Search Results (68)

In the LoRa (long-range) wide area network (LoRaWAN), Class A devices employ a pure ALOHA random access mechanism. Under large-scale access and bursty traffic conditions, severe packet collisions are likely, which reduces throughput and increases the packet loss rate. To address these issues, herein, we propose a collision mitigation scheme integrating the extended Kalman filter (EKF) with nonorthogonal multiple access (NOMA). First, a nonlinear state-space model is constructed to capture the dynamic evolution of backlog nodes and the uncertainty of traffic arrivals. The backlog node number is modeled as the hidden state, while newly arrived and successfully decoded packets are incorporated into the state-transition equation. At the gateway, decoded packet counts and channel occupancy are treated as observations based on which a nonlinear mapping between system state and observable features is established. The EKF is then applied to recursively predict and correct, enabling real-time estimation of the backlog state. Accordingly, an adaptive backoff strategy is designed to adjust transmission probability based on the estimated optimal load. Furthermore, to mitigate packet loss caused by collisions, a power-domain NOMA scheme with successive interference cancelation (SIC) is introduced. Signals transmitted with different spreading factors (SFs) are decoupled into approximately independent processing branches by exploiting inter-SF quasi-orthogonality. To account for imperfect inter-SF orthogonality, cross-SF residual coupling coefficients are introduced to characterize leakage interference. For transmissions sharing the same SF, overlapping packets are successively decoded and recovered through a NOMA-SIC mechanism jointly constrained by the SINR-based decoding threshold, the power-domain separation requirement, the maximum number of resolvable SIC layers, and residual SIC interference. Accordingly, the proposed receiver architecture enhances the decoding and recovery capability for collided LoRa packets. Simulation results demonstrate that, under medium-to-high traffic loads, the proposed scheme significantly improves throughput and access success rate while effectively reducing collision probability and packet loss, thereby enhancing the overall robustness and efficiency of the LoRa network. Full article

This paper investigates the tracking control problem for a class of networked nonlinear systems in a non-ideal communication environment, where both internal communication constraints (delays and packet dropouts) and external random deception attacks are taken into account. From a symmetry perspective, the backward and forward channels constitute a paired sensing–actuation structure, and channel-dependent imperfections may destroy their functional coordination. To compensate for the resulting sensing–actuation mismatch, a data-driven sliding-mode predictive tracking control scheme is developed without relying on an explicit system model. First, an equivalent dynamic linearization is adopted to represent the input–output behavior using a data-dependent incremental model. Then, using delayed measurements together with historical input–output data, an online estimator is constructed to update the pseudo partial derivative (PPD). Based on the estimated PPD, a multi-step predictor is further designed to generate the predicted outputs, and a data-driven sliding-mode predictive tracking controller is proposed by imposing a discrete reaching law on the predicted outputs. Rigorous analysis is provided to ensure the stability of the closed-loop system and to guarantee that the tracking error remains bounded, together with an explicit bound that reveals the influence of the delay horizon, estimation mismatch, and attack amplitudes. Finally, numerical simulations under square-wave and sinusoidal references validate the effectiveness and robustness of the proposed approach. Full article

Endogenous growth theory explains the asymmetric dynamic relationship between economic and social development through human capital accumulation and innovation, institutional quality, and demand channels. The objective of this paper is to assess the dynamic relationship between social and economic development in developing countries, where institutional imperfections and development instability create the most pronounced asymmetries. A composite social development index, obtained using the entropy method, operationalizes social development as the expansion of human capabilities in three dimensions: health, education, and material security. A panel vector error correction model (PVECM), estimated using the generalized method of moments (GMM) on panel data from 18 countries in Central Asia, the Middle East, and North Africa for the period 2001–2023, revealed asymmetric dynamic relationships: improved social indicators are associated with a short-term slowdown in economic indicators and more favorable economic dynamics in the medium term. In contrast, economic growth is accompanied by a positive lagged response in social development, although the short-term response may reflect the costs of social adjustment. The influence of control variables confirms the positive role of agglomeration for economic development, revealing the social costs of rapid urbanization and demographic pressure on social development. Estimates of the error correction coefficients indicate a slow adaptation of the system to long-term equilibrium, high inertia, and institutional rigidity of macrosocial processes. Impulse response functions confirm the dynamic and delayed nature of the interaction between economic and social development and positive shocks in the medium term. The obtained empirical results substantiate the need for institutional regulation of policy decisions on human capital accumulation and innovation, as well as social reforms. 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

Sea-surface reflections and wind–wave motion render maritime channels strongly time-varying and statistically non-stationary, while nearshore deployments face sparse infrastructure and co-channel multiuser interference. This study integrates reconfigurable intelligent surfaces (RISs) with rate-splitting multiple access (RSMA) for joint online resource allocation. A physics-inspired time-varying channel model is established by embedding fractional Brownian motion-driven slow statistical drift and reflection-phase perturbations. With imperfect, delayed channel state information (CSI) and discrete RIS phase quantization, a proportional-fairness utility maximization problem is formulated to jointly optimize shore base-station precoding, RIS phase shifts, and RSMA common-rate allocation. To cope with strong non-convexity, high dimensionality, mixed continuous–discrete coupling, and partial observability, a fractal-aware recurrent robust Actor–Critic (FRRAC) algorithm is developed. FRRAC encodes short observation histories using a gated recurrent unit and incorporates a lightweight Hurst-proxy estimator to capture slow channel statistics for robust value evaluation and policy learning. Truncated quantile critics and mixed prioritized–uniform replay further improve value robustness, training stability, and sample efficiency. Simulation results show that FRRAC converges faster and more stably under both conventional and fractal non-stationary channel modeling, and outperforms representative baselines across the objective and multiple statistical metrics, validating its effectiveness for joint resource optimization in maritime RIS–RSMA systems. Full article

This paper examines the role of agricultural credit in shaping farm performance and technology-related outcomes in Peru, using nationally representative microdata from the Encuesta Nacional Agropecuaria (ENA). In a context characterized by credit rationing and institutional constraints, access to finance may influence agricultural income, productivity, and the adoption of improved practices through multiple direct and indirect channels. To address the non-random allocation of credit, the analysis employs a quasi-experimental framework that combines propensity score trimming, block-based common support restrictions, entropy balancing, and doubly robust treatment-effect estimators (IPWRA and AIPW). Descriptive evidence documents substantial heterogeneity in credit sources, loan uses, and rejection reasons, highlighting structural barriers related to collateral, land tenure, and risk. Regression results on the balanced sample indicate positive and statistically significant associations between credit access and both real agricultural income and land productivity. However, estimated treatment effects are sensitive to the estimation strategy: while IPWRA estimates suggest economically meaningful gains among credit recipients, AIPW estimates are smaller and not always statistically distinguishable from zero. Exploratory results further suggest that credit access is positively associated with technology adoption and managerial capacity, consistent with, but not identifying, a potential association between credit approval and technological practices. Overall, the findings are consistent with a growing body of evidence showing that the impacts of agricultural credit are modest, heterogeneous, and context dependent. From a sustainability perspective, the results underscore the importance of complementary interventions—such as land tenure security, risk management instruments, and tailored financial services—in enhancing the effectiveness of rural credit programs in agricultural systems characterized by imperfect markets and high production risk. Full article

Low-Power Wide-Area Network (LPWAN) technologies play a central role in large-scale wireless sensor network (WSN) deployments, where energy efficiency, coverage and reliability dominate over throughput. Among them, Long Range (LoRa) technology has emerged as a widely adopted physical-layer solution due to its ability to operate at extremely low signal-to-noise ratios (SNRs). While multi-antenna techniques can potentially enhance link performance, their applicability in LoRa-type systems is constrained by low-SNR operation, strict energy budgets and the quality of channel state information (CSI). This paper presents a systematic and progressively structured evaluation of multiple-input multiple-output (MIMO) techniques in LoRa-type systems under representative operating conditions. A multi-stage simulation framework, implemented using the Vienna SLS v2.0 (Q3) simulator and adapted to LoRa-like waveforms, is employed to isolate the impact of large-scale propagation, small-scale fading, antenna configuration and CSI quality. The analysis starts from a system-level coverage baseline and advances to link-level evaluations of diversity-oriented MIMO schemes and spatial multiplexing configurations under both ideal and imperfect CSI. The results demonstrate that spatial diversity techniques are well aligned with the operational characteristics of LoRa links, offering robust performance in low-SNR regimes and under limited CSI accuracy. In contrast, spatial multiplexing exhibits higher sensitivity to channel estimation errors, with its practical benefits becoming apparent primarily when evaluated using throughput-oriented metrics such as packet error rate and normalized goodput. Overall, the study highlights the fundamental trade-off between reliability and capacity in LoRa MIMO systems and provides design-oriented insights for wireless sensor network deployments. Full article

Integrated sensing and communication (ISAC) is expected to be a key enabler for future wireless networks, improving spectral and hardware efficiency by jointly performing radar sensing and wireless communication within a unified framework. This paper proposes BeamNet, an unsupervised deep learning framework for transmit beamforming in dual-function radar-communication systems operating over general fading with imperfect channel state information (CSI). BeamNet maps noisy estimates of the communication and sensing channels to a transmit beamforming vector and is trained end-to-end by maximizing a weighted sum of the communication rate (CR) and sensing rate (SR), thereby learning the CR–SR Pareto frontier without beamforming labels or embedded optimization solvers. Using Rayleigh fading with perfect CSI, we first show that BeamNet reproduces the analytical Pareto-optimal beamforming solutions. We then use BeamNet to characterize, for Nakagami-m and Rician fading, the CR–SR trade-off across a range of fading parameters, and to assess robustness under distribution mismatch between training and test channels. Finally, under imperfect CSI, we demonstrate that BeamNet yields CR–SR trade-offs that are consistently sandwiched between the perfect-CSI and mismatched analytical baselines, outperforming the closed-form beamformer applied to imperfect CSI and recovering part of the performance loss caused by channel estimation errors. These results indicate that unsupervised learning offers a flexible and robust approach to ISAC beamforming in fading environments with imperfect channel knowledge. Full article

We propose a low-complexity hybrid beamforming method for massive Multiple-Input Multiple-Output (MIMO) systems that is robust to Channel State Information (CSI) estimation errors. These errors stem from hardware impairments, pilot contamination, limited training, and fast fading, causing spectral-efficiency loss. However, existing hybrid beamforming solutions typically either assume near-perfect CSI or rely on greedy/black-box designs without an explicit mechanism to regularize the error-distorted singular modes, leaving a gap in unified, low-complexity, and theoretically grounded robustness. We unfold the Alternating Direction Method of Multipliers (ADMM) into a trainable Deep Learning (DL) network, termed DL-ADMM, to jointly optimize Radio-Frequency (RF) and baseband precoders and combiners. In DL-ADMM, the ADMM update mappings are learned (layer-wise parameters and projections) to amortize the joint RF/baseband optimization, whereas Regularized Singular Value Decomposition (RSVD) acts as an analytical regularizer that reshapes the observed channel’s singular values to suppress noise amplification under imperfect CSI. RSVD is integrated to stabilize singular modes and curb noise amplification, yielding a unified and scalable design. For ${\sigma }_e^2=0.1$, the proposed DL-ADMM-Reg achieves approximately 8–11 bits/s/Hz higher spectral efficiency than Orthogonal Matching Pursuit (OMP) at Signal-to-Noise Ratio (SNR) $=20$–40 dB, while remaining within <1 bit/s/Hz of the digital-optimal benchmark across both $(N_t,N_r)=(32,32)$ and $(64,64)$ settings. Simulations confirm higher spectral efficiency and robustness than OMP and Adaptive Phase Shifters (APSs). Full article

This paper establishes a comprehensive statistical framework for analyzing quantum teleportation protocols under realistic noisy conditions. We develop novel mathematical tools to characterize the complete statistical distribution of teleportation fidelity, including both mean and variance, for systems experiencing decoherence and channel imperfections. Our analysis demonstrates that the teleportation fidelity follows a characteristic distribution $F\sim \mathcal{P}(F_{\mathrm{avg}},\mathrm{\Delta }{F}^2)$ where the variance $\mathrm{\Delta }{F}^2$ depends crucially on entanglement quality and channel noise. We derive the optimal resource allocation condition $\partial E_{\mathrm{ent}}/\partial F/\partial C_{\mathrm{classical}}/\partial F=\beta /\alpha$ that minimizes total resource consumption while achieving target fidelity. Furthermore, we introduce a Bayesian adaptive protocol that enhances robustness against noise through real-time statistical estimation. The theoretical framework is validated through numerical simulations and provides practical guidance for experimental implementations in quantum communication networks. Full article

This paper proposes an optimized digital predistortion (DPD) framework. Firstly, a peak-detection-based loop-delay estimation is developed by leveraging the unique peak distribution of Orthogonal Frequency Division Multiplexing (OFDM) signals. It reduces the required number of samples to as small as two without compromising estimation accuracy. Then, a Biased Memory Polynomial (BMP) model is proposed for power amplifier modeling. It addresses low-power inaccuracies caused by circuit imperfections (e.g., DC offsets) by adding a bias term to conventional memory polynomials, improving linearization accuracy in low-power regime. Last, to improve the accuracy of coefficient derivation, Memory-Clustering Biased Memory Polynomial (MBMP) is proposed by grouping signals into clusters based on memory-attenuated input vectors and processing them with dedicated sub-models. It improves linearization accuracy in high-power regime. Experimental results demonstrate that the MBMP model reduces normalized mean square error (NMSE) by 16.12 dB, and reduces adjacent channel power ratio (ACPR) by about 12 dBm compared to conventional MP. Full article

We study UAV-assisted 5G uplink connectivity for disaster response, in which a UAV (unmanned aerial vehicle) acts as an aerial base station to restore service to ground users. We formulate a joint control problem coupling UAV kinematics (bounded acceleration and velocity), per-subchannel uplink power allocation, and uplink non-orthogonal multiple access (UL-NOMA) scheduling with adaptive successive interference cancellation (SIC) under a minimum user-rate constraint. The wireless channel follows 3GPP urban macro (UMa) with probabilistic line of sight/non-line of sight (LoS/NLoS), realistic receiver noise levels and noise figure, and user equipment (UE) transmit-power limits. We propose a bounded-action proximal policy optimization with generalized advantage estimation (PPO-GAE) agent that parameterizes acceleration and power with squashed distributions and enforces feasibility by design. Across four user distributions (clustered, uniform, ring, and edge-heavy) and multiple rate thresholds, our method increases the fraction of users meeting the target rate by 8.2–10.1 percentage points compared to strong baselines (OFDMA with heuristic placement, PSO-based placement/power, and PPO without NOMA) while reducing median UE transmit power by $64.6\%$. The results are averaged over at least five random seeds, with $95\%$ confidence intervals. Ablations isolate the gains from NOMA, adaptive SIC order, and bounded-action parameterization. We discuss robustness to imperfect SIC and CSI errors and release code/configurations to support reproducibility. Full article

Weighted sum-rate (WSR) maximization in downlink massive multi-user multiple-input (MU-MIMO) with per-antenna power constraints (PAPCs) and imperfect channel state information (CSI) is computationally challenging. Classical weighted minimum mean-square error (WMMSE) algorithms, in particular, have per-iteration costs that scale cubically with the number of base-station antennas. This article proposes a robust low-complexity WMMSE-based precoding framework (RLC-WMMSE) tailored for massive MU-MIMO downlink under PAPCs and stochastic CSI mismatch. The algorithm retains the standard WMMSE structure but incorporates three key enhancements: a diagonal dual-regularization scheme that enforces PAPCs via a lightweight projected dual ascent with row-wise safety projection; a Woodbury-based transmit update that replaces the dominant $M\times M$ inversion with an $\left(NK\right)\times \left(NK\right)$ symmetric positive-definite solve, greatly reducing the per-iteration complexity; and a hybrid switching mechanism with adaptive damping that blends classical and low-complexity updates to improve robustness and convergence under channel estimation errors. We also analyze computational complexity and signaling overhead for both TDD and FDD deployments. Simulation results over i.i.d. and spatially correlated channels show that the proposed RLC-WMMSE scheme achieves WSR performance close to benchmark WMMSE-PAPCs designs while providing substantial runtime savings and strictly satisfying the per-antenna power limits. These properties make RLC-WMMSE a practical and scalable precoding solution for large-scale MU-MIMO systems in future wireless sensor and communication networks. Full article

Accurate co-registration between on-scalp Optically Pumped Magnetometer (OPM)–Magnetoencephalography (MEG) sensors and anatomical Magnetic Resonance Imaging (MRI) remains a critical bottleneck restricting the spatial fidelity of source localization. Optical Scanning Image (OSI) methods can provide high spatial accuracy but depend on surface visibility and cannot directly determine the internal sensitive point of each OPM sensor. Coil-based magnetic dipole localization, in contrast, targets the sensor’s internal sensitive volume and is robust to occlusion, yet its accuracy is affected by coil fabrication imperfections and the validity of the dipole approximation. To integrate the complementary advantages of both approaches, we propose a hybrid co-registration framework that combines Rigid Coil Structures (RCS), magnetic dipole-based sensor localization, and optical orientation constraints. A complete multi-stage co-registration pipeline is established through a unified mathematical formulation, including MRI–OSI alignment, OSI–RCS transformation, and final RCS–sensor localization. Systematic simulations are conducted to evaluate the accuracy of the magnetic dipole approximation for both cylindrical helical coils and planar single-turn coils. The results quantify how wire diameter, coil radius, and turn number influence dipole model fidelity and offer practical guidelines for coil design. Experiments using 18 coils and 11 single-axis OPMs demonstrate positional accuracy of a few millimeters, and optical orientation priors suppress dipole-only orientation ambiguity in unstable channels. To improve the stability of sensor orientation estimation, optical scanning of surface markers is incorporated as a soft constraint, yielding substantial improvements for channels that exhibit unstable results under dipole-only optimization. Overall, the proposed hybrid framework demonstrates the feasibility of combining magnetic and optical information for robust OPM–MEG co-registration. Full article

This paper presents a novel method for locally estimating vehicle density on highways based on vehicle-to-vehicle (V2V) communication, a communication mode within intelligent transport systems (ITSs), enabled via IEEE 802.11p and 3GPP C-V2X technologies. Awareness messages (AMs), such as basic safety messages (BSMs, SAE J2735) and cooperative awareness messages (CAMs, ETSI EN 302 637-2), are periodically broadcast by vehicles and can be leveraged to sense the presence of nearby vehicles. Unlike existing approaches that directly combine the number of sensed vehicles with measured packet reception ratio (PRR) of the AM, our method accounts for the deviations in PRR caused by imperfect channel conditions. To address this, we estimate the actual packet reception probability (PRP)–distance curve by exploiting its inherent downward trend along with multiple measured PRR points. From this curve, two metrics are introduced: node awareness probability (NAP) and average awareness ratio (AAR), the latter representing the ratio of sensed vehicles to the total number of vehicles. The real density is then estimated using the number of sensed vehicles and AAR, mitigating the underestimation issues common in V2V-based methods. Simulation results across densities ranging from 0.02 vehs/m to 0.28 vehs/m demonstrate that our method improves estimation accuracy by up to 37% at an actual density of 0.28 vehs/m, compared with methods relying solely on received AMs, without introducing additional communication overhead. Additionally, we demonstrate a practical application where the basic safety message (BSM) transmission rate is dynamically adjusted based on the estimated density, thereby improving traffic management efficiency. Full article