Online change detection (OCD) aims to rapidly identify change points in streaming data and is critical in applications such as power system monitoring, wireless network sensing, and financial anomaly detection. Existing OCD methods typically assume precise system knowledge, which is unrealistic due to estimation errors and environmental variations. Moreover, existing OCD methods often struggle with efficiency in large-scale systems. To overcome these challenges, we propose RoS-Guard, a robust and optimal OCD algorithm tailored for linear systems with uncertainty. Through a tight relaxation and reformulation of the OCD optimization problem, RoS-Guard employs neural unrolling to enable efficient parallel computation via GPU acceleration. The algorithm provides theoretical guarantees on performance, including expected false alarm rate and worst-case average detection delay. Extensive experiments validate the effectiveness of RoS-Guard and demonstrate significant computational speedup in large-scale system scenarios.

The primary goal of online change detection (OCD) is to promptly identify changes in the data stream. OCD problem find a wide variety of applications in diverse areas, e.g., security detection in smart grids and intrusion detection in communication networks. Prior research usually assumes precise knowledge of the system parameters. Nevertheless, this presumption often proves unattainable in practical scenarios due to factors such as estimation errors, system updates, etc. This paper aims to take the first attempt to develop a triadic-OCD framework with certifiable robustness, provable optimality, and guaranteed convergence. In addition, the proposed triadic-OCD algorithm can be realized in a fully asynchronous distributed manner, easing the necessity of transmitting the data to a single server. This asynchronous mechanism could also mitigate the straggler issue that faced by traditional synchronous algorithm. Moreover, the non-asymptotic convergence property of Triadic-OCD is theoretically analyzed, and its iteration complexity to achieve an $\epsilon$-optimal point is derived. Extensive experiments have been conducted to elucidate the effectiveness of the proposed method.

In this paper, we investigate a novel physical layer secure (PLS) potential technique which is summarized into a category of index modulation (IM) scheme, namely the spatial field digital modulation (SFDM). In an SFDM communication system, bit streams are modulated in the spatial distribution of electromagnetic (EM) field. Orbital angular momentum (OAM) mode groups can be utilized for beamforming and thus realize an OAM based SFDM (OAM-SFDM) system. This kind of IM systems that utilize the distribution of EM field to transmit information can radiate different signals to different directions and possess inherent anti-eavesdropping capability. Since an eavesdropper lacks prior knowledge, it is difficult to construct appropriate and effective estimators for signal demodulation. However, such system is vulnerable when potential eavesdroppers adopt clustering algorithm, a type of non-realtime demodulation. We propose two PLS strategies with different computational complexity, the power hopping (PH) method and the symbol variation (SV) method, to confront single-antenna eavesdroppers and multi-antenna eavesdroppers, respectively. Both approaches are key-free, and do not require any prior channel state information (CSI) about eavesdroppers. The mentioned methods could constantly disrupt the statistical properties of eavesdroppers’ channels and even their received signals. Theoretical analysis and numerical simulations have been conducted to validate the feasibility of these two schemes. Furthermore, a prototype of OAM-SFDM based PLS communication system is built in a realistic scenario. The experimental results demonstrate that both PH method and SV method can effectively resist clustering algorithm in their corresponding application scenarios.

To establish reliable wireless communication systems in underground mines, accurate channel modeling plays an indispensable role in the design phase. In this paper, we propose ray tracing-based channel modeling in room-and-pillar mines. Specifically, the propagation paths of signals are firstly determined in the horizontal plane of a room-and-pillar mine using the ray tracing method. Then, the two-dimensional (2D) propagation paths are converted into their corresponding three-dimensional (3D) counterparts by deriving the heights of intersection points at obstacles and the locations of reflection points on mine ceilings and floors. Since the roughness of mine surfaces has a non-negligible influence on radio propagation properties, it is also taken into account when the electric fields of propagation paths are calculated. To validate the accuracy of the proposed ray tracing-based room-and-pillar channel model, we compare simulation results with experimental measurements conducted in an actual mine in terms of mean excess delay and root mean square (RMS) delay spread. The comparisons show simulation results have a good consistency with measurement data.