With the advancements in six-generation (6G) communication technology, there is a growing need for comprehensive and interpretable network performance evaluation for network optimization. Traditional evaluation methods often overlook uncertainties and are limited to a single time scale or performance dimension, while the recent machine learning-based method lacks interpretability. To address this issue, we propose a multidimensional performance evaluator for interpretable and traceable network performance evaluation (MPITE). MPITE, constructed with a three-layer evaluation model incorporating physical, logical, and causal topology structures, reflects the causal relationship of communication system configurations, the changing network states, and performance metrics. We introduce a multidimensional performance index that considers value, time, and certainty dimensions to evaluate network performance comprehensively. We propose interpretable Bayesian theory-based network inference algorithms to derive network certainty for interpretable network performance evaluation. Then, we intelligently derive optimal network configuration parameters through reverse inferencing for network tracing. Experimental results demonstrate the advantage, interpretability, and traceability of MPITE.

Abstract This paper investigates the application of unsupervised learning methods for computed tomography reconstruction. To motivate our work, we review several existing priors, namely the truncated Gaussian prior, the prior, the total variation prior, and the deep image prior (DIP). We find that DIP outperforms the other three priors in terms of representational capability and visual performance. However, the performance of DIP deteriorates when the number of iterations exceeds a certain threshold due to overfitting. To address this issue, we propose a novel method (MCDIP‐ADMM) based on multi‐code deep image prior (MCDIP) and plug‐and‐play alternative direction method of multipliers (ADMM). Specifically, MCDIP utilizes multiple latent codes to generate a series of feature maps at an intermediate layer within a generator model. These maps are then composed with trainable weights, representing the complete image prior. Experimental results demonstrate the superior performance of the proposed MCDIP‐ADMM compared to three existing competitors. In the case of parallel beam projection with Gaussian noise, MCDIP‐ADMM achieves an average improvement of 4.3 dB over DIP, 1.7 dB over ADMM DIP‐weighted total variation (WTV) and 1.2 dB over PnP‐DIP in terms of peak‐signal‐to‐noise ratio (PSNR). Similarly, for fan‐beam projection with Poisson noise, MCDIP‐ADMM achieves an average improvement of 3.09 dB over DIP, 1.86 dB over ADMM DIP‐WTV and 0.84 dB over PnP‐DIP in terms of PSNR.

We provide a complete framework for performing infinite dimensional Bayesian inference and uncertainty quantification for image reconstruction with Poisson data. In particular, we address the following issues to make the Bayesian framework applicable in practice. We first introduce a positivity-preserving reparametrization, and we prove that under the reparametrization and a hybrid prior, the posterior distribution is well-posed in the infinite dimensional setting. Second, we provide a dimension-independent Markov chain Monte Carlo algorithm, based on the preconditioned Crank--Nicolson Langevin method, in which we use a primal-dual scheme to compute the offset direction. Third, we give a method combining the model discrepancy method and maximum likelihood estimation to determine the regularization parameter in the hybrid prior. Finally we propose to use the obtained posterior distribution to detect artifacts in a recovered image. We provide an example to demonstrate the effectiveness of the proposed method.