Search Results (247)

Integrated sensing and communications (ISAC) requires tight coordination between spatial signal design and multiple-access strategies to balance communication throughput and sensing accuracy under shared spectral and hardware constraints. However, existing ISAC frameworks with rate-splitting multiple access (RSMA) typically rely on fixed antenna arrays and decoupled optimization, which fundamentally limit their ability to adapt to fast channel variations and dynamic sensing requirements. This paper introduces a fluid antenna-enabled RSMA-assisted ISAC architecture, in which movable antenna ports are exploited as a new spatial degree of freedom to enhance adaptability in both communication and sensing operations. Fluid antenna systems (FAS) are deployed at both the base station and user terminals, allowing dynamic port selection that reshapes the effective channel and sensing beampattern in real time. We formulate a joint sum-rate maximization problem subject to explicit sensing-quality constraints, capturing the coupled impact of antenna port selection, RSMA rate allocation, and multi-beam transmit design. The proposed framework maximizes the communication sum-rate while ensuring that the sensing functionality satisfies a predefined sensing quality constraint. This constraint-based ISAC formulation guarantees that sufficient sensing power is directed toward the target while optimizing communication performance. The resulting optimization involves strongly coupled discrete and continuous decision variables, rendering conventional optimization methods ineffective. To address this challenge, a hierarchical deep reinforcement learning (HDRL) framework is developed, where an upper-layer deep Q-network (DQN) determines discrete antenna port selection and a lower-layer twin delayed deep deterministic policy gradient (TD3) algorithm optimizes continuous beamforming and rate-splitting parameters. Numerical results demonstrate that the proposed approach significantly improves system performance, achieving higher communication sum-rate while satisfying sensing requirements under dynamic propagation conditions. Full article

The Dengying Formation gas reservoir in the Penglai gas field, located in the central Sichuan Basin, exhibits substantial resource potential and promising development prospects. This reservoir is characterized by well-developed fractures and dissolution cavities, strong heterogeneity, complex gas–water relationships, and widespread edge–bottom water. During production, edge–bottom water is prone to channeling and intrusion through high-permeability pathways, which severely constrains well productivity and overall gas recovery. To address these challenges, this study takes a fractured-vuggy carbonate edge–bottom water gas reservoir as an example. By integrating large-scale physical simulation with cross-scale numerical simulation, a rational production allocation method suitable for strongly heterogeneous gas reservoirs has been developed. The research results indicate that: (1) Large-scale physical simulation experiments demonstrate that for fractured-vuggy bottom water gas reservoirs, implementing rate reduction and pressure control after water breakthrough can effectively suppress water invasion and coning, extend the stable production period, and increase the recovery factor by approximately 16%; (2) Based on the dynamic characteristics of water invasion, key similarity criteria including the Bond number, capillary number, gravity–viscous force ratio, and geometric–temporal similarity ratio were selected to establish a scientific parameter design method for cross-scale numerical simulation; (3) By considering factors such as reservoir type and aquifer energy, single-well mechanistic models were used to determine appropriate production rates for individual wells, enabling rapid optimization of production allocation plans. This provides crucial guidance for efficient gas well development and surface facility planning. Full article

Agricultural economic resilience plays a pivotal role in the integrated development of agriculture and rural areas, and carries great significance for ensuring national food security and advancing sustainable agricultural development in the context of complex risks and challenges. Using panel data covering 41 cities in the Yangtze River Delta region from 2011 to 2023, this paper empirically investigates the impact mechanism of fiscal support for agriculture on agricultural economic resilience. The results demonstrate that fiscal support for agriculture in the Yangtze River Delta exerts a significant positive effect on agricultural economic resilience, especially with a pronounced promoting influence on resistance capacity. Mechanism analysis indicates that fiscal support for agriculture indirectly affects agricultural economic resilience through channels including agricultural industrial agglomeration and the urban–rural income gap. Accordingly, to strengthen agricultural economic resilience, it is necessary to optimize the allocation and expenditure structure of fiscal funds, adopt differentiated strategies with dynamic and timely adjustments, allocate funds to boost agricultural industrial agglomeration, enhance investment in human capital to narrow the urban–rural income gap, and facilitate sustainable agricultural development. Full article

Non-orthogonal multiple access (NOMA) has become one of the main enabling technologies for next-generation cellular networks. The ability to allocate multiple users on the same frequency resources simultaneously leads to improved spectral efficiency. This paper examines power allocation and user pairing for NOMA networks with an objective to enhance the sum spectral efficiency (sum capacity, bps/Hz) while guaranteeing the target rate of the far user. Two benchmark methods were used to evaluate the performance of the proposed scheme: (1) fixed power allocation, in which fixed power coefficients are allocated to the near and far users, and (2) random power allocation, where random coefficients are assigned to the users. However, these static methods fail to adapt to instantaneous channel conditions and may lead to reduced performance for the weak user and inefficient power utilization. To manage these limitations, a novel interference-limited power allocation (IL-PA) scheme is proposed. In the IL-PA, the power allocation coefficients are dynamically allocated to users according to an interference threshold. The proposed scheme guarantees that the interference induced by the near user does not exceed a predefined interference threshold; thus, the target rate of the far user is achieved. The proposed interference threshold is derived theoretically to enhance the overall system capacity and optimize the signal-to-interference-plus-noise ratio (SINR). Additionally, a user pairing scheme, which separates users into two groups according to their channel gains, is proposed to reduce complexity while preserving good performance. The simulation results show that the proposed power allocation and user pairing scheme outperforms the benchmark methods in terms of overall capacity. Full article

With the rapid evolution toward 6G networks, ensuring robust physical layer security (PLS) in highly dynamic and heterogeneous wireless environments has become a key challenge. Traditional security methods often struggle to adapt to time-varying channels, especially in the absence of perfect channel state information. Furthermore, the dynamic nature of node selection and power allocation in heterogeneous networks creates a complex hybrid action space operating across multiple timescales, significantly complicating the design of efficient and adaptive security strategies. To address this, this paper proposes a novel constrained hierarchical reinforcement learning (CHRL) framework for secure cooperative communications in next-generation wireless systems. The framework is designed to optimize secrecy performance within a hybrid action space comprising both discrete node selection and continuous power allocation, operating at different timescales. By hierarchically decoupling the joint optimization problem, the upper layer performs risk-aware node selection to maximize long-term secrecy capacity (SC) while guaranteeing a stable and secure link. At the lower layer, we develop a constrained MiniMax Multi-objective Deep Deterministic Policy Gradient (M3DDPG) algorithm that optimizes power allocation considering worst-case conditions. Lagrange multipliers are integrated to enforce a strictly positive SC constraint throughout transmission, effectively preventing security outages. Simulation results under time-varying Rayleigh fading channels demonstrate that the proposed CHRL framework outperforms existing HRL methods, achieving up to 17% improvement in SC while strictly maintaining security constraints. These results validate the effectiveness of the proposed approach for enhancing PLS in next-generation cooperative wireless networks. Full article

Payment channel networks enable scalable off-chain payments, but their practical deployment remains constrained by a persistent tension among routing efficiency, liquidity visibility, transaction privacy, and settlement security. Existing multipath routing mechanisms can improve payment success under fragmented liquidity, yet they often expose sensitive balance information, leak structural features of payment routes, and enlarge the attack surface for probing, channel exhaustion, and selective forwarding. This paper presents a novel framework, Adaptive Multipath Proofs (AMPs), for privacy protection and security in payment channel networks. The core idea is to bind multipath routing decisions with lightweight zero-knowledge verifiability, allowing intermediate nodes to validate path feasibility, fragment consistency, and settlement constraints without learning exact channel balances, the complete payment amount, or the global route structure. AMP integrates three mechanisms: a hidden-liquidity feasibility proof that supports privacy-preserving route selection, an adaptive payment-splitting strategy that dynamically determines fragment allocation according to network congestion and balance uncertainty, and a proof-coupled settlement guard that enforces atomicity and timeout consistency across all payment fragments. Together, these mechanisms reduce information leakage while preserving robust payment execution under dynamic network conditions. Experimental evaluation on real Lightning Network topologies and synthetic stress scenarios demonstrates that AMP significantly lowers balance disclosure and endpoint inference risk, improves payment completion under skewed liquidity distributions, and introduces only moderate computational and communication overhead. The results indicate that adaptive proof-carrying multipath routing offers a practical and effective direction for building secure, privacy-preserving, and high-success payment channel networks. Full article

As mobile networks evolve toward Beyond 5G and 6G architectures, energy efficiency and sustainability have become increasingly critical due to growing traffic volumes, denser base station deployments, and the rising number of connected devices. Supporting Ultra-Reliable Low-Latency Communication (URLLC) services is particularly challenging, as their stringent requirements for both high reliability and minimal latency can lead to a significant increase in energy consumption within the radio access network. This paper examines slot structure mechanisms for concurrently servicing URLLC and enhanced Mobile Broadband (eMBB) traffic within the 5G Advanced framework, with a focus on improving energy efficiency and optimizing radio resource utilization. We propose an adaptive algorithm for managing radio interface time resources, which dynamically allocates sub-slots based on current network load and radio channel conditions. The system model is implemented in Simulink and incorporates URLLC and eMBB traffic generation, signal-to-noise ratio estimation, and a priority-based scheduling mechanism. Simulation results demonstrate that the proposed approach meets URLLC latency and reliability requirements while reducing redundant transmissions and enhancing the energy efficiency of the radio access network. These findings position the proposed method as a promising solution for the design of energy-efficient, next-generation mobile networks. Full article

Reducing agricultural emissions is vital for climate mitigation, yet evidence on green finance’s potential to facilitate agricultural decarbonization—particularly in China—remains scarce. Leveraging China’s Green Finance Reform and Innovation Pilot Zones as a quasi-natural experiment, this study employs a staggered difference-in-differences design and complementary Callaway-Sant’Anna estimates. Using a balanced panel of 282 prefecture-level and above cities spanning 2012–2022—a window covering five pre-policy years before the initial 2017 pilot rollout and sufficient post-policy years to capture dynamic effects for the 2017, 2019, and 2022 cohorts—this study assesses the policy impact on agricultural carbon emission intensity. The findings reveal that the pilot policy reduces emission intensity by approximately 9.2% on average. This result is robust across event-study analyses, placebo tests, PSM-DID, policy interference checks, and alternative outcome specifications. Channel-consistent evidence suggests that the effect operates through three mechanisms: greener credit allocation, stronger green technological innovation, and lower-carbon adjustment of the agricultural production structure. The effect is larger in eastern China, major grain-producing regions, and cities with higher levels of financial development, and exhibits a strengthening trend over time. By analyzing China’s city-based pilot approach, this study demonstrates how financial policy can support agricultural decarbonization in settings characterized by dispersed emitters, imperfect environmental monitoring, and strong food-security constraints. The findings extend beyond China to inform other developing economies seeking non-price-based pathways to greener agriculture. Full article

Transfer pricing has become a major channel through which multinational enterprises shift profits across countries. This study examines the macroeconomic and institutional determinants of transfer pricing in seven European economies (France, Spain, Germany, the United Kingdom, Italy, the Netherlands, and Portugal) over the period 1985–2025. The main objective is to identify the key factors influencing profit shifting and to analyze the mechanisms through which multinational firms allocate profits across jurisdictions. The study employs panel data techniques and uses two different proxies to capture transfer pricing practices (trade-based and intangible-based channels). To analyze both long-run and short-run relationships between transfer pricing, exchange rate dynamics, foreign direct investment, inflation and institutional quality, the analysis relies on heterogeneous panel estimators and cointegration tests, supported by several robustness checks. The empirical results reveal the existence of a long-run relationship between transfer pricing and its macroeconomic and institutional determinants. Exchange rate fluctuations and inflation exert a negative effect on transfer pricing, whereas Foreign Direct Investment has a positive impact by expanding multinational investment networks and intra-group transactions. The effect of institutional quality, proxied by control of corruption, appears more heterogeneous and may vary across jurisdictions as well as across the type of transfer pricing channel, whether related to tangible trade or intangible assets. These results emphasize the importance of institutional quality and international tax coordination in limiting aggressive profit-shifting practices. Full article

Unmanned aerial vehicles (UAVs) acting as mobile aerial edge platforms can deliver on-demand communication and computing for the Internet of Vehicles (IoV) via flexible deployment and line-of-sight (LoS) links, improving reliability and reducing latency. However, high vehicle mobility, time-varying channels, and limited onboard energy make task offloading and resource coordination challenging. This paper studies joint task offloading and resource allocation in a UAV-assisted IoV system, where the UAV selects its hovering position from discrete candidate sites each time slot and splits vehicular tasks between the UAV and a roadside unit (RSU) to relieve backhaul congestion and enhance edge resource utilization. Considering vehicle mobility, multi-stage queue dynamics, and UAV energy consumption for communication, computation, and movement, the online optimization of position selection, task splitting, and bandwidth allocation is formulated as a constrained Markov decision process (CMDP). The goal is to maximize the number of tasks completed within the latency deadlines while satisfying the UAV energy budget. To solve this CMDP, we propose a graph-attention-based constrained twin delayed deep deterministic policy gradient (GAT-CTD3) algorithm. A graph attention network captures spatial correlations and resource competition among active vehicles, while a Lagrangian TD3 framework enforces long-term energy constraints and improves learning stability via twin critics, delayed policy updates, and target smoothing. The simulation results demonstrate that it outperforms the comparative scheme in terms of task completion rate, delay, and energy consumption per completed task, and exhibits strong robustness in situations with dense traffic. Full article

To address the degradation in communication performance caused by the high mobility and dynamic uncertainty in vehicular network channels, this paper proposes a hybrid resource allocation framework that integrates the advantage actor–critic (A2C) algorithm with the multi-agent deep deterministic policy gradient (MADDPG) algorithm. By modeling the high-speed vehicular network environment, the resource allocation task is formulated as a multi-agent deep reinforcement learning (MADRL) problem within a continuous action space. The proposed framework leverages the advantage function to refine gradient estimation, thereby improving training stability and convergence behavior. Additionally, regularization penalty terms and constraint mechanisms are incorporated into the learning process to balance multiple communication objectives. Specifically, the method aims to maximize the throughput of vehicle-to-infrastructure (V2I) links while ensuring the transmission reliability of vehicle-to-vehicle (V2V) links. In simulation experiments, the proposed method performs better in terms of convergence. Compared with the conventional MADDPG algorithm, the average access success probability is improved by 1.6%, and the average V2I throughput increases by 3.5%, indicating a significant enhancement in overall vehicular communication efficiency and transmission performance. 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

Urban commercial restructuring, driven by the closure of traditional supermarkets and the expansion of new-format superstores, creates a large-scale spatial reallocation challenge requiring scientific location-allocation methods. Traditional heuristic algorithms such as Genetic Algorithm (GA) struggle with discrete spatial optimization under 400+ candidate sites and complex geographic mask constraints: they converge slowly and easily fall into local optima. This study proposes a Deep Reinforcement Learning (DRL) framework named GeoPPO (Geospatial Proximal Policy Optimization) to address this gap. Using Xi’an’s retail restructuring as a case setting—427 candidate locations and multidimensional geographic features—the approach models spatial constraints via a gridded environment encoded as a five-channel state tensor. Key innovations include a dynamic action-constraint mechanism that masks invalid actions based on boundary rules and competition avoidance, and a curriculum learning strategy that enables stable convergence. The framework fills the need for methods that handle hard spatial constraints in large-scale location-allocation. Tests demonstrate rapid convergence within 1,000 epochs, achieving 75% average demand coverage—2.7% and 5.5% higher than GA and Particle Swarm Optimization (PSO), respectively. Ablation experiments confirm that Vanilla PPO without dynamic action masking fails to produce feasible solutions. The framework offers a feasible technical path for handling highly dynamic urban facility spatial configuration with geographic mask constraints. Full article

This study examines the transmission of monetary policy to bank credit granted to the non-financial private sector in Morocco, a bank-dominated emerging economy where non-financial corporations play a central role in investment, employment, and economic growth. Using monthly data over the period 2006–2023, the analysis relies on an ARDL–ECM framework that distinguishes short-run credit dynamics from long-run adjustment processes while accounting for potential structural breaks. The results indicate that changes in the policy rate do not exert a statistically significant effect on bank credit in the short run, suggesting a high degree of credit inertia. The bounds test supports the existence of a stable long-run equilibrium relationship in credit, although no significant long-run elasticities with respect to monetary policy or credit risk variables are identified. Instead, credit dynamics appear to be driven primarily by short-run adjustment mechanisms, largely shaped by credit risk and balance-sheet allocation. Overall, these findings suggest that monetary transmission in Morocco operates gradually and indirectly, mainly through prudential and balance-sheet channels rather than the conventional interest-rate channel. This implies that the effectiveness of monetary policy depends critically on prevailing risk conditions and their interaction with prudential frameworks in bank-based emerging financial systems. Full article

In this paper, we propose a robust multi-input single-output (MISO) underwater visible light communication (UVLC) system. By integrating NLTCP and NW-Viterbi decoding, the system effectively alleviates nonlinear distortions and stochastic power fluctuations. NLTCP is employed to achieve probabilistic shaping by generating a non-uniformly distributed constellation, which effectively suppresses the occurrence of high-amplitude symbols to mitigate device nonlinearity. To further optimize power allocation, a MISO architecture is utilized to distribute the signal load and reduce the power burden on individual devices. Moreover, the NW-Viterbi decoder incorporates a noise-aware weighting mechanism to optimize the decision metric, thereby enhancing decoding reliability in response to signal-dependent power fluctuations and noise variations in the underwater channel. Experimental results confirm that at an aggregate data rate of 5.8 Gbps, the proposed scheme achieves a significant Q-factor gain of 0.92 dB compared to the traditional PAM4 scheme, alongside a 90.76% enlargement in the effective operating dynamic range. This approach offers a computationally efficient yet effective solution to nonlinearity and power jitter, demonstrating significant potential for practical underwater deployments. Full article