Timely detection and localization of non-cooperative unmanned aerial vehicles (UAVs) in complex urban environments is essential for low-altitude security monitoring and rapid response. However, single-modality sensing often suffers from occlusion, multipath, non-line-of-sight propagation, and constrained backhaul capacity, thereby degrading long-range detectability and localization accuracy. These limitations motivate multimodal sensing to improve robustness via complementary measurements. This paper presents a distributed ground-based multimodal sensing network and an online Mutual-Information-Driven Adaptive Scheduling (MIDAS) algorithm. The proposed system integrates received-signal-strength (RSS)-based RF sensing with acoustic angle-of-arrival (AoA) sensing, while an optical module can be triggered for high-precision refinement and tracking. Under bandwidth constraints, MIDAS employs an information-theoretic greedy policy that selects a subset of sensors for cooperative localization by maximizing the incremental mutual information. At the fusion center, covariance-weighted fusion is performed to suppress low-reliability modalities. To address RF-silent UAVs, we incorporate a degradation-aware acoustic fallback selection strategy to maintain localization during RF outages. System-level simulations over multiple urban ingress routes and UAV types demonstrate robust performance under heterogeneous sensing conditions.

With the rapid popularization of Internet of Things (IoT) devices, the freshness of data has become a key factor affecting decision quality and system efficiency. The application of unmanned aerial vehicle (UAV) technology provides a new solution for IoT data collection. This article mainly studies how multiple UAVs can improve the freshness of IoT data collection through joint optimization of trajectory planning and channel selection in a three-dimensional (3D) interference environment. We conducted markov decision process (MDP) modeling on the combinatorial optimization problem of the model and proposed an intelligent joint trajectory planning and channel selection for data collection (ITPCS-DC) algorithm based on multi-agent deep reinforcement learning (MADRL). This algorithm can not only avoid the agent falling into local optimum caused by 3D interference, but also effectively reduce the age of information (AoI) of IoT data collection. Simulation results show that the proposed ITPCS-DC algorithm can achieve higher rewards, lower average AoI, reduced channel switching costs, and shorter trajectory lengths compared to other benchmark algorithms. Moreover, it has better adaptability to more complex collaborative environments.

This paper addresses the asynchronous multi-user dynamic spectrum access (DSA) problem in half-duplex cognitive radio networks (HD-CRNs). The core challenge lies in resolving the decision asynchrony caused by variable action duration under HD constraints, which traditional deep reinforcement learning (DRL) methods fail to address. The problem is formulated as a Macro-Action Decentralized Partially Observable Markov Decision Process (MacDec-POMDP), with a decentralized macro-action-based multi-agent Dueling Double Deep Q-Network (D3QN) framework being proposed. An extra reward mechanism is specifically designed to encourage sustained conflict-free access. Simulation results demonstrate that our approach outperforms synchronous fixed-duration methods by dynamically adjusting access duration. The reward function design significantly improves adaptability, reducing collisions to both primary and secondary users while optimizing the spectrum utilization under HD constraints, validating its effectiveness in asynchronous multi-user DSA.

Spectrum sharing problem of the cognitive radio networks (CRN) with non-stationarity of secondary users (SUs) traffic demands, which could undermine the performances of traditional dynamic spectrum access (DSA) methods, is addressed in this study. The SU traffic arrival pattern is modelled with Poisson process and a SU-traffic-aware multi-agent deep reinforcement learning (MADRL) method is proposed to optimize the spectrum sharing among SUs. Each agent firstly learns the traffic patterns of both local SU and primary users (PUs) to avoid collisions and to improve quality of service (QoS) of SU. Moreover, a priori-rule-assisted DRL algorithm is designed to accelerate agent's training process by enforcing each agent to execute preset actions under specific states. Simulation results show that the proposed method achieves significant improvement on SU QoS while maintains low SU-PU collision probability.

ABSTRACT Spectrum maps are visualization tools that reflect the underlying spectral environment, enabling advanced functions such as spectrum decision‐making and emitter identification. To enhance mapping accuracy and optimize resource utilization, this study addresses the sensor node selection problem in ground‐based sensing scenarios. We propose an entropy‐greedy node selection (EGNS) framework that employs a two‐stage scheduling strategy: the first stage performs coarse sensing via spatial sector partitioning to obtain an initial estimate of emitter locations, and the second stage executes an enhanced greedy selection algorithm to iteratively minimize the signal reconstruction error. Simulation results on real‐world spectrum datasets show that the proposed method achieves superior reconstruction accuracy and lower sensing costs compared to conventional sampling approaches, making it well‐suited for dynamic electromagnetic monitoring applications under constrained budgets.

Accurate radio environment maps (REMs) can enhance the performance of wireless networks and optimize spectrum utilization efficiency. However, in rugged terrain environments, radio propagation is significantly affected by terrain variations, resulting in spatial heterogeneity in received signal strength (RSS) and impairing the accuracy of REM construction. To address these challenges, a Gaussian Process Regression method incorporating terrain (GPRT) is proposed to exploit both spatial and terrain correlation properties. In GPRT, a specialized kernel function is designed to integrate digital elevation data into the Gaussian process framework, capturing anisotropic spatial correlation and terrain effects. In addition, an Adaptive Moment Estimation (Adam) optimization algorithm is utilized for efficient hyperparameter tuning, enhancing convergence speed and parameter accuracy. Simulations with varying numbers of emitters and field experiments in real-world terrain demonstrate the superiority and effectiveness of the proposed GPRT over competing methods in terms of robustness and accuracy. Specifically, GPRT outperformed the best comparative approaches by 20% to 33% in simulations and by up to 20% in the field experiment.

With the rapid advancement of unmanned aerial vehicle (UAV) technology, its high mobility and ease of deployment have demonstrated tremendous potential in integrated sensing and communication (ISAC) systems. However, as user demands diversify, network architectures become increasingly distributed, and as the number of UAVs grows rapidly, ISAC systems face significant challenges in optimizing communication and sensing resources. In particular, UAV communication in collaborative UAV missions is highly susceptible to hostile signal disruptions, leading to degraded communication quality, weakened sensing performance, resource wastage, and increased energy consumption. To address these challenges, this paper proposes a game-theoretic optimization method for multi-UAV ISAC networks. First, a multi-UAV communication-sensing network model is constructed to characterize the impact of interference sources on communication and sensing performance. Based on this model, under energy constraints, a joint optimization of transmission power and UAV trajectory is performed. The problem is formulated as a utility maximization framework for the ISAC network and modeled as a game-theoretic approach. The proposed model is rigorously proven to be an exact potential game, ensuring the existence of at least one pure-strategy Nash equilibrium. To solve for the equilibrium, a distributed optimization algorithm—the electric eel foraging optimization (EEFO) algorithm is developed. Simulation results validate the effectiveness of the proposed method, showing that it significantly enhances the communication and sensing performance of multi-UAV networks while effectively reducing energy consumption. This work provides a novel solution to the resource optimization challenges in multi-UAV ISAC networks, offering both theoretical and practical contributions to advance ISAC technology.

We consider the network-side mobile localization problem in future 5G and beyond wireless networks with distributed multi-antenna base stations (BSs). For this application, we propose a semi-supervised multi-point channel charting (SS-MPCC) framework, which consists of (i) collaborative collection of channel state information (CSI) and other side-information by distributed BSs; (ii) local CSI feature extraction and self-learning of a dissimilarity metric, and (iii) global graph construction and constrained manifold learning. We show that side-information from routine network operations, including timestamps, channel qualities, and a small set of labeled samples, can be exploited to construct a consistent global graph. The graph is then mapped to a 2D channel chart using constrained manifold learning for localization purposes. We evaluate the performance of SS-MPCC in a simulated urban outdoor scenario with realistic user motion. Our results show that SS-MPCC achieves a mean localization error of 5.6 m with only 10% of labeled CSI samples. SS-MPCC does not require accurate synchronization among multiple BSs and is promising for future cellular localization.

For a cognitive radio network (CRN) in which a set of secondary users (SU) competes for a limited number of channels (spectrum resources) belonging to primary user, the channel allocation is a challenge and dominates the throughput and congestion of the network . In this paper, the channel allocation problem is first formulated as the 0-1 integer programming optimization, with considering the overall utility both of primary system and secondary system. Inspired by matching theory, a many-to-one matching mechanism is used to remodel the channel allocation problem, and the corresponding PU proposing deferred acceptance (PPDA) algorithm is also proposed to yield a stable matching. We compare the performance and computation complexity between these two solutions. Numerical results demonstrate the efficiency and obtain the communication overhead of the proposed schemes.

Abstract In the heterogeneous and unreliable channel environment of cognitive radio ad hoc networks (CRAHNs), a multipath route with channel assigned is preferable in both throughput and reliability. The cross‐layer multipath routing and channel assignment in CRAHNs is becoming a challenging issue. In this paper, this problem is characterized, formulated, and shown to be in the form of mixed integer programming. For this Non‐deterministic Polynomial‐time (NP)‐hard problem, the deficiency of the widely used linearization and sequential fixing algorithm is first analyzed. The main contribution of this paper is the development of a new backtracking algorithm with feasibility checking to search optimal solutions and a heuristic algorithm with high feasible solution‐obtained probability (HHFOP) for distributed application in CRAHNs. Through feasibility checking and solution bounds validating, backtracking algorithm with feasibility checking cuts off unnecessary searching space in early stage without loss of optimal solutions, making it much more efficient than brute searching. For practical application in CRAHNs with polynomial complexity, HHFOP first computes the maximal‐supported throughput through link‐channel assignment and link‐capacity coordination for each candidate path. Then the paths are combined, and the route throughput is optimized. Extensive simulation results demonstrate that HHFOP can achieve a high feasible solution‐obtained probability with little throughput degradation compared with linearization and sequential fixing algorithm, indicating its practicability for distributed applications in CRAHNs. Copyright © 2013 John Wiley & Sons, Ltd.