Radiation source (RS) localization is crucial in electromagnetic environmental monitoring. Existing methods require prior knowledge of the environment and have overlooked the impact of multipath effects. Their positioning accuracy is penalized in practical applications. This paper presents an environment cognition-based variational Bayesian positioning method, which can achieve precise multi-source localization in uncooperative multipath environments using only the measurements of received signal strength (RSS). We begin by leveraging the signal propagation properties to design a model-data integrated unsupervised learning network, which partitions the positioning area according to the transmission states of different multipath signals. To estimate the number of RSs and mitigate the multipath effect, we identify the data samples of multipath RSS and divide them into groups, each corresponding to an RS. Then, a new variational Bayesian positioning algorithm is developed to operate without prior channel information and locate multiple RSs accurately across varying signal propagation models. Simulations show that our positioning method can effectively improve the accuracy of multi-RS localization by 10% in uncooperative multipath environments, compared to the state-of-the-art RSS-based localization techniques.

This paper presents a novel approach for wireless federated learning (WFL) that, for the first time, enables the aggregation of local models with mild to moderate errors under practical communication settings, which has to date been prevented by floating-point standards, e.g., IEEE binary32, and encryption. Specifically, we propose a new conversion from floating-point local models to fixed-point models on a layer basis, eliminating the need to transmit error-intolerant sign and exponent bits of floating-point numbers while accommodating variations in model layer widths and magnitudes. We also quantify how bit errors in the ciphertext affect the plaintext when symmetric encryption is employed for local model uploading, e.g., under Rayleigh, Rician, and Nakagami-m fading channels. Notably, these bit errors are leveraged to enhance the privacy of local models. We interpret the local model transmission process as a (λ, ϵ)-Rényi Differential Privacy (DP) mechanism, where bit errors induced by noisy channels, controlled via transmit powers, and exacerbated by decryption act as DP perturbations. Experiments show the superiority of the new WFL to the status quo with higher training accuracy and lower communication overhead.

This paper introduces an interference-free multi-stream transmission architecture leveraging stacked intelligent metasurfaces (SIMs), from a new perspective of interference exploitation. Unlike traditional interference exploitation precoding (IEP) which relies on computational hardware circuitry, we perform the precoding operations within the analog wave domain provided by SIMs. However, the benefits of SIM-enabled IEP are limited by the nonlinear distortion (NLD) caused by power amplifiers. A hardware-efficient interference-free transmitter architecture is developed to exploit SIM’s high and flexible degree of freedom (DoF), where the NLD on modulated symbols can be directly compensated in the wave domain. Moreover, we design a frame-level SIM configuration scheme and formulate a max-min problem on the safety margin function. With respect to the optimization of SIM phase shifts, we propose a recursive oblique manifold (ROM) algorithm to tackle the complex coupling among phase shifts across multiple layers. A flexible DoF-driven antenna selection (AS) scheme is explored in the SIM-enabled IEP system. Using an ROM-based alternating optimization (ROM-AO) framework, our approach jointly optimizes transmit AS, SIM phase shift design, and power allocation (PA), and develops a greedy safety margin-based AS algorithm. Simulations show that the proposed SIM-enabled frame-level IEP scheme significantly outperforms benchmarks. Specifically, the strategy with AS and PA can achieve a 20 dB performance gain compared to the case without any strategy under the 12 dB signal-to-noise ratio, which confirms the superiority of the NLD-aware IEP scheme and the effectiveness of the proposed algorithm.

Reconfigurable intelligent surface (RIS) has gained much attention as a cost-effective solution to enhance connectivity and coverage in massive machine-type communication. However, the passive nature of RIS poses fundamental challenges to decoupling and estimating base station (BS)-RIS and RIS-device channels, as well as identifying active devices. To effectively tackle this issue, we cast the joint channel estimation and activity detection for RIS-assisted Internet-of-Things networks as a tensor-based two-layer problem by exploiting the channel sparsity and a multi-frame pilot training structure. The first layer involves the Canonical Polyadic (CP) decomposition of a third-order tensor observation, while the second layer addresses compressive sensing (CS)-based simple measurement vector (SMV) and multiple measurement vector (MMV) problems. Then, by leveraging the Bayesian inference framework, we propose a tensor-based approximate message passing (TAMP) algorithm to estimate one-hop BS-RIS channel, one-hop RIS-device channels, and active IoT devices simultaneously. Furthermore, we conduct the state evolution (SE) analysis of TAMP to theoretically characterize its MSE. Numerical results corroborate the superior estimation and detection performance of TAMP and demonstrate that our SE analysis perfectly predicts the actual MSE.

Deep neural networks (DNNs) for image classification remain vulnerable to adversarial perturbations–subtle input manipulations that induce catastrophic misclassifications. To address this issue, we propose the Adversarial Image Rectifier (AIR), a linguistically inspired detection and mitigation framework that enhances DNN robustness by intercepting and inverting adversarial perturbations at the feature level. Unlike existing defenses, AIR operates without prior knowledge of attack patterns: it first encodes hierarchical hidden-layer feature maps of a DNN into semantically structured sentence representations, then identifies adversarial inputs through “sentiment” anomalies in these sentences–a linguistic metaphor for subtle adversarial traces. Crucially, we pinpoint a pivotal intermediate layer where adversarial perturbations dominantly propagate and train a lightweight rectifier network to selectively nullify adversarial features at this layer while preserving benign semantics. Extensive experiments on Tiny-ImageNet, CIFAR-10, SVHN, and MS COCO demonstrate that AIR achieves a correction rate of up to 95.02% and 94.62% when defending against known attacks and unknown attacks, respectively, significantly surpassing existing defense techniques.