In modern communication systems, to conserve transmission energy, the collected data are often max-aggregated. This aggregation involves observing only the features with relatively larger values in each observed sample. Recovering the joint distribution from such systematically missing data is of great interest for numerous downstream applications, including network optimization and user localization. This paper is motivated by the aforementioned industrial problem and investigates the recoverability and estimation of the joint distribution in the bivariate case. We consider various parametric model assumptions (uniform, Gaussian, mixture of Gaussians) and derive corresponding loss functions based on the maximum likelihood (ML) principle. The effectiveness of the proposed method is demonstrated through simulations. We also discuss its potential application in future wireless communication networks.

The problem of joint direction-of-arrival estimation and distorted sensor detection has received a lot of attention in recent decades. Most state-of-the-art work formulated such a problem via low-rank and row-sparse decomposition, where the low-rank and row-sparse components were treated in an isolated manner. Such a formulation results in a performance loss. Differently, in this paper, we entangle the low-rank and row-sparse components by exploring their inherent connection. Furthermore, we take into account the maximal distortion level of the sensors. An alternating optimization scheme is proposed to solve the low-rank component and the sparse component, where a closed-form solution is derived for the low-rank component and a quadratic programming is developed for the sparse component. Numerical results exhibit the effectiveness and superiority of the proposed method.

Integer linear programs (ILPs) are commonly employed to model diverse practical problems such as scheduling and planning. Recently, machine learning techniques have been utilized to solve ILPs. A straightforward idea is to train a model via supervised learning, with an ILP as the input and an optimal solution as the label. An ILP is symmetric if its variables can be permuted without changing the problem structure, resulting in numerous equivalent and optimal solutions. Randomly selecting an optimal solution as the label can introduce variability in the training data, which may hinder the model from learning stable patterns. In this work, we incorporate the intrinsic symmetry of ILPs and propose a novel training framework called SymILO. Specifically, we modify the learning task by introducing solution permutation along with neural network weights as learnable parameters and then design an alternating algorithm to jointly optimize the loss function. We conduct extensive experiments on ILPs involving different symmetries and the computational results demonstrate that our symmetry-aware approach significantly outperforms three existing methods----achieving $50.3\\%$, $66.5\\%$, and $45.4\\%$ average improvements, respectively.

Nowadays it has still remained as a big challenge to efficiently compress color images in the encrypted domain. In this paper we present a novel deep-learning-based approach to encryption-then-lossy-compression (ETC) of color images by incorporating the domain knowledge of the encrypted image reconstruction process. In specific, a simple yet effective uniform down-sampling is utilized for lossy compression of images encrypted with a modulo-256 addition, and the task of image reconstruction from an encrypted down-sampled image is then formulated as a problem of constrained super-resolution (SR) reconstruction. A customized residual dense spatial network (RDSN) is proposed to solve the formulated constrained SR task by taking advantage of spatial attention mechanism (SAM), global skip connection (GSC), and uniform down-sampling constraint (UDC) that is specific to an ETC system. Extensive experimental results show that the proposed ETC scheme achieves significant performance improvement compared with other state-of-the-art ETC methods, indicating the feasibility and effectiveness of the proposed deep-learning based ETC scheme.

Localizing the root cause of network faults is crucial to network operation and maintenance (O&M). Significant operational expenses will be saved if the root cause can be identified agilely and accurately. However, this is challenging for human beings due to the complicated wireless environments and network architectures. Resorting to data analysis and machine learning is promising but remains difficult due to various practical issues, such as the lack of well-labeled samples, hybrid fault behaviors, missing data, and so on. In this paper, we introduce a novel real-world dataset for wireless communication network fault diagnosis. The goal is to infer the root cause timely when we observe certain symptoms in a network. Several baseline methods are provided.

We propose a novel Gaussian process kernel that takes advantage of a deep neural network (DNN) structure but retains good interpretability. The resulting kernel is capable of addressing four major issues of the previous works of similar art, i.e., the optimality, explainability, model complexity, and sample efficiency. Our kernel design procedure comprises three steps: (1) Derivation of an optimal kernel with a non-stationary dot product structure that minimizes the prediction/test mean-squared-error (MSE); (2) Decomposition of this optimal kernel as a linear combination of shallow DNN subnetworks with the aid of multi-way feature interaction detection; (3) Updating the hyper-parameters of the subnetworks via an alternating rationale until convergence. The designed kernel does not sacrifice interpretability for optimality. On the contrary, each subnetwork explicitly demonstrates the interaction of a set of features in a transformation function, leading to a solid path toward explainable kernel learning. We test the proposed kernel with both synthesized and real-world data sets, and the proposed kernel is superior to its competitors in terms of prediction performance in most cases. Moreover, it tends to maintain the prediction performance and be robust to data over-fitting issue, when reducing the number of samples.