Jamming attacks pose a critical threat to wireless networks, particularly in cell-free massive MIMO systems, where distributed access points and user equipment (UE) create complex, time-varying topologies. This paper proposes a novel jamming detection framework leveraging dynamic graphs and graph convolution neural networks (GCN) to address this challenge. By modeling the network as a dynamic graph, we capture evolving communication links and detect jamming attacks as anomalies in the graph evolution. A GCN-Transformers-based model, trained with supervised learning, learns graph embeddings to identify malicious interference. Performance evaluation in simulated scenarios with moving UEs, varying jamming conditions and channel fadings, demonstrates the method’s effectiveness, which is assessed through accuracy and F1 score metrics, achieving promising results for effective jamming detection.
A promising artificial intelligence (AI)-driven cross-layer modulation is proposed to perform the bit-symbol mapping for the multiple-input-multiple-output (MIMO) transmission mode in the next generation wireless communication system. Unlike the prior modulation technologies, multiple influence factors including he constellation mapping, the number of spatial layers, layer mapping and power allocation among spatial layers are simultaneously taken into account in the proposed method pursuing for spectral efficiency maximization via auto-encoder (AE) model. In this paper, a joint optimization subject to influence factors above for the cross-layer modulation is fulfilled based on the AE framework for simplifying the design complexity and achieving a better performance. Simulation results demonstrate that the block error rate (BLER) performance of the proposed cross-layer modulation is beyond the traditional scheme by around 1 dB as a result of the learned bit-symbol mapping for spatial layers.
To enhance target sensing capabilities in low Earth orbit (LEO) satellite communication systems, this paper proposes a LEO satellite-enabled integrated sensing and communication (ISAC) beamforming (LSEIB) algorithm. Specifically, a LEO satellite-enabled bistatic ISAC system is constructed, in which the LEO satellite simultaneously transmits both communication and sensing signals, while the ground gateway receives the echoes to enable cooperative sensing. To balance communication performance and sensing accuracy, the upper bound of the achievable communication rate based on statistical channel state information and the Cramer-Rao bound (CRB) expression for two-dimensional angle-of-arrival estimation are derived. A joint beamforming optimization problem is then formulated with the objective of minimizing the CRB. Subsequently, a semidefinite relaxation method is introduced to transform the original non-convex problem into an equivalent semidefinite program, and a closed-form rank-one solution is constructed to satisfy the rank constraint. Simulation results demonstrate that the proposed LSEIB algorithm exhibits great convergence and optimization performance under various settings, and the proposed bistatic architecture achieves significant improvements in sensing performance compared to the monostatic counterpart.
This paper presents an IoT-enabled digital twin framework designed to enhance real-time monitoring and management of freshwater pollution. The framework integrates distributed IoT sensors to collect high-resolution data on dissolved oxygen (DO), temperature, and pollution dynamics, enabling the timely detection of ecological stressors. A four-layer architecture, comprising device, virtualisation, aggregation, and service layers, facilitates scalable data processing, visualisation, and stakeholder engagement. Key contributions include empirical validation of the IoT-enabled digital twin framework to monitor fresh water pollution, a modular system for anomaly detection and historical trend analysis, and actionable insights derived from spatially distributed edge nodes. The results highlight the inverse correlation between temperature and DO levels, disrupted during storm events by abrupt oxygen crashes (e.g. below 4 mg/l) and the critical role of edge node deployment in the river bed. The digital twin monitoring, recollection, and predictive modules collectively support proactive water quality management, identifying pollution gradients and seasonal patterns. Testing and evaluation on the Derwent river shows the system’s practical efficacy in reducing environmental risks and informing sustainable interventions.
This study presents a bio-hybrid implant comprising a passive microwave resonator and a colony of engineered E. coli that express proteins in response to thermal stimuli. We propose a novel method for wireless control of the genetically modified bacteria using microwave hyperthermia. Synthetic biology has enabled cell-based sensing and actuation; however, the lack of viable wireless communication mechanisms with those cells limits their usage inside the human body. Direct electromagnetic interaction with individual cells would require terahertz and beyond frequencies, which are impractical for in-body use due to high tissue absorption. Instead, we introduce a wireless control strategy based on focused microwave hyperthermia, delivered via an on-body antenna operating between 0.782 GHz and 1.938 GHz. Cellular activation is achieved by localized heating at the implant site, created by the passive resonator. Preliminary results demonstrate that the system can achieve a localized temperature increase of more than 6 degrees C in 5 minutes with 1 W transmit power to activate heat-sensitive genetic circuits, thus establishing a proof-of-concept for wireless thermal control of biological function.
Unmanned aerial vehicles (UAVs) are emerging as key enablers for adaptive connectivity in future sixth-generation (6G) networks, offering high mobility, flexible deployment, and enhanced line-of-sight coverage. However, the open nature of wireless communication and the elevated positioning of UAVs as a relay expose them to severe security threats, including jamming and eavesdropping. In this work, we propose a secure downlink communication framework where a UAV relay flying at a fixed altitude serves multiple ground users while contending with an active jammer and a passive eavesdropper. To enhance physical layer security, we jointly optimize the UAV’s trajectory and transmit power over a discrete time horizon to maximize the cumulative secrecy rate. The resulting non-convex optimization problem is solved using both a successive convex approximation (SCA) method and deep reinforcement learning (DRL). Simulation results demonstrate the optimized trajectories in various user topologies and shed light on the relationship between secrecy energy efficiency and maximal transmit power of the UAV relay as well as the number of users.
Metasurface-aided near-field radio imaging is emerging as an essential part of the future mmWave communication systems. Adjusting the metasurface phase shift to generate multiple measurements can significantly increase the system imaging aperture and enhance the resolution. However, this imaging technique relies heavily on precise measurements, and high-precision sampling leads to expensive hardware costs and memory burdens. To address this issue, this paper draws inspiration from binary compressive sensing and proposes a method for imaging under one-bit quantization. First, we propose a dynamic metasurface-aided mmWave imaging system with one-bit sampling, which simplifies the received signal acquisition process and significantly reduces the hardware and storage requirements. Then, a fused binary compressive sensing model is developed with an additional total-variation norm penalty to promote target continuity and suppress artifacts in mmWave images. Subsequently, we employ the proximal binary iterative hard thresholding algorithm to optimize the joint sparsity and the total variation (TV) constraints. In addition, the hybrid l(1) -TV constraint is introduced to solve the problem of unknown a priori sparsity of image, and the alternating direction multiplication method is designed for effective reconstruction. Finally, the simulation results show that the proposed algorithms can utilize the target features under binary measurements and achieve better imaging accuracy and focusing performance than the sparsity constraint-only methods.
This paper presents a Hardware-in-the-Loop demonstration of a 5G Integrated Access and Backhaul (IAB) system operating in the FR2 band. The proposed implementation explores the feasibility of IAB for millimeter-wave (mmW) networks, leveraging a Transmissive Reconfigurable Intelligent Surface (T-RIS) to enhance performance. The experimental setup includes two collocated transmitters (TXs) and two receivers (RXs), where one TX-RX pair establishes the backhaul link and the other the access link. The MATLAB 5G toolbox is used for 5G NR waveform generation and reception; the interface between the physical layer and the RF is based on Hardware Description Language - allowing for fast reconfiguration - and a T-RIS assists in optimizing signal propagation. We evaluate the effectiveness of beam search techniques utilizing Synchronization Signal Blocks and their capability to mitigate signal blockage challenges. We furthermore demonstrate that the use of a T-RIS enables effective spatial separation of receivers. These results prove the viability of IAB for mmW deployments, showing improvements in signal coverage and link reliability. This work provides a foundation for future real-time adaptive IAB implementations, aiming to enhance 5G network efficiency in dense urban environments.
In this paper, we propose an improved anomaly detection algorithm for O-RAN Near-RT RIC environments by enhancing the Isolation Forest-based xApp deployed for traffic steering. Conventional implementations rely on arbitrary or correlation-based feature selection and (semi-)supervised-like hyperparameter tuning, which can result in suboptimal performance. To address these issues, we introduce a proxy label generation method grounded domain expertise, enabling evaluation-driven hyperparameter optimization. We further propose a three-step feature selection framework to produce interpretable and effective feature subsets. Experimental results demonstrate that our proposed method consistently improves F1-score performance, with Bayesian optimization showing the most robust results. Furthermore, our analysis highlights the value of retaining certain correlated features that may be beneficial for tree-based models like Isolation Forest, counter to common practices in feature selection. These findings validate the proposed framework’s applicability in O-RAN anomaly detection and offer new directions for interpretable xApp development.
We use Bayesian game theory to investigate the interaction between a system controller and an additional unknown agent in a cyber-physical system. The system controller performs some monitoring for real-time operation management, with the aim of minimizing the age of incorrect information (AoII). The additional agent reports some extra information, which ideally can serve to aid the controller and meet the same objective of decreasing AoII, but it is uncertain whether these actions are useful or correspond to (possibly international) false data injection in the system. The controller only has information in terms of probability of the legitimacy of this extra agent through a common prior, and also knows that, in case it is malicious, it will try to increase AoII instead. Our analysis reveals that, under rational behavior, an adversary can effectively masquerading as a sensor injecting legitimate data, as the controller can hardly distinguish the behavior of a true helper from that of an attacker. However, under variable data drift, the strategic behavior of the external agent can give away their type.
The localization of near-field sources is crucial in wireless communications. Practical arrays have mutual coupling between the elements of the array that can significantly affect the accuracy of localization algorithms. In this paper, we utilize a practical array to evaluate the performance of an iterative method based on oblique projection (IMOP) in estimating the location of near-field sources. The conventional two-dimensional (2D) search method for near-field source localization (TSMNSL) employs a 2D search to estimate the direction of arrival (DOA) and range of the source, resulting in a high computational load. In contrast, the IMOP method uses one-dimensional searches, which significantly reduces computational complexity. The results of numerical and full-wave electromagnetic simulations in this paper show that the performance of DOA and range estimations in the IMOP method is comparable to that in the TSMNSL method. The simulation results further indicate that the computational complexity of the IMOP method is at least 49 times lower than that of the TSMNSL method.
While millimeter wave (mm-Wave) frequencies can meet the high data rate demands, their combination with high-mobility scenarios causes significant Doppler shifts that degrade the error performance of the commonly used orthogonal frequency division multiplexing (OFDM) modulation. Orthogonal time frequency space (OTFS) modulation has been introduced as an alternative approach that offers resilience to Doppler shifts by modulating symbols directly in the delay-Doppler (DD) domain. Although many studies have demonstrated that OTFS modulation outperforms OFDM in terms of error performance, these evaluations are largely influenced by the particular channel equalization method employed. In this paper, we focus on three linear minimum mean-square error (MMSE) channel equalization and detection in the time, frequency, and DD domains, and we investigate the error performance of OTFS and OFDM in high-mobility mm-Wave channels with various coding rates and modulation orders. The simulation results show that OTFS significantly outperforms OFDM in terms of error performance when the frequency domain equalization is applied to OFDM, regardless of the modulation order and coding rate. However, this advantage diminishes when time domain equalization is used with high-order modulations and low coding rates. In particular, OFDM shows a signal-to-noise ratio (SNR) of 0.8 dB better than OTFS in error performance when using 64-quadrature amplitude modulation (QAM) and a coding rate of 251/1024.
In future wireless communication, an exponential increase in connected devices and the need for safety-critical and data-intensive applications like Autonomous Vehicles (AVs) and eXtended Reality (XR) will render large-scale intelligent agent deployments infeasible. To address this, we propose a novel framework where a centralized Smart Service Provider (SSP) transmits actions to agents, balancing effectiveness and transmission costs. Our approach leverages the observation that both semantic information extraction from raw sensor data and action generation from semantic states are many-to-one mappings. We model action decisions using stochastic policies from Reinforcement Learning (RL) literature. A key novelty is our focus on when to transmit. Namely, the SSP, observing what an agent observes, estimates the agent’s likely action. Based on the weighted statistical distance between the SSP’s policy and the agent’s estimated policy, the SSP decides whether an "action innovation" has occurred, and if so, transmits an action to the agent. Simulations demonstrate that transmitting only 10% of the time is sufficient to satisfy goals with up to 1% deviation, and compression rates up to 8,108:1 are achievable compared to existing standards. This "effective communication" scheme significantly reduces transmission overhead while maintaining high performance.
Integrated sensing and communication (ISAC) in multi-hop wireless networks is a key technology for supporting a wide range of emerging Internet of Things (IoT) applications, addressing challenges such as spectrum scarcity and limited network coverage. To investigate the performance trade-off between end-to-end communication and sensing in these networks, this paper focuses on maximizing the end-to-end communication rate while ensuring the overall sensing performance in multiple-input multiple-output (MIMO) ISAC multi-hop wireless networks. In order to achieve this, we formulate a mixed-integer nonlinear programming (MINLP) problem that is highly non-convex and difficult to solve directly. To address this difficulty, we first transform the MINLP problem into a more tractable form through a series of equivalent transformations. We then propose an efficient algorithm based on generalized Benders decomposition (GBD) to solve the transformed problem optimally. Finally, numerical results demonstrate that the proposed algorithm achieves the optimal performance obtained by the exhaustive search method but with much lower complexity.
With the high-speed and low-latency connection capabilities brought by the wireless communications, the performance of unmanned aerial vehicle (UAV) swarm for executing tasks such as emergency response, smart farming, and aerial base station has been significantly improved. As the use of UAVs becomes more prevalent in various tasks, the demand for cross-domain task allocation and operations continues to grow. Cross-domain task allocation enables the UAVs to flexibly execute tasks over a wider range, work together in different task domains, thereby improving operation efficiency. However, due to variations in management policies, authentication protocols, and security standards across various domains, to ensure the secure and reliable interoperability of UAVs across diverse domains, cross-domain authentication has become the primary line of defense for UAVs. In this paper, we propose a lightweight and trusted blockchain-based cross-domain authentication scheme for UAV swarm. To achieve a lightweight authentication process, we apply certificateless authentication and avoid costly operations for resource-constrained UAVs. Additionally, the trustworthiness of UAVs and domains is evaluated through credibility, which is stored on blockchain. We analyze the theoretical security of the proposed scheme, and extensive experiments demonstrate its efficiency.
Multiple base stations (multi-BS) cooperative sensing is considered a promising solution for achieving high-accuracy sensing and robust connectivity in future sixth-generation (6G) mobile communication systems. In this paper, we investigate the design of a target localization scheme for a cooperative integrated sensing and communication (ISAC) system. Specifically, our objective is to achieve high-accuracy target localization without compromising communication performance. To accomplish this, we propose a high-accuracy sensing scheme consisting of two stages. In the first stage, we develop a novel noise subspace re-projection orthogonal matching pursuit (NSR-OMP) algorithm to enable simultaneous, high-accuracy estimation of multipath parameters in a high-dimensional signal space. In the second stage, we introduce a data fusion algorithm based on the weighted average method, which effectively mitigates the impact of ill-conditioned measurements within the bistatic ranges. Finally, simulation results demonstrate that the proposed sensing scheme enhances sensing accuracy by approximately 78.9% compared to benchmark schemes, effectively validating its feasibility in practical systems.
The increasing demand for emerging vehicular services, such as immersive entertainment, safety applications, and enhanced infotainment, has driven the development of Vehicle-to-Everything communication. However, vehicular networks face significant challenges due to stringent Quality of Service (QoS) requirements and the highly dynamic nature of wireless environments. Traditional Radio Access Network (RAN) architectures struggle to adapt to these conditions, necessitating more flexible and intelligent solutions. Open RAN, with its virtualised and intelligent architecture, offers a promising approach by incorporating Artificial Intelligence and Machine Learning for real-time network optimisation. This paper proposes a Deep Reinforcement Learning (DRL)-based Modulation and Coding Scheme (MCS) selection algorithm within an Open RAN-enabled vehicular network to optimise resource usage while ensuring QoS compliance. The proposed algorithm leverages the flexibility of Open RAN and the adaptability of DRL to dynamically configure MCS parameters, enhancing the Quality of Experience for users in challenging vehicular scenarios. Simulation results demonstrate that the DRL-based approach reduces network resource usage by 33% compared to conventional SNR-based MCS selection while improving QoS satisfaction by approximately 5%.
This paper proposes an extension of CNN-based demodulation in image sensor-based visible light communication from single-color to multi-color processing. Conventional CNN-based demodulation methods mainly focus on a single color space, limiting the utilization of chromatic information. In this study, we introduce a multiplexed chromatic modulation by incorporating both Cr and Cb components into the demodulation process. To evaluate the proposed method, we conducted fundamental experiments using 24 different monochromatic backgrounds. The performance of our approach was compared with the conventional cell averaging method in terms of bit error rate (BER). Experimental results demonstrate that the proposed CNN-based demodulation with multi-color processing achieves a lower BER than conventional methods, indicating improved communication quality in image sensor-based visible light communication.
