Bayesian Cramér-Rao Bound for Sensing Performance in Meta-Backscatter Systems

Meta-backscatter system that utilizes meta-material sensors is a promising enabler for future environmental sensing, offering distinct advantages such as low cost, zero-power consumption, and robustness. Specifically, the electromagnetic response of the sensor, typically characterized by a frequency-selective absorption profile, is affected by the environmental conditions, allowing the estimation of these conditions from the reflected signal. However, it remains unclear what estimation accuracy can be achieved fundamentally. Motivated by this gap, we quantify this accuracy limit using the Bayesian Cramér-Rao bound (BCRB), which provides a lower bound on the mean-squared error for the environmental condition. Establishing this limit is challenging because the electromagnetic response of the sensor is distorted by the channel fading, while the channel estimation is infeasible since the sensors cannot be configured to predefined states to generate training data. To address this challenge, we consider the joint BCRB of the channel coefficient and the environmental condition in a multicarrier framework. The BCRB of the environmental condition is then obtained by selecting the corresponding element from the joint BCRB. An analysis of the derived BCRB reveals the impact of the absorption peak shape and the number of subcarriers. The derivation and analysis of the BCRB are verified through simulations.

Aerial IRS Deployment-Aided Secure Computation Offloading Against DISCO Jamming Attacks

With the rapid growth of Multi-access Edge Computing (MEC), secure and efficient computation offloading from user equipment (UEs) to edge access points (APs) is critical. However, DISCO intelligent reflective surface-based fully-passive jammers (DIRS-based FPJs) use random time-varying phase shifts to launch DISCO jamming attacks, disrupting offloading performance. This paper leverages an aerial intelligent reflective surface (AIRS) to enable secure computation offloading against DISCO jamming by jointly optimizing offloading ratios, AIRS phase shifts, and deployment. A two-timescale (2Ts) framework is proposed to address the optimization challenge caused by the distinct update frequencies of different strategies. Specifically, AIRS deployment is adjusted on a long timescale to boost antijamming capability due to the impracticality of frequent physical adjustment, while offloading ratios and phase shifts are optimized on a short timescale to adapt to DIRS-jammed dynamic channel conditions. We propose a dual-agent deep reinforcement learning (DRL)-based AIRS deployment-aided secure computation offloading (DDADSO) scheme to maximize the secure offloading utility under DISCO jamming. Simulation results verify that the proposed DDADSO scheme outperforms benchmark schemes, demonstrating the effectiveness of AIRS deployment in improving offloading performance against DISCO jamming attacks.

Bistatic Integrated Sensing and Communication in the Presence of a Disco Reconfigurable Intelligent Surface: Disruption, Enhancement, or Both?

Integrated sensing and communication (ISAC) is widely regarded as one of the key enabling technologies for future sixth-generation (6G) wireless communication systems. In this work, we investigate a bistatic ISAC system in the presence of a disco reconfigurable intelligent surface (DRIS), whose random and time-varying reflection coefficients emulate a "disco ball." The introduction of the DRIS breaks the underlying assumption in existing ISAC systems that the sensing and communication channels remain static or quasi-static within the channel coherence time. We first develop a bistatic system model incorporating the DRIS and characterize all involved wireless channels. Then, an ISAC waveform design that balances sensing and communication performance is proposed by formulating a Pareto optimization problem, where the trade-off is controlled through a tunable factor. Communication and sensing performance in the bistatic ISAC system are quantified by the signal-to-interference-plus-noise ratio (SINR) and the Cramer-Rao lower bound (CRLB), respectively. To quantify the impact of the DRIS on the bistatic ISAC system, we derive the statistical characteristics of DRIS-induced active channel aging (ACA) channels for communications and the cascaded DRIS-based sensing channel. Then, we establish a theoretical lower bound on the SINR and closed-form CRLB expressions in the presence of a DRIS. The analysis reveals several distinctive properties of the DRIS in bistatic ISAC systems. In particular, the DRIS degrades communication performance significantly due to the introduction of ACA interference. In contrast, with respect to sensing performance, the DRIS decreases the estimation accuracy of the angle of departure (AoD) while concurrently enhancing that of the angle of arrival (AoA). Numerical results validate the derived theoretical analysis and confirm these DRIS-induced behaviors.

Towards Performance-Enhanced Model-Contrastive Federated Learning using Historical Information in Heterogeneous Scenarios

Federated Learning (FL) enables multiple nodes to collaboratively train a model without sharing raw data. However, FL systems are usually deployed in heterogeneous scenarios, where nodes differ in both data distributions and participation frequencies, which undermines the FL performance. To tackle the above issue, this paper proposes PMFL, a performance-enhanced model-contrastive federated learning framework using historical training information. Specifically, on the node side, we design a novel model-contrastive term into the node optimization objective by incorporating historical local models to capture stable contrastive points, thereby improving the consistency of model updates in heterogeneous data distributions. On the server side, we utilize the cumulative participation count of each node to adaptively adjust its aggregation weight, thereby correcting the bias in the global objective caused by different node participation frequencies. Furthermore, the updated global model incorporates historical global models to reduce its fluctuations in performance between adjacent rounds. Extensive experiments demonstrate that PMFL achieves superior performance compared with existing FL methods in heterogeneous scenarios.

An Unsupervised Normalizing Flow-Based Neyman-Pearson Detector for Covert Communications in the Presence of Disco Reconfigurable Intelligent Surfaces

Covert communications, also known as low probability of detection (LPD) communications, offer a higher level of privacy protection compared to cryptography and physical-layer security (PLS) by hiding the transmission within ambient environments. Here, we investigate covert communications in the presence of a disco reconfigurable intelligent surface (DRIS) deployed by the warden Willie, which simultaneously reduces his detection error probabilities and degrades the communication performance between Alice and Bob, without relying on either channel state information (CSI) or additional jamming power. However, the introduction of the DRIS renders it intractable for Willie to construct a Neyman-Pearson (NP) detector, since the probability density function (PDF) of the test statistic is analytically intractable under the Alice-Bob transmission hypothesis. Moreover, given the adversarial relationship between Willie and Alice/Bob, it is unrealistic to assume that Willie has access to a labeled training dataset. To address these challenges, we propose an unsupervised masked autoregressive flow (MAF)-based NP detection framework that exploits prior knowledge inherent in covert communications. We further define the false alarm rate (FAR) and the missed detection rate (MDR) as monitoring performance metrics for Willie, and the signal-to-jamming-plus-noise ratio (SJNR) as a communication performance metric for Alice-Bob transmissions. Furthermore, we derive theoretical expressions for SJNR and uncover unique properties of covert communications in the presence of a DRIS. Simulations validate the theory and show that the proposed unsupervised MAF-based NP detector achieves performance comparable to its supervised counterpart.

RIS-Aided Wireless Amodal Sensing for Single-View 3D Reconstruction

Amodal sensing is critical for various real-world sensing applications because it can recover the complete shapes of partially occluded objects in complex environments. Among various amodal sensing paradigms, wireless amodal sensing is a potential solution due to its advantages of environmental robustness, privacy preservation, and low cost. However, the sensing data obtained by wireless system is sparse for shape reconstruction because of the low spatial resolution, and this issue is further intensified in complex environments with occlusion. To address this issue, we propose a Reconfigurable Intelligent Surface (RIS)-aided wireless amodal sensing scheme that leverages a large-scale RIS to enhance the spatial resolution and create reflection paths that can bypass the obstacles. A generative learning model is also employed to reconstruct the complete shape based on the sensing data captured from the viewpoint of the RIS. In such a system, it is challenging to optimize the RIS phase shifts because the relationship between RIS phase shifts and amodal sensing accuracy is complex and the closed-form expression is unknown. To tackle this challenge, we develop an error prediction model that learns the mapping from RIS phase shifts to amodal sensing accuracy, and optimizes RIS phase shifts based on this mapping. Experimental results on the benchmark dataset show that our method achieves at least a 56.73% reduction in reconstruction error compared to conventional schemes under the same number of RIS configurations.

Meta-Backscatter: Long-Distance Battery-Free Metamaterial-Backscatter Sensing and Communication

Battery-free Internet of Things (BF-IoT) enabled by backscatter communication is a rapidly evolving technology offering advantages of low cost, ultra-low power consumption, and robustness. However, the practical deployment of BF-IoT is significantly constrained by the limited communication range of common backscatter tags, which typically operate with a range of merely a few meters due to inherent round-trip path loss. Meta-backscatter systems that utilize metamaterial tags present a promising solution, retaining the inherent advantages of BF-IoT while breaking the critical communication range barrier. By leveraging densely paved sub-wavelength units to concentrate the reflected signal power, metamaterial tags enable a significant communication range extension over existing BF-IoT tags that employ omni-directional antennas. In this paper, we synthesize the principles and paradigms of metamaterial sensing to establish a unified design framework and a forward-looking research roadmap. Specifically, we first provide an overview of backscatter communication, encompassing its development history, working principles, and tag classification. We then introduce the design methodology for both metamaterial tags and their compatible transceivers. Moreover, we present the implementation of a meta-backscatter system prototype and report the experimental results based on it. Finally, we conclude by highlighting key challenges and outlining potential avenues for future research.

Tree-Mamba: A Tree-Aware Mamba for Underwater Monocular Depth Estimation

Underwater Monocular Depth Estimation (UMDE) is a critical task that aims to estimate high-precision depth maps from underwater degraded images caused by light absorption and scattering effects in marine environments. Recently, Mamba-based methods have achieved promising performance across various vision tasks; however, they struggle with the UMDE task because their inflexible state scanning strategies fail to model the structural features of underwater images effectively. Meanwhile, existing UMDE datasets usually contain unreliable depth labels, leading to incorrect object-depth relationships between underwater images and their corresponding depth maps. To overcome these limitations, we develop a novel tree-aware Mamba method, dubbed Tree-Mamba, for estimating accurate monocular depth maps from underwater degraded images. Specifically, we propose a tree-aware scanning strategy that adaptively constructs a minimum spanning tree based on feature similarity. The spatial topological features among the tree nodes are then flexibly aggregated through bottom-up and top-down traversals, enabling stronger multi-scale feature representation capabilities. Moreover, we construct an underwater depth estimation benchmark (called BlueDepth), which consists of 38,162 underwater image pairs with reliable depth labels. This benchmark serves as a foundational dataset for training existing deep learning-based UMDE methods to learn accurate object-depth relationships. Extensive experiments demonstrate the superiority of the proposed Tree-Mamba over several leading methods in both qualitative results and quantitative evaluations with competitive computational efficiency. Code and dataset will be available at https://wyjgr.github.io/Tree-Mamba.html.

Simultaneously Exposing and Jamming Covert Communications via Disco Reconfigurable Intelligent Surfaces

Covert communications provide a stronger privacy protection than cryptography and physical-layer security (PLS). However, previous works on covert communications have implicitly assumed the validity of channel reciprocity, i.e., wireless channels remain constant or approximately constant during their coherence time. In this work, we investigate covert communications in the presence of a disco RIS (DRIS) deployed by the warden Willie, where the DRIS with random and time-varying reflective coefficients acts as a "disco ball", introducing timevarying fully-passive jamming (FPJ). Consequently, the channel reciprocity assumption no longer holds. The DRIS not only jams the covert transmissions between Alice and Bob, but also decreases the error probabilities of Willie's detections, without either Bob's channel knowledge or additional jamming power. To quantify the impact of the DRIS on covert communications, we first design a detection rule for the warden Willie in the presence of time-varying FPJ introduced by the DRIS. Then, we define the detection error probabilities, i.e., the false alarm rate (FAR) and the missed detection rate (MDR), as the monitoring performance metrics for Willie's detections, and the signal-to-jamming-plusnoise ratio (SJNR) as a communication performance metric for the covert transmissions between Alice and Bob. Based on the detection rule, we derive the detection threshold for the warden Willie to detect whether communications between Alice and Bob is ongoing, considering the time-varying DRIS-based FPJ. Moreover, we conduct theoretical analyses of the FAR and the MDR at the warden Willie, as well as SJNR at Bob, and then present unique properties of the DRIS-based FPJ in covert communications. We present numerical results to validate the derived theoretical analyses and evaluate the impact of DRIS on covert communications.

Building a Human-Verified Clinical Reasoning Dataset via a Human LLM Hybrid Pipeline for Trustworthy Medical AI

Despite strong performance in medical question-answering, the clinical adoption of Large Language Models (LLMs) is critically hampered by their opaque 'black-box' reasoning, limiting clinician trust. This challenge is compounded by the predominant reliance of current medical LLMs on corpora from scientific literature or synthetic data, which often lack the granular expert validation and high clinical relevance essential for advancing their specialized medical capabilities. To address these critical gaps, we introduce a highly clinically relevant dataset with 31,247 medical question-answer pairs, each accompanied by expert-validated chain-of-thought (CoT) explanations. This resource, spanning multiple clinical domains, was curated via a scalable human-LLM hybrid pipeline: LLM-generated rationales were iteratively reviewed, scored, and refined by medical experts against a structured rubric, with substandard outputs revised through human effort or guided LLM regeneration until expert consensus. This publicly available dataset provides a vital source for the development of medical LLMs that capable of transparent and verifiable reasoning, thereby advancing safer and more interpretable AI in medicine.