Design of Artificial Interference Signals for Covert Communication Aided by Multiple Friendly Nodes

In this paper, we consider the scenario of covert communication aided by multiple friendly interference nodes. The objective is to conceal the legitimate communication link under the surveillance of a warden. We propose a novel strategy for generating artificial noise signals. In the absence of accurate channel fading information between the friendly interference nodes and the legitimate receiver, we leverage the statistical information of channel coefficients to optimize the basis matrix of the artificial noise signals space. The optimization aims to design artificial noise signals within the space to facilitate covert communication while minimizing the impact on the performance of legitimate communication. Due to the non-convex nature of the basis matrix constraints, the optimization problem is challenging to solve. Therefore, we employ the Riemannian optimization framework to analyze the geometric structure of the basis matrix constraints and transform the original non-convex optimization problem into an unconstrained problem on the complex Stiefel manifold for solution. Specifically, we utilize the Riemannian Stochastic Variance Reduced Gradient (R-SVRG) algorithm on the complex Stiefel manifold to solve the problem, significantly reducing the computational burden per iteration compared to full gradient algorithms. Additionally, we theoretically prove the convergence of the proposed algorithm to a stationary point. Finally, the performance of the proposed artificial noise strategy can be evaluated through numerical simulations, and compared to the Gaussian artificial noise strategy without optimization, the proposed strategy significantly improves covert performance.

On the design of Massive MIMO-QAM detector via $\ell_2$-Box ADMM approach

In this letter, we develop an $\ell_2$-box maximum likelihood (ML) formulation for massive multiple-input multiple-output (MIMO) quadrature amplitude modulation (QAM) signal detection and customize an alternating direction method of multipliers (ADMM) algorithm to solve the nonconvex optimization model. In the $\ell_2$-box ADMM implementation, all variables are solved analytically. Moreover, several theoretical results related to convergence, iteration complexity, and computational complexity are presented. Simulation results demonstrate the effectiveness of the proposed $\ell_2$-box ADMM detector in comparison with state-of-the-arts approaches.

Multi-Level Contrastive Learning for Cross-Lingual Alignment

Cross-language pre-trained models such as multilingual BERT (mBERT) have achieved significant performance in various cross-lingual downstream NLP tasks. This paper proposes a multi-level contrastive learning (ML-CTL) framework to further improve the cross-lingual ability of pre-trained models. The proposed method uses translated parallel data to encourage the model to generate similar semantic embeddings for different languages. However, unlike the sentence-level alignment used in most previous studies, in this paper, we explicitly integrate the word-level information of each pair of parallel sentences into contrastive learning. Moreover, cross-zero noise contrastive estimation (CZ-NCE) loss is proposed to alleviate the impact of the floating-point error in the training process with a small batch size. The proposed method significantly improves the cross-lingual transfer ability of our basic model (mBERT) and outperforms on multiple zero-shot cross-lingual downstream tasks compared to the same-size models in the Xtreme benchmark.

Efficient ADMM Decoder for Non-binary LDPC Codes with Codeword-Independent Performance

In this paper, we devote to devise a non-binary low-density parity-check (LDPC) decoder in Galois fields of characteristic two ($\mathbb{F}_{2^q}$) via the alternating direction method of multipliers (ADMM) technique. Through the proposed bit embedding technique and the decomposition technique of the three-variables parity-check equation, an efficient ADMM decoding algorithm for non-binary LDPC codes is proposed. The computation complexity in each ADMM iteration is roughly $\mathcal{O}(nq)$, which is significantly lower than the existing LDPC decoders. Moreover, we prove that the proposed decoder satisfies the favorable property of the codeword-independent. Simulation results demonstrate the outstanding performance of the proposed decoder in contrast with state-of-the-art LDPC decoders.

Secure Hybrid Beamforming for IRS-Assisted Millimeter Wave Systems

In this paper, we investigate the secure beamforming design in an intelligent reflection surface (IRS) assisted millimeter wave (mmWave) system, where the hybrid beamforming (HB) and the passive beamforming (PB) are employed by the transmitter and the IRS, respectively. To maximize the secrecy capacity, the joint optimization of HB and PB is formulated as a nonconvex problem with constant-modulus constraints. To efficiently solve such a challenging problem, the original problem is decomposed into a PB subproblem and an HB subproblem, then these two subproblems are sequentially solved by proposed algorithms. Simulation results demonstrate the superior performance of proposed approach in comparison with the state-of-the-art works.

Community-based Layerwise Distributed Training of Graph Convolutional Networks

The Graph Convolutional Network (GCN) has been successfully applied to many graph-based applications. Training a large-scale GCN model, however, is still challenging: Due to the node dependency and layer dependency of the GCN architecture, a huge amount of computational time and memory is required in the training process. In this paper, we propose a parallel and distributed GCN training algorithm based on the Alternating Direction Method of Multipliers (ADMM) to tackle the two challenges simultaneously. We first split GCN layers into independent blocks to achieve layer parallelism. Furthermore, we reduce node dependency by dividing the graph into several dense communities such that each of them can be trained with an agent in parallel. Finally, we provide solutions for all subproblems in the community-based ADMM algorithm. Preliminary results demonstrate that our proposed community-based ADMM training algorithm can lead to more than triple speedup while achieving the best performance compared with state-of-the-art methods.

Designing Binary Sequence Set with Optimized Correlation Properties via ADMM Approach

In this paper, we design low correlation binary sequences favorable in wireless communication and radar applications. First, we formulate the designing problem as a nonconvex combination optimization problem with flexible correlation interval; second, by relaxing constraints and introducing auxiliary variables, the original minimization problem is equivalent to a consensus continuous optimization problem; third, to achieve its good approximate solution efficiently, we propose the distributed executable algorithms based on alternating direction method of multipliers (ADMM); fourth, we prove that the proposed ADMM algorithms can converge to some stationary point of the approximate problem. Moreover, the computational complexity analysis is considered. Simulation results demonstrate that the proposed ADMM approaches outperform state-of-the-art ones in either computational cost or selection of correlation interval of the designed binary sequences.

Meeting-Merging-Mission: A Multi-robot Coordinate Framework for Large-Scale Communication-Limited Exploration

This letter presents a complete framework Meeting-Merging-Mission for multi-robot exploration under communication restriction. Considering communication is limited in both bandwidth and range in the real world, we propose a lightweight environment presentation method and an efficient cooperative exploration strategy. For lower bandwidth, each robot utilizes specific polytopes to maintains free space and super frontier information (SFI) as the source for exploration decision-making. To reduce repeated exploration, we develop a mission-based protocol that drives robots to share collected information in stable rendezvous. We also design a complete path planning scheme for both centralized and decentralized cases. To validate that our framework is practical and generic, we present an extensive benchmark and deploy our system into multi-UGV and multi-UAV platforms.

Indexing Context-Sensitive Reachability

Many context-sensitive data flow analyses can be formulated as a variant of the all-pairs Dyck-CFL reachability problem, which, in general, is of sub-cubic time complexity and quadratic space complexity. Such high complexity significantly limits the scalability of context-sensitive data flow analysis and is not affordable for analyzing large-scale software. This paper presents \textsc{Flare}, a reduction from the CFL reachability problem to the conventional graph reachability problem for context-sensitive data flow analysis. This reduction allows us to benefit from recent advances in reachability indexing schemes, which often consume almost linear space for answering reachability queries in almost constant time. We have applied our reduction to a context-sensitive alias analysis and a context-sensitive information-flow analysis for C/C++ programs. Experimental results on standard benchmarks and open-source software demonstrate that we can achieve orders of magnitude speedup at the cost of only moderate space to store the indexes. The implementation of our approach is publicly available.

Towards Quantized Model Parallelism for Graph-Augmented MLPs Based on Gradient-Free ADMM framework

The Graph Augmented Multi-layer Perceptron (GA-MLP) model is an attractive alternative to Graph Neural Networks (GNNs). This is because it is resistant to the over-smoothing problem, and deeper GA-MLP models yield better performance. GA-MLP models are traditionally optimized by the Stochastic Gradient Descent (SGD). However, SGD suffers from the layer dependency problem, which prevents the gradients of different layers of GA-MLP models from being calculated in parallel. In this paper, we propose a parallel deep learning Alternating Direction Method of Multipliers (pdADMM) framework to achieve model parallelism: parameters in each layer of GA-MLP models can be updated in parallel. The extended pdADMM-Q algorithm reduces communication cost by utilizing the quantization technique. Theoretical convergence to a critical point of the pdADMM algorithm and the pdADMM-Q algorithm is provided with a sublinear convergence rate $o(1/k)$. Extensive experiments in six benchmark datasets demonstrate that the pdADMM can lead to high speedup, and outperforms all the existing state-of-the-art comparison methods.