Search Results (443)

Wireless sensor networks supported by mobile edge computing are increasingly required to process heterogeneous sensing data under stringent latency, reliability, and energy constraints. However, most existing task-offloading studies are still formulated for generic user equipment and primarily focus on uplink transmission, which is insufficient for practical sensing systems where sensor nodes continuously upload measurements while simultaneously receiving control commands, model updates, and feedback from the edge. To address this gap, this paper reformulates joint computation offloading and power control as a sensor-aware optimization problem in an edge-assisted wireless sensor network. We propose a three-layer architecture consisting of sensor nodes, access points with lightweight edge servers, and a cloud coordination layer. Each sensing task is characterized by data size, computation density, latency deadline, and sensing priority, while the optimization objective jointly minimizes long-term task delay, communication and computation energy, and packet-loss penalty under transmission power, edge resource, and residual-energy constraints. To solve the resulting mixed discrete–continuous problem, we develop a multi-agent reinforcement learning framework in which each sensor node acts as an autonomous agent and learns offloading and transmission policies with clipped proximal policy optimization, while the cloud layer performs coordinated edge-resource allocation through the alternating direction method of multipliers. In addition to delay and energy, network lifetime and sensing delivery performance are incorporated into the evaluation. Simulation results in a sensor-network monitoring scenario demonstrate that the proposed framework consistently reduces latency, lowers energy consumption, and prolongs network lifetime compared with representative baselines, highlighting its effectiveness and practical potential for intelligent sensing applications that require integrated sensing, communication, and edge computing. Full article

Automatic Modulation Classification (AMC) is essential for waveform-level signal characterization. It supports spectrum sensing, signal identification, and adaptive resource allocation in cognitive radio and next-generation wireless systems. However, channel impairments such as multipath propagation, frequency offset, fast fading, and noise degrade modulation signatures, making reliable AMC challenging. Existing deep learning-based approaches often rely on purely data-driven learning, leading to insufficient modeling of modulation-relevant features, loss of transient characteristics, and limited exploitation of hierarchical relationships among modulation types. To address these issues, this paper proposes PHM-Net, a physics-informed hierarchical multi-scale network for robust AMC. The model employs a hierarchical backbone with residual encoder blocks. A Transient Feature Gating (TFG) module enhances modulation-relevant representations, a Cross-Resolution Signal Aggregation (CRSA) module fuses multi-stage features, and a Physics-Informed Hierarchical Loss (PI-HL) enforces consistency between coarse- and fine-grained predictions. Experimental results on three benchmark datasets (RML2016.10a, RML2016.10b, and RML2018.01a) show that PHM-Net consistently achieves the highest average accuracy among all compared models. On RML2018.01a, which contains 1024-sample sequences and 24 classes, PHM-Net achieves an average accuracy of 64.59% and a best-case accuracy of 98.42%, surpassing AMC_Net by 11.14 and 17.09 percentage points and CNN-Transformer by 9.43 and 11.15 percentage points, respectively. PHM-Net provides a robust and interpretable solution for AMC under complex channel conditions. Full article

Indoor visible light communication (VLC) has attracted increasing attention as a promising wireless access technology because of its large unlicensed bandwidth and dual functionality of illumination and data transmission. However, practical VLC systems are vulnerable to line-of-sight (LoS) blockage caused by user mobility, human shadowing, and indoor obstacles, which may degrade link reliability and service continuity. Although hybrid VLC/RF networks can improve robustness by using RF transmission as a backup link, excessive RF fallback under severe optical blockage may overload the bandwidth-limited RF interface and reduce the service quality of RF-associated users. To address this issue, this paper investigates a risk-aware illumination-constrained resource allocation scheme for hybrid VLC/RF indoor networks under random optical blockage. A unified system model is developed by considering Lambertian optical propagation, random optical blockage, RF backup transmission, and working-plane illumination constraints. Based on this model, a joint user association and power allocation problem is formulated under QoS, transmit-power, and illumination requirements. The proposed scheme evaluates VLC service utility under blockage uncertainty, controls RF fallback to avoid excessive backup-link loading, allocates VLC/RF transmission power, and performs illumination feasibility adjustment to preserve the required lighting level. Simulation results show that, under severe blockage conditions, the proposed scheme reduces the outage probability to approximately 0.26, compared with 0.68 for VLC-only transmission and 0.47 for threshold-based VLC/RF switching. For a 20-user network, the proposed scheme achieves an average sum rate of approximately 277 Mbps, maintains a 100% illumination compliance ratio, and achieves higher energy efficiency than the benchmark schemes. Further RF backup analysis shows that the proposed scheme can maintain the service quality of RF-associated users by avoiding excessive RF fallback. These results demonstrate the effectiveness of the proposed framework for reliable and illumination-feasible hybrid VLC/RF indoor communication. Full article

Converging millimetre-wave (mmWave) radio access with passive optical network (PON) fronthaul under the Open RAN (O-RAN) architecture promises unprecedented capacity for beyond-5G and 6G systems. Yet today, dynamic bandwidth allocation (DBA) in the PON and physical resource block (PRB) scheduling in the mmWave RAN operate independently, a critical design flaw that causes severe latency accumulation, resource fragmentation, and consistent failure to meet the divergent quality-of-service requirements of network slices. This paper breaks that deadlock by introducing the first slice-aware, computationally efficient orchestration framework that jointly optimises DBA and PRB allocation in a converged mmWave-PON O-RAN. We formulate the problem as a constrained Markov decision process (CMDP) with explicit latency, reliability, and throughput constraints for URLLC, eMBB, and mMTC slices. The core technical advance is a reward-shaped proximal policy optimisation (RS-PPO) algorithm whose potential-based shaping function directly penalises DBA–PRB misalignment and dense feedback on queue build-up, accelerating learning without compromising optimality. To make this work in near-real time on the O-RAN RIC, we embed three complementary efficiency engines: graph convolutional network (GCN) state abstraction, action masking, and prioritised N-step replay. Extensive 3GPP-compliant simulations show that RS-PPO slashes URLLC end-to-end latency by 37% (from 1.38 ms to 0.87 ms), boosts PRB utilisation by 28% (from 68% to 87%), and delivers 99.999% reliability, all while converging 45% faster and cutting inference time by 45% (to just 2.3 ms). The result is a sub-5 ms control cycle, compatible with O-RAN specifications and deployable as an xApp on the near-RT RIC. Our framework closes a long-standing coordination gap left unresolved by prior art, enabling true slice-aware convergence between the optical and wireless domains. Full article

Little existing work addresses the joint design of time slot allocation and power splitting ratio optimization in simultaneous wireless information and power transfer (SWIPT)-enabled wireless sensor networks (WSNs). To fill this gap, this paper proposes a novel energy-sustainable framework termed ETAPS that co-optimizes time slot allocation and power splitting ratio for SWIPT-enabled WSNs. A dedicated frame structure is designed that partitions each cluster member (CM) into four operational modes for slot scheduling, toward conflict-free and coordinated resource allocation among CMs. A dynamic power splitting strategy is further developed to adaptively refine slot allocation for CMs and derive the optimal power splitting ratio for the cluster head (CH). Comprehensive numerical simulations are performed to validate the proposed scheme. The results demonstrate that ETAPS maintains effective energy sustainability even under limited energy input from the energy access point (EAP). When the EAP provides a sufficient energy supply, the optimal power splitting ratio converges to 0.9. Moreover, under sufficient transmit power at CMs, ETAPS adaptively allocates transmission time from CMs to the CH by setting the optimal power splitting ratio to 0.6. Full article

This paper addresses the problem of throughput utility maximization in a non-orthogonal multiple access (NOMA)-enabled wireless power transfer mobile edge computing (WPT-MEC) network with dynamic task arrivals and user cooperation. To promote fairness and effectively handle random task arrivals and time-varying channels, we model the system utility as a nonlinear function of time-averaged throughput. We then formulate a stochastic optimization problem aimed at maximizing utility while strictly maintaining sensor queue stability. By leveraging the Lyapunov optimization framework, the long-term network-wide utility maximization is decomposed into efficient, slot-wise convex subproblems that operate online without requiring prior knowledge of future task arrivals or channel states. We develop a Dynamic User Cooperation Throughput Maximization (DUCTM) algorithm that enables adaptive resource allocation and cooperative computation offloading in an online manner. Theoretical analysis establishes a provable $\left[O\right(1/V),O(V\left)\right]$ trade-off between utility optimality and queue backlog. Extensive simulations demonstrate that our approach consistently outperforms baseline methods, providing robust and stable performance even under bursty traffic and highly dynamic environmental conditions. Full article

Reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) has become an effective architecture for supporting computation-intensive and latency-sensitive applications by enabling flexible deployment and enhanced wireless coverage. However, when non-orthogonal multiple access (NOMA) is incorporated, the joint optimization of computation offloading, wireless resource allocation, RIS phase configuration, and UAV trajectory design becomes highly challenging owing to the strong coupling among decision variables, problem non-convexity, and time-varying system dynamics. To address these challenges, this paper investigates the energy consumption minimization problem in a NOMA-based RIS-assisted UAV-MEC system by jointly optimizing user offloading ratios, transmit power, UAV computing resource allocation, and flight trajectory. A long short-term memory (LSTM)-embedded proximal policy optimization (PPO) algorithm is developed to capture the temporal dependencies of system states and enable adaptive decision-making in dynamic environments. In addition, a closed-form phase conjugation-based optimal RIS configuration is derived and incorporated into the environment model to reduce the action space and improve training efficiency. The simulation results show that the proposed LSTM-PPO method converges faster and achieves lower energy consumption than conventional PPO, deep deterministic policy gradient (DDPG), and fixed offloading schemes, while exhibiting improved stability and scalability in the tested multi-user scenarios. These results demonstrate the effectiveness of combining temporal learning and model-assisted RIS optimization for energy efficient resource management in RIS-assisted UAV-MEC systems. Full article

The increasing complexity of next-generation wireless systems challenges the scalability and generalization capabilities of traditional model-driven physical layer (PHY) design, which relies on analytically derived channel models and optimization frameworks. This paper presents a comprehensive survey and critical review of deep learning (DL) architectures enabling the transition toward AI-native PHY design. A unified optimization perspective is developed in which all PHY tasks—including channel estimation, channel state information (CSI) feedback, massive MIMO processing, signal detection, channel coding, beamforming, resource allocation, and semantic-aware transmission—are formulated under a common empirical risk minimization (ERM) framework. Neural architectures such as autoencoders, convolutional and recurrent networks, transformers, and reinforcement learning models are examined through their underlying optimization formulations, loss functions, training methodologies, and representation learning mechanisms. The review compares model-driven and AI-native approaches in terms of performance metrics, computational complexity, robustness, generalization capability, and practical deployment constraints, including hardware limitations, energy efficiency, and real-time feasibility. The analysis highlights the conditions under which AI-native architectures provide adaptability and performance improvements while identifying trade-offs in complexity, latency, and interpretability. The study concludes by outlining prioritized research directions toward fully adaptive and self-optimizing wireless communication systems. Full article

Backscatter communication (BackCom) has emerged as an energy-efficient and low-cost communication paradigm, in which wireless devices transmit information by reflecting incident signals rather than actively generating radio frequency signals. Owing to the extremely low power consumption and hardware cost, BackCom is particularly suitable for Internet of Things (IoT) devices with stringent low energy and cost constraints. However, due to the severe double channel attenuation inherent in backscatter links, conventional ground-based deployment of transmitters and receivers often suffers from poor communication quality and low energy efficiency. Unmanned aerial vehicles (UAVs), with their high mobility and favorable line-of-sight (LoS) links, can act as dynamic aerial transmitters and receivers in BackCom, thereby mitigating channel attenuation and improving both communication reliability and energy efficiency. To enhance the data collection efficiency of UAV-assisted BackCom systems under a limited mission duration, this paper proposes a joint optimization method for communication resource allocation and UAV trajectory design under task time constraints. Specifically, a mixed-integer non-convex optimization problem is formulated to maximize the number of devices served by the UAV within a given task duration. The original problem is then decomposed into two subproblems, namely communication resource allocation optimization and UAV trajectory optimization. An iterative algorithm based on Block Coordinate Descent (BCD) and Successive convex approximation (SCA) is developed to obtain an efficient solution. Simulation results demonstrate that the proposed method can effectively increase the number of served devices within the specified mission time limit. Full article

Despite significant advancements in communication systems, inherent limitations persist in providing reliable data transmission for emerging applications with massive data exchanges. Semantic communication offers promising solutions by extracting and transmitting meaningful information rather than raw bit sequences. However, it faces challenges from high mobility and dynamic channel conditions in wireless environments. In this paper, we design a ground-to-air network architecture that integrates a rotary-wing unmanned aerial vehicle (UAV) and ground terminals to maximize semantic transmission efficiency while maintaining low energy consumption. This approach leverages the high mobility of the UAV for flexible deployment and the data reduction capabilities of semantic communication. Therefore, we formulate a multi-objective optimization problem to simultaneously balance the total semantic transmission rate and the UAV propulsion energy consumption by jointly optimizing the UAV hovering position, semantic encoding lengths, and resource block (RB) allocation. The problem is complex, with mixed continuous and discrete variables, which necessitates an advanced optimization method. To address these challenges, we propose a novel greedy-enhanced adaptive multi-objective cellular genetic algorithm (GEAMOCell), which utilizes an adaptive neighborhood selection mechanism to balance exploration and exploitation, and employs a crowding-guided archive feedback mechanism to maintain population diversity. The simulation results demonstrate that the proposed GEAMOCell algorithm outperforms baseline algorithms in terms of convergence, semantic transmission rate, and energy efficiency. Full article

Non-convex stochastic optimization presents fundamental mathematical challenges across machine learning, wireless networks, data center resource allocation, and optical wireless communication systems, where complex loss landscapes with multiple local minima and saddle points impede classical variational inference methods. This paper introduces the Quantum-Inspired Variational Inference (QIVI) framework, which systematically integrates quantum mechanical principles (superposition, entanglement, and measurement operators) into classical variational inference through rigorous mathematical formulations grounded in Hilbert space theory and operator algebras. We develop a unified optimization framework that encodes classical parameters as quantum-inspired states within finite-dimensional complex Hilbert spaces, employing unitary evolution operators and adaptive basis selection governed by gradient covariance eigendecomposition. The core mathematical contribution establishes that QIVI achieves a convergence rate of $\mathit{O}({log}^{2}T/T^{1/2})$ for $\sigma$-strongly non-convex functions, provably improving upon the classical $\mathit{O}\left(T^{-1/4}\right)$ rate, yielding a theoretical speedup factor of $1.85$–$1.96\times$. Comprehensive experiments across synthetic benchmarks, Bayesian neural networks, and real-world applications in network optimization and financial portfolio management demonstrate 23–$47\%$ faster convergence, 15–$35\%$ superior objective values, and 28–$46\%$ improved uncertainty calibration. The principal contributions include: (i) a rigorous Hilbert space-based mathematical framework for quantum-inspired variational inference grounded in operator algebras, (ii) a novel hybrid quantum–classical algorithm (QIVI) with adaptive basis selection via gradient covariance eigendecomposition, (iii) formal convergence proofs establishing provable improvement over classical methods, (iv) comprehensive empirical validation across diverse problem domains relevant to machine learning and network optimization, and (v) demonstration of the framework’s applicability to optimization problems arising in wireless networks, data center resource allocation, and network system design. Statistical validation using the Friedman test (${\chi }^2=847.3$, $p<0.001$) and post hoc Wilcoxon signed-rank tests with Holm–Bonferroni correction confirm that QIVI’s improvements over all baseline methods are statistically significant at the $\alpha =0.05$ level across all benchmark categories. The framework discovers $18.1$ out of 20 true modes in multimodal distributions versus $9.1$ for classical methods, demonstrating the potential of quantum-inspired optimization approaches for challenging stochastic problems arising in machine learning, wireless communication, and network optimization. Full article

To meet the growing demand for autonomous decision-making and real-time optimization in industrial manufacturing, integrating Artificial Intelligence-Generated Content (AIGC) services with Industry 5.0 can enable real-time industrial intelligence. The effectiveness of a generative model is closely related to the current state of the production environment. However, existing studies often ignore the dynamic temporal relationship between generative models and production environments, especially in industrial scenarios with large model transmission delays and random AIGC task arrivals. Therefore, we define a novel metric, namely the Age of Model (AoM), to measure the freshness of generative models with respect to current industrial tasks. We then formulate an average-AoM-minimization problem that jointly considers LoRA-based fine-tuning, wireless transmission and resource allocation. To solve this problem, we propose a Hybrid-Action Multi-Agent Proximal Policy Optimization (HA-MAPPO) algorithm. The proposed algorithm follows the centralized training and decentralized execution (CTDE) paradigm and introduces a Main-Agent Priority State Strategy to support coordinated training and independent execution. In addition, a multi-head output structure is designed to handle the hybrid-action space, which includes discrete fine-tuning association decisions and continuous transmission resource allocation. Simulation results show that the proposed scheme outperforms all benchmark methods. Specifically, the cumulative rewards are improved by approximately $11.13\%$, $20.32\%$, $36.61\%$, and $38.78\%$ compared with the four benchmark algorithms, respectively. These results demonstrate that the proposed scheme can significantly reduce the average AoM while providing high-quality and timely industrial AIGC services. Full article

Unmanned Aerial Vehicles (UAVs) are gradually emerging as key platforms for Integrated Sensing, Communication, and Computation (ISCC) systems in next-generation wireless networks. However, strict resource constraints and task coupling make static allocation inefficient in dynamic environments. This paper studies a UAV-driven ISCC system in which a single UAV dynamically allocates communication bandwidth, sensing resources, and computing power. Considering that sensing data in mission-critical applications is highly time-sensitive, minimizing the response time is paramount. To reduce system latency while maintaining sensing quality and energy efficiency, we propose D3PG-Light, a deployment oriented and stability-enhanced refinement of the deep reinforcement learning framework, specifically tailored for real-time resource scheduling under UAV hardware constraints. D3PG-Light incorporates an adaptive gradient stabilization mechanism, Long Short-Term Memory (LSTM), and feature fusion to enhance training stability. Simulation results based on real air–ground channel measurements show that D3PG-Light converges faster and achieves more stable learning behavior than DDPG, TD3, and the original D3PG. In particular, the proposed method reduces the 95th-percentile latency from over 100 ms to approximately 24 ms, achieves higher converged reward values, and requires fewer than 50 k model parameters. These results demonstrate the effectiveness of D3PG-Light for latency-sensitive UAV-ISCC applications. Full article

In disaster scenarios where communication infrastructure is damaged, Unmanned Aerial Vehicle (UAV)-assisted wireless networks can provide temporary connectivity and hence the indispensable mobile edge computing functionality. However, limited resources on UAVs require prioritization of critical data in such scenarios. This research addresses reliable transmission and task offloading by modeling user tasks as layered compositions, where the base layer is essential and enhancement layers are optional. TDMA-based prioritization is employed to ensure reliable decoding of high-priority layers of the computational tasks (i.e., intra-user priority) along with inter-user priority needed for urgent users like rescue teams. Under these reliability constraints, this work formulates a joint communication–computation optimization problem to allocate transmission power and UAV CPU cycles efficiently in order to minimize total weighted offloading latency. The original problem is non-convex; thus, we leverage epigraph and perspective functions to recast the problem into a convex one. We also derive analytically, using the KKT conditions, the optimal water-filling-like solutions for the reformulated problem. The numerical results show that, at a signal-to-noise ratio of 5 dB, the proposed algorithm achieves relative latency reductions vs. the baseline algorithms (39.99% reduction vs. Equal Allocation, 49.99% reduction vs. Enhancement First, and 69.99% reduction vs. No Priority), which reflect considerable latency reduction with priority-aware offloading. Full article

The susceptibility of a Line-of-Sight (LOS) link in Optical Wireless Communication (OWC) to blockage is a major challenge affecting its deployment for next generation networks. Another challenge is the random orientation of the receiving device which also affects the amount of received optical power when the incidence angle is high. Reflecting Intelligent Surfaces (RIS) is a promising technology for using non-LOS (NLOS) communication. This work aims to study the effect of these LOS link impairments on Wavelength Division Multiplexing (WDM)-based resource allocation in OWC with and without the use of RIS elements and the effect on resilience. In this work, we adopt the state-of-the-art Orientation-based Random Way-Point (ORWP) model for mobility and random orientation of the User Equipment (UE) and calculate blockage geometrically assuming human objects since OWC links are not independent in contrast to RF-based communication. We propose multi-connectivity with physical path disjointness using multiple Angle Diversity Receiver (ADR) designs to evaluate the system performance using both a Mixed Integer Linear Program (MILP) and a low-complexity algorithm. Full article