The IEC 61850 Sampled Values (SV) protocol lacks built-in security, exposing substations to cyber risks. We present a physics-guided, explainable anomaly detector combining statistical descriptors with ensemble-based meta-features and an interpretable classifier. On the QUT-ZSS 2023 SV dataset, it achieves 98.2% F1 and 99.8% accuracy with 1.4 μs latency. An integrated alert parser provides feature-level explanations for actionable operator feedback without harming real-time performance.

In heterogeneous ultra-dense networks with millimeter wave macro cells and small cells, base stations (BSs) and mobile user equipments (UEs) perform beamforming operations to establish highly directional links. In spite of the spatial diversity achieved through directional links, as a number of BSs are densely deployed, inter-cell interference caused by concurrent directional transmissions of adjacent BSs becomes severe, resulting in downlink performance degradation in the network. However, it is very difficult to manage inter-cell interference because of the nature of the time-varying wireless fading environment, the dynamic changes in beam propagation directivity, and unpredictable UEs' locations. In this paper, we propose an online learning-based transmission coordination algorithm based on the framework of multi-armed bandits to learn the unknown characteristics of inter-BS interference and exploit learned data to derive an optimal policy for maximizing the number of successful downlink transmissions. Through the numerical simulations, we verify the effectiveness of the proposed online learning-based inter-BS interference management scheme.

In this paper, we propose a single-drone-based localization for radio frequency (RF) sources in an outdoor environment. For fast and accurate localization, we propose a dynamic trajectory planning scheme, which guides the RF scanning drone toward a direction minimizing the geometric dilution of precision (GDOP) in order to minimize the uncertainty of location estimation for target RF sources.

In this paper, we study the problem of accurately estimating the location of indoor wireless devices which, due to its vast application areas, has continued to generate much interest both in the academia and industry. Specifically, we consider the location estimation problem and develop non-hybrid Bayesian location estimators for when received signal strength (RSS) and/or time of arrival (TOA) measurements between the target node (TN) and designated beacon nodes (BNs) in the network are obtainable. However, RSS- and TOA-based location estimators can be inaccurate when the RF-characteristics are not stable and time synchronization between the TN and BNs is not set up properly, respectively. To mitigate the disadvantages of these two location estimators, we propose the hybrid Bayesian location estimators. We show, through the results of extensive simulation experiments conducted that in comparison with the non-hybrid estimators, the hybrid Bayesian location estimators are more robust in the localization environment where RSS/TOA measurement precision varies.

Multi-user multiple-input multiple-output (MIMO) technology makes it possible for wireless nodes to successfully receive multiple packets from simultaneous transmitters in wireless networks. As it can provide more transmission opportunities without causing collisions, the network throughput performance can be dramatically improved. In this paper, we propose an asynchronous medium access control (MAC) protocol, which enables senders to independently start their transmissions if the access point (AP) can receive more simultaneous packets up to its multi-packet reception capability. This asynchronous protocol makes the multi-user MIMO channel more efficiently used, especially in wireless networks where transmission durations are dynamically varying due to different packet sizes and transmission rates. Through our performance analysis and extensive simulations, we show that the proposed asynchronous MAC protocol achieves significantly higher uplink throughput performance in multi-user MIMO wireless networks.