Privacy Protection Framework against Unauthorized Sensing in the 5.8 GHz ISM Band

Unauthorized sensing activities pose an increasing threat to individual privacy, particularly in the industrial, scientific, and medical (ISM) band where regulatory frameworks remain limited. This paper presents a novel signal process methodology to monitor and counter unauthorized sensing activities. Specifically, we model the pedestrian trajectories as a random process. Then, we leverage the Cram\'er-Rao bound (CRB) to evaluate sensing performance and model it as sampling error of such a random process. Through simulation, we verify the accuracy of monitoring unauthorized sensing activities in urban scenarios, and validate the effectiveness of corresponding mitigation strategies.

Heterogeneous System Design for Cell-Free Massive MIMO in Wideband Communications

Cell-free massive multi-input multi-output (CFmMIMO) offers uniform service quality through distributed access points (APs), yet unresolved issues remain. This paper proposes a heterogeneous system design that goes beyond the original CFmMIMO architecture by exploiting the synergy of a base station (BS) and distributed APs. Users are categorized as near users (NUs) and far users (FUs) depending on their proximity to the BS. The BS serves the NUs, while the APs cater to the FUs. Through activating only the closest AP of each FU, the use of downlink pilots is enabled, thereby enhancing performance. This heterogeneous design outperforms other homogeneous massive MIMO configurations, demonstrating superior sum capacity while maintaining comparable user-experienced rates. Moreover, it lowers the costs associated with AP installations and reduces signaling overhead for the fronthaul network.

Evaluation of Reinforcement Learning for Autonomous Penetration Testing using A3C, Q-learning and DQN

Penetration testing is the process of searching for security weaknesses by simulating an attack. It is usually performed by experienced professionals, where scanning and attack tools are applied. By automating the execution of such tools, the need for human interaction and decision-making could be reduced. In this work, a Network Attack Simulator (NASim) was used as an environment to train reinforcement learning agents to solve three predefined security scenarios. These scenarios cover techniques of exploitation, post-exploitation and wiretapping. A large hyperparameter grid search was performed to find the best hyperparameter combinations. The algorithms Q-learning, DQN and A3C were used, whereby A3C was able to solve all scenarios and achieve generalization. In addition, A3C could solve these scenarios with fewer actions than the baseline automated penetration testing. Although the training was performed on rather small scenarios and with small state and action spaces for the agents, the results show that a penetration test can successfully be performed by the RL agent.

Exploring 6G Potential for Industrial Digital Twinning and Swarm Intelligence in Obstacle-Rich

With the advent of 6G technology, the demand for efficient and intelligent systems in industrial applications has surged, driving the need for advanced solutions in target localization. Utilizing swarm robots to locate unknown targets involves navigating increasingly complex environments. Digital Twinning (DT) offers a robust solution by creating a virtual replica of the physical world, which enhances the swarm's navigation capabilities. Our framework leverages DT and integrates Swarm Intelligence to store physical map information in the cloud, enabling robots to efficiently locate unknown targets. The simulation results demonstrate that the DT framework, augmented by Swarm Intelligence, significantly improves target location efficiency in obstacle-rich environments compared to traditional methods. This research underscores the potential of combining DT and Swarm Intelligence to advance the field of robotic navigation and target localization in complex industrial settings.

Bridging Simulation and Measurements through Ray-Launching Analysis: A Study in a Complex Urban Scenario Environment

With the rapid increase in mobile subscribers, there is a drive towards achieving higher data rates, prompting the use of higher frequencies in future wireless communication technologies. Wave propagation channel modeling for these frequencies must be considered in conjunction with measurement results. This paper presents a ray-launching (RL)-based simulation in a complex urban scenario characterized by an undulating terrain with a high density of trees. The simulation results tend to closely match the reported measurements when more details are considered. This underscores the benefits of using the RL method, which provides detailed space-time and angle-delay results.

Cost-Effectiveness Analysis and Design of Cost-Efficient Cell-Free Massive MIMO Systems

Cell-free massive multi-input multi-output (MIMO) has recently attracted much attention, attributed to its potential to deliver uniform service quality. However, the adoption of a cell-free architecture raises concerns about the high implementation costs associated with deploying numerous distributed access points (APs) and the need for fronthaul network installation. To ensure the sustainability of next-generation wireless networks, it is crucial to improve cost-effectiveness, alongside achieving high performance. To address this, we conduct a cost analysis of cell-free massive MIMO and build a unified model with varying numbers of antennas per AP. Our objective is to explore whether employing multi-antenna APs could reduce system costs while maintaining performance. The analysis and evaluation result in the identification of a cost-effective design for cell-free massive MIMO, providing valuable insights for practical implementation.

GNN-based Anomaly Detection for Encoded Network Traffic

The early research report explores the possibility of using Graph Neural Networks (GNNs) for anomaly detection in internet traffic data enriched with information. While recent studies have made significant progress in using GNNs for anomaly detection in finance, multivariate time-series, and biochemistry domains, there is limited research in the context of network flow data. In this report, we explore the idea that leverages information-enriched features extracted from network flow packet data to improve the performance of GNN in anomaly detection. The idea is to utilize feature encoding (binary, numerical, and string) to capture the relationships between the network components, allowing the GNN to learn latent relationships and better identify anomalies.

Unified Modeling and Performance Comparison for Cellular and Cell-Free Massive MIMO

Cell-free massive multi-input multi-output (MIMO) has recently gained a lot of attention due to its high potential in sixth-generation (6G) wireless systems. The goal of this paper is to first present a unified modeling for massive MIMO, encompassing both cellular and cell-free architectures with a variable number of antennas per access point. We derive signal transmission models and achievable spectral efficiency in both the downlink and uplink using zero-forcing and maximal-ratio schemes. We also provide performance comparisons in terms of per-user and sum spectral efficiency.

Cell-Free Terahertz Massive MIMO: A Novel Paradigm Beyond Ultra-Massive MIMO

Terahertz (THz) frequencies have recently garnered considerable attention due to their potential to offer abundant spectral resources for communication, as well as distinct advantages in sensing, positioning, and imaging. Nevertheless, practical implementation encounters challenges stemming from the limited distances of signal transmission, primarily due to notable propagation, absorption, and blockage losses. To address this issue, the current strategy involves employing ultra-massive multi-input multi-output (UMMIMO) to generate high beamforming gains, thereby extending the transmission range. This paper introduces an alternative solution through the utilization of cell-free massive MIMO (CFmMIMO) architecture, wherein the closest access point is actively chosen to reduce the distance, rather than relying solely on a substantial number of antennas. We compare these two techniques through simulations and the numerical results justify that CFmMIMO is superior to UMMIMO in both spectral and energy efficiency at THz frequencies.

A Robust UAV-Based Approach for Power-Modulated Jammer Localization Using DoA

Unmanned aerial vehicles (UAVs) are well-suited to localize jammers, particularly when jammers are at non-terrestrial locations, where conventional detection methods face challenges. In this work we propose a novel localization method, sample pruning gradient descend (SPGD), which offers robust performance against multiple power-modulated jammers with low computational complexity.