Adaptive RSMA-OMA for Resilient MIMO Networks Under Imperfect CSI and SIC

This paper addresses the challenge of power control in Rate-Splitting Multiple Access (RSMA) systems for downlink Multi-Input Multi-Output (MIMO) networks under practical impairments such as spatial correlation, imperfect Channel State Information (CSI), and residual Successive Interference Cancellation (SIC) errors. We propose a novel degeneracyaware framework that adaptively adjusts the power allocation between the common and private streams, ensuring optimal performance despite CSI uncertainty and imperfect SIC. Our approach incorporates a dynamic switching mechanism between RSMA and Orthogonal Multiple Access (OMA) to maintain system feasibility and resilience in the face of these impairments. Extensive analytical and simulation results demonstrate that the proposed framework significantly enhances power efficiency, mitigates outage probability, and improves overall system robustness, making RSMA a viable and efficient solution for modern wireless networks with realistic CSI and SIC conditions.

Graph-Spectral Fusion of Wavelet Packets and Higher-Order Statistics for Anomaly Detection in Industrial IoT Networks

Industrial Internet of Things (IIoT) networks demand reliable anomaly detection under harsh wireless conditions, yet most detectors fail on four fronts: hostile fading, stealthy non-Gaussian faults, discarded spatial structure, or constrained edge hardware. We propose Graph WPT+HOS, a classical label-free detector that fuses three complementary views: the Graph Fourier Transform (GFT) for spatial inconsistency, the Wavelet Packet Transform (WPT) for transient time-frequency localization, and Higher-Order Statistics (HOS) for non-Gaussian shape. The fused features are scored by a Mahalanobis distance with Ledoit-Wolf shrinkage and converted to alarms by a one-sided CUSUM. The pipeline is asymptotically optimal at the decision stage, requires no labeled anomalies, and runs on ARM-class edge hardware without GPU acceleration. Across six baselines and four domain-shift regimes under Rayleigh fading, Graph WPT+HOS attains the highest ROC-AUC and PR-AUC and reduces CUSUM detection latency.

Agent-Based Modeling of Low-Emission Fertilizer Adoption for Dairy Farm Decarbonisation using Empirical Farm Data

To understand complex system dynamics in dairy farming, it is essential to use modeling tools that capture farm heterogeneity, social interactions, and cumulative environmental impacts. This study proposes an agent-based modeling (ABM) framework to simulate nitrogen management and the adoption of low-emission fertilizer across 295 Irish dairy farms over a 15-year period. Using empirical data, the model represents farm communication through a social network, capturing peer influence and discussion group dynamics, where adoption probabilities are driven by social contagion, farm-scale characteristics, and policy interventions such as subsidies and carbon taxes. The framework estimates sectoral greenhouse gas emissions, cumulative abatement, and private-social cost trade-offs, using Monte Carlo simulation and sensitivity analysis to quantify uncertainty. The model shows strong agreement with observed adoption trajectories ($R^2 = 0.979$, RMSE = 0.0274) and is validated against empirical data using a Kolmogorov-Smirnov test (D = 0.2407, p < 0.001), indicating its ability to reproduce structural patterns in adoption behavior. Adoption dynamics are further characterized using a logistic diffusion model consistent with Rogers' innovation diffusion theory, capturing progression from early adoption to a saturation level of approximately 91%. By framing decarbonization as a socio-technical diffusion process rather than a purely economic optimization problem, this study provides an in silico policy laboratory for evaluating the robustness and diffusion speed of climate mitigation strategies prior to implementation.

Degeneracy-Aware Agent-Based Resource Allocation for Multi-User MIMO RSMA Network

This paper proposes a pilot-aware, degeneracy-driven Agent-Based Modelling (ABM) framework for distributed resource allocation in RSMA-enabled multi-user MIMO systems under imperfect Channel State Information (CSI) and residual Successive Interference Cancellation (SIC) error. The centralized RSMA power allocation problem is reformulated as a distributed multi-agent system, where users operate as autonomous agents that iteratively adapt transmit powers based on locally observed feasibility conditions. To capture the joint impact of interference coupling, CSI estimation errors, pilot overhead, and residual SIC error, a novel degeneracy index defined as the ratio of target to achieved signal-to-interference-plus-noise ratio (SINR) is introduced as a unified feasibility metric. This enables a scalable fixed-point power control mechanism that characterizes the feasible operating region without requiring global CSI. Analytical expressions for user-level and system-level outage probabilities are derived under spatially correlated fading, providing insights into reliability under practical impairments. The fundamental interplay between degeneracy, outage probability, and effective throughput is established, revealing that system performance is governed by the feasibility of the bottleneck user. To further enhance resilience, Degeneracy-Weighted Path Robustness (DWPR) and Functional Substitution Score (FSS) are incorporated to exploit path diversity and functional redundancy. Numerical results show that the proposed framework achieves near-centralized performance in sparse networks, while providing notable throughput gains and improved scalability in dense deployments, highlighting its effectiveness for robust and distributed resource management in next-generation wireless systems.

Wavelet-Packet-based Noise Signatures With Higher-Order Statistics for Anomaly Prediction

This note develops the first-ever noise-centric anomaly prediction method for a fused discrete-time signal. A Wavelet Packet Transform (WPT) provides a time--frequency expansion in which structure and residual can be separated via orthogonal projection. Higher-Order Statistics (HOS), particularly the third-order cumulant (and its bispectral interpretation), quantify non-Gaussianity and nonlinear coupling in the extracted residual. Compact noise signatures are constructed and an analytically calibrated Mahalanobis detector yields a closed-form decision rule with non-central chi-square performance under mean-shift alternatives. Propositions and proofs establish orthonormality, energy preservation, Gaussian-null behavior of cumulants, and the resulting test statistics.

Blockage-aware Hierarchical Codebook Design for RIS-Assisted Movable Antenna Systems

In this paper, we propose a novel blockage-aware hierarchical beamforming framework for movable antenna (MA) systems operating at millimeter-wave (mm-Wave) frequencies. While existing works on MA systems have demonstrated performance gains over conventional systems, they often neglect the design of specialized codebooks to leverage MA's unique capabilities and address the challenges of increased energy consumption and latency inherent to MA systems. To address these aspects, we first integrate blockage detection into the codebook design process based on the Gerchberg-Saxton (GS) algorithm, significantly reducing inefficiencies due to beam evaluations done in blocked directions. Then, we use a two-stage approach to reduce the complexity of the joint beamforming and Reconfigurable Intelligent Surfaces (RIS) optimization problem. The simulations demonstrate that the proposed adaptive codebook successfully improves the Energy Efficiency (EE) and reduces the beam training overhead, substantially boosting the practical deployment potential of RIS-assisted MA systems in future wireless networks.

Efficient Medium Access Control for Low-Latency Industrial M2M Communications

Efficient medium access control (MAC) is critical for enabling low-latency and reliable communication in industrial Machine-to-Machine (M2M) net-works, where timely data delivery is essential for seamless operation. The presence of multi-priority data in high-risk industrial environments further adds to the challenges. The development of tens of MAC schemes over the past decade often makes it a tough choice to deploy the most efficient solu-tion. Therefore, a comprehensive cross-comparison of major MAC protocols across a range of performance parameters appears necessary to gain deeper insights into their relative strengths and limitations. This paper presents a comparison of Contention window-based MAC scheme BoP-MAC with a fragmentation based, FROG-MAC; both protocols focus on reducing the delay for higher priority traffic, while taking a diverse approach. BoP-MAC assigns a differentiated back-off value to the multi-priority traffic, whereas FROG-MAC enables early transmission of higher-priority packets by fragmenting lower-priority traffic. Simulations were performed on Contiki by varying the number of nodes for two traffic priorities. It has been shown that when work-ing with multi-priority heterogenous data in the industrial environment, FROG-MAC results better both in terms of delay and throughput.

A Study on Delay Assessment for Heterogenous Traffic in VANET

Vehicular Ad hoc Networks (VANETs) comprise of multi-priority hetero-genous nodes, both stationary and/or mobile. The data generated by these nodes may include messages relating to information, safety, entertainment, traffic management and emergency alerts. The data in the network needs dif-ferentiated service based on the priority/urgency. Media Access Control (MAC) protocols hold a significant value for managing the data priority. This paper studies a comparison of 802.11p which is a standard PHY and MAC protocol for VANET with a fragmentation-based protocol, FROG-MAC. The major design principle of 802.11-p is to allow direct Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication without associa-tion, using Enhanced Distributed Channel Access (EDCA) to prioritize safety-critical messages. However, if non-critical messages already start to transmit, the nodes with critical data have to wait. FROG-MAC reduces this delay by transmitting normal packets in fragments with short pauses between them, al-lowing urgent packets to access the channel during these intervals. Simula-tions have been performed to assess the delay and throughput for high and low priority data. We report that FROG-MAC improves both the performance parameters due to offering an early channel access to the emergency traffic.

IoT-Enabled Sleep Monitoring and Cognitive Assessment for Evaluating Teacher Well-Being

Sleep quality is an important indicator of the efficient cognitive function for high school teachers. Due to the high work stress and multi-tasking expectations, the teachers often face issues with their sleep quality and cognitive function, which has a clearly negative influence on their teaching abilities. In this work, we propose a unique but simple method of deploying Internet of Things (IoT) technology to monitor the sleep quality of high school teachers at Pakistan. Smart watches embedded with pulse rate and SpO2 sensors were used to collect data and categorize the sleep quality as "poor", "fair" or "good". Moreover, we used a psychological tool, Cognitive Assessment Questionnaire (CAQ) for the self-assessment of teachers' cognitive function. The study was conducted over 208 high school teachers from across Pakistan. It has been found that most of the teachers had a poor sleep quality and cognitive function; The link between these two variables indicate that the workload and other factors must be improved for the teachers to ensure their well-being, which will in turn have a positive impact on their teaching quality.

Classical Capacity of Arbitrarily Distributed Noisy Quantum Channels

With the rapid deployment of quantum computers and quantum satellites, there is a pressing need to design and deploy quantum and hybrid classical-quantum networks capable of exchanging classical information. In this context, we conduct the foundational study on the impact of a mixture of classical and quantum noise on an arbitrary quantum channel carrying classical information. The rationale behind considering such mixed noise is that quantum noise can arise from different entanglement and discord in quantum transmission scenarios, like different memories and repeater technologies, while classical noise can arise from the coexistence with the classical signal. Towards this end, we derive the distribution of the mixed noise from a classical system's perspective, and formulate the achievable channel capacity over an arbitrary distributed quantum channel in presence of the mixed noise. Numerical results demonstrate that capacity increases with the increase in the number of photons per usage.