MOTIVATION: Large-scale prospective cohort studies collect longitudinal biospecimens alongside time-to-event outcomes to investigate biomarker dynamics in relation to disease risk. The nested case-control (NCC) design provides a cost-effective alternative to full cohort biomarker studies while preserving statistical efficiency. Despite advances in joint modeling for longitudinal and time-to-event outcomes, few approaches address the unique challenges posed by NCC sampling, non-normally distributed biomarkers, and competing survival outcomes. RESULTS: Motivated by the TEDDY study, we propose "JM-NCC", a joint modeling framework designed for NCC studies with competing events. It integrates a generalized linear mixed-effects model for potentially non-normally distributed biomarkers with a cause-specific hazard model for competing risks. Two estimation methods are developed. fJM-NCC leverages NCC sub-cohort longitudinal biomarker data and full cohort survival and clinical metadata, while wJM-NCC uses only NCC sub-cohort data. Both simulation studies and an application to TEDDY microbiome dataset demonstrate the robustness and efficiency of the proposed methods. AVAILABILITY AND IMPLEMENTATION: Software is available at https://github.com/Zhaoyn-oss/JMNCC and archived on Zenodo at https://zenodo.org/records/18199759 (DOI: 10.5281/zenodo.18199759).

This paper presents STS-T, a novel spatial-temporal-spectral transformer for end-to-end prediction of jamming spectrum situations. To address challenges such as node mobility, legitimate interference, and noisy or missing data, STS-T employs spatiotemporal positional encoding and axial attention mechanisms that effectively capture global features while reducing computational complexity. Specifically, the model integrates a Temporal Predictive Decoder with dilated convolution for improved temporal forecasting, and a Spatial Interpolation Decoder with cross-attention to seamlessly map between input and output spaces. A synthetic dataset generated via OMNeT++ simulations is also introduced to compensate for the lack of real-world UAV jamming data. Experimental results confirm that STS-T outperforms existing baselines, providing a robust solution for predicting jamming spectrum situations in complex UAV swarm environments.

This paper analyzes the impact of beam alignment errors (BAE) on directional ad hoc networks and proposes a new angular error model that derives angular errors from positional errors, overcoming limitations of existing models that assume fixed angular error variance. We derive the probability density function (PDF) of antenna gain for both flat-top and Gaussian antenna models and analyze the signal-to-interference-plus-noise ratio (SINR) coverage probability in Poisson networks. Numerical results show that the new model more accurately captures the effects of BAE on SINR coverage and main lobe alignment probability. The proposed model provides a more realistic understanding of beam misalignment effects in practical network scenarios.

Due to network delays and scalability limitations, clustered ad hoc networks widely adopt Reinforcement Learning (RL) for on-demand resource allocation. Albeit its demonstrated agility, traditional Model-Free RL (MFRL) solutions struggle to tackle the huge action space, which generally explodes exponentially along with the number of resource allocation units, enduring low sampling efficiency and high computational complexity. To mitigate these limitations, Model-Based RL (MBRL) offers a solution by generating simulated samples through an environment model, which boosts sample efficiency and stabilizes the training by avoiding extensive real-world interactions. However, establishing an accurate dynamic model for complex and noisy environments necessitates a careful balance between model accuracy and computational complexity & stability. To address these issues, we propose a conditional Diffusion Model (DM) as high-dimensional offline resource allocation planner in multifrequency time division multiple access (MF-TDMA) wireless ad hoc networks. By leveraging the astonishing generative capability of DMs, our approach takes advantage of generated high-quality samples to guide exploration and learn optimal policy. Extensive experiments show that our model outperforms MFRL in average reward and Quality of Service (QoS) while demonstrating comparable performance to other MBRL algorithms.

MOTIVATION: Mendelian randomization (MR) serves as a valuable tool for investigating causal relationships between exposures and disease outcomes in observational studies. However, MR methods, operating under classical assumptions, may yield biased estimates and inflated false-positive causal relationships when faced with realistic and complex correlated horizontal pleiotropy (CHP). While numerous MR methods have emerged to address CHP effects, limited methods can effectively handle relatively large direct effects, commonly known as idiosyncratic pleiotropy. RESULTS: To address this gap, we propose an efficient and robust MR method to account for idiosyncratic and correlated pleiotropy, named RMR-ICP. Furthermore, Our method incorporates linkage disequilibrium structure using paralleled Gibbs sampling to enhance statistical power. The robustness and efficiency of our method are demonstrated through extensive simulation studies and applications. RMR-ICP is first used to analyze the effects of plasma proteins on stroke, followed by its application to conventional stroke risk factors. Our analysis reveals that Selectin E (SELE) exhibits a positive causal effect on the occurrence of any stroke. Only those specifically designed to account for idiosyncratic and CHP identified a significant positive causal effect of myeloperoxidase on ischemic stroke, with RMR-ICP providing stronger statistical evidence. Elevated Natriuretic Peptide B (BNP) levels specifically increase the risk of cardioembolic stroke (CES), though not with other stroke subtypes. This finding is consistent with previous studies suggesting that plasma BNP levels may help distinguish CES from other stroke types. Higher Waist-hip ratio (WHR) levels raise the risk across all stroke types. These findings provide new insights into identifying stroke-related risk factors. AVAILABILITY AND IMPLEMENTATION: RMR-ICP is publicly available at https://github.com/QingCheng0218/RMR.ICP.

Semantic communication (SemCom) has been deemed as a promising communication paradigm to break through the bottleneck of traditional communications. Nonetheless, most of the existing works focus more on point-to-point communication scenarios and its extension to multi-user scenarios is not that straightforward due to its cost-inefficiencies to directly scale the joint source-channel coding (JSCC) framework to the multi-user communication system. Meanwhile, previous methods optimize the system by differentiable bit-level supervision, easily leading to a “semantic gap”. Therefore, we delve into multi-user broadcast communication (BC) based on the universal transformer (UT) and propose a reinforcement learning (RL) based self-critical alternate learning (SCAL) algorithm, named SemanticBC-SCAL, to capably adapt to the different BC channels from one transmitter (TX) to multiple receivers (RXs) for sentence generation task. In particular, to enable stable optimization via a non-differentiable semantic metric, we regard sentence similarity as a reward and formulate this learning process as an RL problem. Considering the huge decision space, we adopt a lightweight but efficient self-critical supervision to guide the learning process. Meanwhile, an alternate learning mechanism is developed to provide cost-effective learning, in which the encoder and decoders are updated asynchronously as independent agents. Notably, the incorporation of RL makes SemanticBC-SCAL compliant with any user-defined semantic similarity metric and simultaneously addresses the channel non-differentiability issue by alternate learning. Besides, the convergence of SemanticBC-SCAL is also theoretically established. Extensive simulation results have been conducted to verify the effectiveness and superiorness of our approach, especially in low signal-to-noise ratio regions.

In modern clustered ad hoc networks, efficient and dynamic resource allocation is crucial for ensuring Quality of Service (QoS) under dynamic and uncertain environments. However, the challenges posed by limited sample efficiency, high interaction cost, and high-dimensional action space limit the effectiveness of the widely adopted Model-Free Reinforcement Learning (MFRL) solutions. In contrast, Model-Based RL (MBRL) offers an alternative approach to boost sample efficiency and stabilize the training by explicitly leveraging a learned environment model. Nevertheless, designing accurate and stable dynamics models in noisy, real-world communication scenarios remains a key bottleneck. To address these issues, we propose a Conditional Diffusion Model Planner (CDMP) for high-dimensional offline resource allocation in clustered ad hoc networks. By leveraging the powerful generative capability of Diffusion Models (DMs), our approach enables the accurate modeling of complex environmental dynamics and utilizes an inverse dynamics model for effective policy planning. Beyond simply adopting DMs in offline RL, we further incorporate the CDMP algorithm with a theoretically guaranteed, uncertainty-aware penalty metric, which theoretically and empirically manifests itself in mitigating the Out-of-Distribution (OOD)-induced distributional shift, a common issue for offline settings with scarce training data. Extensive experiments also show that our model outperforms MFRL in average reward and QoS, while demonstrating superior performance over other MBRL algorithms. These results highlight the practicality and scalability of our model in real-world network resource allocation tasks.

Faced with the abundance of mobile ad-hoc network (MANET) applications, there emerges a strong incentive to provision MANET in millimeter wave. However, the deafness of directional antennas and the decentralized structure of MANET make it difficult to achieve consistent medium access control (MAC) among nodes through random competition. Therefore, graph coloring-based MAC (GC-MAC) scheme is proposed to implement time division multiplexed scheduling, but it allocates equal slots to links, disregarding the unbalanced and piecewise stationary traffic distribution in task-driven MANETs. Here, we propose a continual multi-agent reinforcement learning (RL)-assisted traffic-adaptive scheme to enhance the agility of slot allocation. Specifically, we add a contention period to frames of GC-MAC, during which nodes analyze stochastic characteristics of traffic to derive link traffic distribution for each task, and adjust slot assignment to reach cooperation through decentralized multi-agent deep Q-network (MA-DQN). Besides, considering the traffic variation due to task switch, continual RL is incorporated to accommodate changes of the environment more sensitively. Finally, simulation results prove the proposed scheme achieves faster convergence speed, lower delay and higher throughput.

In recent years, unmanned aerial vehicle (UAV) ad-hoc networks have achieved rapid development due to their autonomy and high reliability. Typically, clustering is widely adopted to reduce the degradation of network performance in large-scale UAV networks. However, due to the limited channel resources and complex communication environment, it becomes challenging to obtain complete information needed for clustering from remote nodes. In addition, the high dimension and non-linear relationships of data also make large-scale clustering diffcult. Therefore, in this paper, we propose a noval distributed clustering scheme. First, a matrix factorization (MF) based time series algorithm is proposed to predict and complement the incomplete information. Second, we adopt deep autoencoder to wisely incorporate the non-linear relationship of information needed for clustering. Finally we extract features as input to k-means to obtain clustering results. Simulation results demonstrate the effectiveness of the proposed clustering scheme.

Mobile ad-hoc network (MANET) has attracted ex-tensive attention in many applications with nodes operating over bandwidth-constrained wireless links in a self-organized manner. Typically, to avoid potential collisions, CSMA/CA method is widely adopted in MANETs and affects the provisioning quality of service (QoS). However, due to the potentially severe collisions in a large-scale MANET, current CSMA/CA methods fail to support QoS effectively, especially for scenarios with different priority services. Here, we propose a priority-aware CSMA/CA strategy to use the higher-priority packets to collect the queueing information from adjacent nodes in a piggyback manner and model the channel access process by a partial observation Markov decision process (POMDP). Furthermore mean-field multi-agent reinforcement learning (MARL) is adopted to allow each individual node in the MANET to dynamically select the appropriate contention window (CW) and gradually compute the global optimal policy. Finally, extensive simulation results verify significantly lower delay and packet loss rate for the higher- priority services and comparable provisioning quality for lower- priority services, which reflects the superiority over baselines.

The beam alignment (BA) problem consists in accurately aligning the transmitter and receiver beams to establish a reliable communication link in wireless communication systems. Existing BA methods search the entire beam space to identify the optimal transmit-receive beam pair. This incurs a significant latency when the number of antennas is large. In this work, we develop a bandit-based fast BA algorithm to reduce BA latency for millimeter-wave (mmWave) communications. Our algorithm is named Two Phase Heteroscedastic Track-and-Stop (2PHT&S). We first formulate the BA problem as a pure exploration problem in multi-armed bandits in which the objective is to minimize the required number of time steps given a certain fixed confidence setting. By taking advantage of the correlation structure among beams that the information from nearby beams are similar and the heteroscedastic property that the variance of the reward of an arm (beam) is linearly related to its mean, the proposed algorithm groups all beams into several sets of beams such that the optimal beam set is first selected, and the optimal beam is identified from this set after that. Theoretical analysis and simulation results demonstrate the superiority of the proposed algorithm vis-à-vis other baseline competitors.

In statistical priority-based multiple access protocol (SPMA), to ensure the performance of high-priority packets, the absolute prioritization mechanism is adopted and the threshold of each priority queue is usually set to a fixed value. However this limits the transmission of low-priority packets when there are few high-priority packets. Moreover, the existing backoff algorithms do not consider the factor of channel occupancy. In order to solve the above problems, in this paper, we propose a Deep Q-Network (DQN) based transmission and backoff (DQN-TB) approach that consists of two sub networks, where the first one aims to transmit more low-priority packets but not affect the performance of high-priority packets, while the second one determines the better backoff duration by utilizing the action of former sub-network and channel occupancy. Numerical experiments demonstrate that the proposed DQN-TB approach has better throughput and backoff delay performance.

In mobile ad-hoc networks (MANETs), network performance depends strongly on routing, therefore intensive researches about routing protocol have been developed. However, most traditional routing protocols suffer from significant performance degradation as they attempt to find a shortest path without considering the impact of congestion and channel quality. In this work, we propose an intelligent routing algorithm based on prioritized replay double deep Q-network (PRD-DQN). We design two kinds of packet, fast routing (FR) packet and experience transfer (ET) packet, to explore network, and a reward function is defined in which congestion and channel quality are both considered for adaptive routing decision. The simulation results demonstrate that the proposed algorithm can effectively learn a routing strategy with low congestion and high signal to noise ratio (SNR) and outperform the Q-Learning algorithm and the optimized link state routing (OLSR) protocol in terms of convergence speed, end-to-end latency and network throughput.

Media-based modulation (MBM) is a novel method that embedding part or all of the information in the variations of the transmission media. The traditional detection algorithms for MBM system have to estimate the channel state informations (CSIs) to recover the transmitted symbols and they are affected by the number of receiving antennas (RAs). In this paper we propose a deep neural networks (DNN) detector with a novel loss function (LF) which does not require estimation of CSIs and is independent of the number of RA. Numerical results validate that the proposed DNN detector has better bit-error-ratio (BER) performance than that of the traditional detection algorithms and the performance advantage of the proposed LF is also confirmed.

Reliable data delivery is a major challenge for underwater wireless sensor networks (UWSNs) due to the harsh acoustic communication environment. In order to address this challenge, we propose a new erasure coding based multipath routing (ECMR) scheme to reduce the packet loss ratio in UWSNs. Specifically, a multipath routing algorithm with the modified link weight is designed to adapt the delay tolerant link state routing (DTLSR) algorithm to the underwater acoustic channel environment. As a reliable transmission process, each packet is split into several blocks, and erasure coding is employed to generate redundant blocks. Based on the Bayes' theorem, we propose a new mechanism to distribute different numbers of coded blocks to multiple paths with specific reliability, in order to optimize the transmission success rate. Simulation results show that the ECMR scheme can effectively reduce the packet loss ratio during data delivery in UWSNs.

Unmanned aerial vehicle (UAV) swarms (or UAV networks) are vulnerable to jamming attacks due to the shared wireless transmission medium. In order to address this difficulty, frequency hopping based anti-jamming schemes are commonly used in the literature, however, their performance is usually limited due to the unique mobility feature of UAV swarms. In this work, a practical local reaction anti-jamming (LRAJ) scheme is proposed to reduce the packet transmission delay when the jammed nodes are dynamically changing. In the proposed scheme, the jammed nodes and their one-hop neighbors determine their node types at each frequency (channel) by exchanging information about the states of their corresponding frequencies, and performing adaptive frequency hopping (AFH) accordingly. In the mean time, the unjammed nodes can still maintain their normal operations. Therefore, with the aid of the proposed LRAJ scheme, the considered UAV swarm is able to resist malicious jamming attacks in the local area. Simulation results validate the effectiveness of the proposed scheme.

The statistical priority-based multiple access protocol (SPMA) is a MAC (medium access control) protocol adopted for the Tactical Targeting Network Technology (TTNT), by virtue of high reliability and low latency. In SPMA, in order to keep the network in a favorable loading situation and ensure 99 percent first time success rate for packets of the highest priority, priority thresholds are normally set to fixed values. However, this fixed threshold strategy is not necessarily optimal when the transmission rate at each node varies due to the change of channel condition. To improve the performance, in this paper, we propose an adaptive priority-setting strategy for SPMA, accounting for the change of transmission rates at network nodes. The performance enhancement from the proposed strategy is validated through numerical simulations. In particular, it is shown that higher throughput and lower latency are achieved when the number of nodes supporting high transmission rate increases, while 99 percent first time success rate for the packets of the highest priority is guaranteed when it decreases.

MOTIVATION: Recent microbiome association studies have revealed important associations between microbiome and disease/health status. Such findings encourage scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, and have led to applying statistical models to quantify causal microbiome effects and to identify the specific microbial agents. However, there are no existing causal mediation methods specifically designed to handle high dimensional and compositional microbiome data. RESULTS: We propose a rigorous Sparse Microbial Causal Mediation Model (SparseMCMM) specifically designed for the high dimensional and compositional microbiome data in a typical three-factor (treatment, microbiome and outcome) causal study design. In particular, linear log-contrast regression model and Dirichlet regression model are proposed to estimate the causal direct effect of treatment and the causal mediation effects of microbiome at both the community and individual taxon levels. Regularization techniques are used to perform the variable selection in the proposed model framework to identify signature causal microbes. Two hypothesis tests on the overall mediation effect are proposed and their statistical significance is estimated by permutation procedures. Extensive simulated scenarios show that SparseMCMM has excellent performance in estimation and hypothesis testing. Finally, we showcase the utility of the proposed SparseMCMM method in a study which the murine microbiome has been manipulated by providing a clear and sensible causal path among antibiotic treatment, microbiome composition and mouse weight. AVAILABILITY AND IMPLEMENTATION: https://sites.google.com/site/huilinli09/software and https://github.com/chanw0/SparseMCMM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, we present a secure transmission scheme to improve the secrecy capacity of wiretap systems by blending the benefits of polar code and artificial noise (AN). In the considered system, a transmitter tries to communicate with a receiver without leaking any confidential information to an eavesdropper. We propose a new system utility function, referred to as message secrecy capacity (MSC), which is obtained by integrating the code rate of polar code into the conventional secrecy capacity. Then, in order to maximize the secrecy capacity of the information bits in polar code, we formulate a max-min optimization problem to optimize the powers allocated among useful signals and the AN. To address the highly non-convexity of the considered problem, we propose a concave-convex procedure (CCCP)-based algorithm by introducing some carefully designed auxiliary variables, and convergence to the set of KarushKuhn-Tucker (KKT) solutions is guaranteed. Numerical results demonstrate the effectiveness of the proposed MSC objective function and power allocation scheme.