireless communication is introduced. While the existing diffusion models have only considered Gaussian noise in the diffusion process, the proposed model is designed to handle the image distortion that occurs in OFDM-based wireless communication. This adaptation allows the model to effectively account for varying levels of signal degradation, leading to improved reconstruction performance.