ABSTRACT Cognitive Spectrum Sensing (CSS) stands as a foundational component in 5G and emerging 6G wireless communication systems, enabling intelligent identification and dynamic utilisation of underutilised spectrum bands. However, the implementation of CSS in dense and heterogeneous 5G/6G environments presents significant challenges, including high spectral dynamics, multi‐protocol interference, and the requirement for real‐time decision‐making across diverse frequency bands. Existing methods such as deep belief networks, CNN‐PSO hybrids, and DQN‐based models suffer from limited adaptability, insufficient spatial‐temporal learning, and poor generalisation in real‐world RF environments. The proposed model includes a dual‐stream deep learning architecture which has a 1D convolutional neural network (CNN)‐based spectral encoder and a graph convolutional network (GCN)‐based spatial encoder for extracting the frequency‐domain and node‐topology features. Experimental analysis of proposed model is performed using the Real‐World Wireless Communication Dataset containing Wi‐Fi, LTE, and 5G RF signals. The dataset is pre‐processed using Fast Fourier Transformation (FFT) transformation and labelled through a signal‐power‐based thresholding mechanism. Results indicate the Spectral‐Spatial Dual Encoder with Bio‐Inspired Swarm Adaptation (SSDE‐BSA) achieves an accuracy of 96.1%, an F1‐score of 96.1%, and a spectral efficiency of 91. These results confirm the model's superiority in adapting to real‐world spectrum dynamics, offering a robust and scalable solution for cognitive spectrum sensing in next‐generation wireless networks.

ABSTRACT Reconfigurable intelligent surface (RIS)‐assisted received spatial modulation (RIS‐RSM) has emerged as a promising technique to enhance spectral and energy efficiency in next‐generation wireless systems. However, performing accurate signal detection without channel state information (CSI) remains a critical challenge, particularly in blind detection scenarios. In this paper, we propose a novel clustering‐based blind detector named energy‐tiered structure initialization (ETSI). The proposed method exploits the amplitude heterogeneity of modulation symbols—originating from the unequal energy levels of QAM or hybrid constellations—by partitioning the signal space into multiple energy tiers. Each receive antenna is associated with a structure prototype, representing a normalized statistical channel pattern, which is initialized using the distribution of the underlying fading model (e.g., Rayleigh) rather than instantaneous CSI. During clustering, these prototypes are iteratively refined through tier‐wise averaging of normalized signal samples, thereby enforcing structural consistency and mitigating amplitude‐induced bias. After convergence, the constellation‐aligned cluster centres are reconstructed by combining the updated prototypes with their corresponding modulation amplitudes, inherently enabling the joint detection of both the modulation symbol and the RIS‐assisted spatial index. Simulation results show that ETSI achieves around 0.5–1 dB SNR gain over amplitude–phase aware clustering under 8PSK, and about 1–1.5 dB improvement under 16QAM, while outperforming the CSI‐based greedy detector (GD) across both modulations. Moreover, ETSI achieves BER performance close to that of the perfect‐CSI maximum likelihood detector, confirming its accuracy, scalability and practical feasibility for blind RIS‐RSM detection.

ABSTRACT With the rapid advancement of communication technologies, space‐air‐ground integrated networks (SAGIN) have become a pivotal research frontier in current and future communication domains. To tackle critical challenges in SAGIN scenarios, such as excessive task‐related energy consumption and insufficient communication‐computing resources, this paper proposes a three‐tier edge computing architecture integrating satellites, unmanned aerial vehicle (UAV) swarms, and ground systems. Aiming to minimize the system's weighted energy consumption and latency, we investigate the joint optimization of task allocation, user‐UAV association, UAV deployment, and resource allocation between UAVs and low‐earth orbit (LEO) satellites. Formulated as a non‐convex mixed‐integer nonlinear combinatorial optimization problem, this work integrates the branch‐and‐bound method, multi‐start global optimization, and gray wolf optimization (GWO) to develop a suboptimal solution based on block coordinate descent (BCD), which decouples the original problem into three subproblems for independent solving and iterative approximation of the optimal solution. Experimental results show that the proposed algorithm reduces the total system cost by 7.81%, 11.99%, and 45.69% compared with baseline algorithms with random user‐UAV association, random UAV positioning, and random task assignment, respectively, effectively cutting down overall energy consumption and task latency.

ABSTRACT We present composite deep bidirectional long short‐term memory (CDBi‐LSTM), a compact flow‐level detector for Internet of Things (IoT) distributed denial of service (DDoS) attacks that couples a CNN stream and a BiLSTM stream, equips each stream with self–attention and residual connections , and combines them via attention‐based fusion . To reflect heterogeneous deployments while avoiding dataset bias, we train and evaluate separately on three public benchmarks: CICDDoS2019, NF‐BoT‐IoT‐v3, and NF‐ToN‐IoT‐v3, under a consistent methodology. The model attains excellent performance: 99.95% accuracy on CICDDoS2019 (binary) and 99.85% (7‐class), 99.99% on NF‐BoT‐IoT‐v3, and 99.85% on NF‐ToN‐IoT‐v3, with very low false positives/negatives confirmed by confusion matrices. Loss curves show fast and stable convergence. A complexity analysis demonstrates edge viability: MB–scale footprint (1.38–1.52 MB; 361k–398k parameters), tiny RAM deltas at load (1.70–1.98 MB), and CPU latency in the tens of milliseconds (61.9–69.0 ms). An ablation study isolates the contributions of per‐stream self‐attention, per‐stream residuals, and gated fusion, revealing favourable accuracy‐efficiency trade‐offs relative to simpler variants. On CICDDoS2019, the method is competitive with or surpasses the state of the art while providing concrete runtime and memory guarantees. Together, these results indicate that CDBi‐LSTM is both accurate and deployment‐ready for real‐time IoT defence, with a clear path to further optimisation and cross‐hardware validation.

ABSTRACT Mobile devices pose several distinct challenges from a security perspective. First, they have varied and ephemeral network connections, often using a cellular provider network as a backup option when connectivity is not available via wireless local access networks. This varied network connectivity makes it difficult to comprehensively deploy in‐network solutions, such as firewalls or intrusion detection systems, since they would have to be active in every network the device would use. Second, with personally owned devices, the device owner may have security goals and privacy priorities that are distinct from organizations that provide connectivity or data assets, such as employers or schools. These complex relationships may complicate efforts to protect the devices. This paper explores a technique that runs on the mobile device endpoints to learn about the usage patterns associated with the device, in order to enforce network policy. We explore sensors that examine the mobile device's user interface, using physical inputs via finger taps, and that link them with the network activity on the device. We incorporate with allow‐list policies that can be provided by organizations to make on‐device access control decisions. Using IP address and DNS host name allow‐lists as a baseline, we explore the accuracy of interface‐aware allow‐lists. We find the interface‐aware allow‐lists can reach over 98.5% accuracy, even when user‐specified destinations are used, greatly exceeding the baseline accuracy. Our performance evaluation indicates our approach introduces a median of 3.87 ms of overall delay with low CPU usage.

ABSTRACT Wireless avionics intra‐communications (WAIC) technologies play an important role in the real‐time transmission between airborne equipment. As the number of deployed WAIC nodes increases, severe inter‐user and inter‐cabin interference occurs, degrading system performance and fairness. In this paper, a deep reinforcement learning‐based interference alignment and power allocation (DRL‐IAPA) scheme is proposed for multi‐user WAIC system. By integrating interference alignment with a deep deterministic policy gradient (DDPG) algorithm, the DRL‐IAPA scheme can mitigate interference, optimize power allocation, and ensure fairness among users under stringent latency and power constraints. Simulation results show that DRL‐IAPA improves spectral efficiency and fairness over traditional and heuristic‐based methods, and demonstrates scalability and consistent performance across various network configurations. Compared with other representative DRL algorithms, such as proximal policy optimization and soft actor–critic, our proposed DDPG‐based approach exhibits faster convergence, higher achievable reward, and better applicability in dynamic WAIC environments.

ABSTRACT Customer service primarily involves interaction with clients through phone calls. Precise keyword extraction from customer complaint texts facilitates the implementation of intelligent task assignment and efficient response systems. However, existing keyword extraction technologies perform sub‐optimally in the customer service domain of telecommunications operators and require substantial manual word segmentation. Given the pronounced clustering of customer service data, this research introduces a synonym matching approach and a few‐shot learning‐based method tailored for extracting keywords in this sector. This enables model training with minimal labelled data and computational resources. Using a dataset generated from the transcription of customer service calls, the proposed model demonstrates a 24.94% improvement in accuracy compared to popular existing methods.

ABSTRACT This paper presents a novel 3D geometry‐based stochastic channel model for intelligent reflecting surface (IRS)‐assisted wireless communication, where a cylindrical array‐based large antenna transmitter (LAT) is employed. Unlike conventional planar array models, the proposed configuration captures the spatial characteristics of both azimuth and elevation domains, enabling enhanced beamforming and coverage flexibility. The system model incorporates the physical positions of each antenna element and their contributions to the overall channel response, including propagation delays, Doppler shifts, and phase variations. Furthermore, hardware impairments at the LAT and IRS are integrated into the channel formulation to assess their impact on spectral efficiency (SE). A compact channel coefficient expression is derived based on the cylindrical geometry and used to evaluate the SE under ideal and non‐ideal conditions. Simulation results demonstrate that the proposed CA‐based LAT‐IRS system achieves significant performance gains over conventional planar configurations, especially in dense environments and under realistic hardware constraints.

ABSTRACT In this paper, we present a modulation scheme called generalized symbol sequence modulation (GSSM), which incorporates multiple symbol sequence modulation mappers. In GSSM, additional information is conveyed through symbol sequences from different symbol constellations to increase data rates. In order to convey information, the sequence of symbols is used in three time slots; therefore, a single antenna can convey at least four bits. Unlike spatial modulation (SM), GSSM conveys information over fading and non‐fading channels. The performance of the GSSM system has been validated through bit error rate (BER) simulations and theoretical framework based on various receiving antennas and fading channels. Furthermore, in terms of secrecy rate, it is superior to conventional single input, multiple output systems (SIMO), space shift keying (SSK), and symbol sequence modulation (SSM). Also, GSSM has shown a reduction in receiver computational complexity when compared to SM at the same bit rate. BER results are presented for non‐fading, fading, correlated, and non‐correlated scenarios, under GSSM.