This paper presents a robust near-field (NF) beam tracking algorithm for terahertz communications based on deep Q-network (DQN). Traditional NF beam tracking methods relying on mobility models are fatal in ultra-massive MIMO systems, where even the slightest error could result in beam tracking failures. Thus, the proposed algorithm aims to maintain a stable beamforming gain by tracking the mobile station through the analysis of received signals without requiring mobile dynamics. By utilizing DQN, the proposed algorithm strengthens its tracking capability from online experiences and updates the combining beam towards positions expected to maximize beamforming gain. Throughout simulations, we compare the proposed algorithm with the Bayesian filter-based NF beam tracking algorithm. The simulation results confirm the robustness of the proposed algorithm for NF beam tracking, especially for abrupt changes in mobile dynamics.

In recent years, cooperative positioning technologies have emerged as promising augmentation systems for providing high-accuracy positioning (HAP) in cooperative intelligent transportation systems (C-ITS). Among the approaches, implicit cooperative positioning (ICP) takes advantage of shared target detections between vehicles to create common reference points for localization refinement. Their performance, however, is limited by reliance on predefined parametric models, low scalability and communication overhead. To address these problems, this paper introduces a deep multi-agent reinforcement learning (MARL) framework modelled as a decentralized-partially observable Markov decision process (Dec-POMDP). We propose an ICP-multi-agent proximal policy optimization (MAPPO) algorithm, where distributed agents (i.e., the connected vehicles) learn their dynamics and those of the surrounding targets by performing belief estimation over dynamic cooperation graphs that are continuously adjusted by de/activating communication links with neighbors agents. A C-ITS scenario is simulated in a CARLA environment accounting for realistic vehicle dynamics and inter-vehicle communications. The findings reveal that our ICPMAPPO algorithm, leveraging dynamic decentralized execution and centralized training, outperforms ICP in terms of positioning accuracy and communication efficiency.

This paper explores filtering methods to protect range-based localization systems from spoofing attacks on vehicles with directional receivers. It focuses on scenarios where multiple spoofers, potentially from unmanned vehicles, disrupt vehicle localization by strategically positioning themselves between the target and the transmitter. The paper introduces an Adaptive Resilience Navigation Filter (ARNF) that detects ongoing attacks, identifies compromised signals, and mitigates their effects using statistical hypothesis testing. Simulations demonstrate the ARNF’s effectiveness under realistic Global Navigation Satellite System conditions, comparing it with the 2-Stage Extended Kalman Fitter and an ideal Clairvoyant Extended Kalman Filter.

Location aware networks will introduce new applications and services for modern convenience, the military, and public safety. In this paper, we introduce a Bayesian method for network localization and navigation in the presence of measurement-origin uncertainty (MOU). In the envisioned cooperative scenario, the agents in a dynamic network aim to better localize themselves by performing pairwise observations with other agents in their environment and sharing their location information. Since pairwise observations suffer from MOU, a data association problem has to be solved before an agent can update its location information. In our approach, joint inference is performed through a factor graph formulation of the entire, network-wide estimation problem. Performing the loopy sum-product algorithm on the derived factor graph results in a distributed and scalable inference algorithm. Simulation results demonstrate that cooperation among agents can significantly improve the localization accuracy even in the presence of MOU.

We study the asymptotic detection performance of large sensor networks, configured as trees with bounded height, in which information is progressively compressed as it moves towards the root of the tree. We show that the error probability decays exponentially fast, and we provide bounds for the error exponent. We analyze further the case where the tree has certain symmetry properties, and derive simple, easily implementable, suboptimal strategies