This paper proposes a simulation-based dynamic message packing framework that enables runtime MPS selection based on queue occupancy and inter-participant distance while preserving the standard TDMA structure and waveform and provides a practical, standards-compliant runtime optimization for Link 16 systems.
Abstract
Link 16 is a frequency-hopping tactical data link that employs four statically assigned Message Packing Structures (MPSs), each imposing a fixed trade-off among throughput, slot efficiency, and anti-jamming protection. Because these assignments are fixed prior to deployment, current Link 16 networks cannot adapt to runtime variations in traffic load, communication range, or interference conditions, often resulting in inefficient slot utilization. This paper proposes a simulation-based dynamic message packing framework that enables runtime MPS selection based on queue occupancy and inter-participant distance while preserving the standard TDMA structure and waveform. To address contested electromagnetic environments, the baseline mechanism is extended with a lightweight interference-aware component that biases MPS selection toward protection-capable structures based on estimated jammer severity, without altering the underlying adaptation triggers. The proposed framework is implemented in a C++ Link 16 simulation environment and evaluated under five operational scenarios, including non-congested and congested traffic, static and dynamic communication ranges, and probabilistic jammer interference. Results show that under non-congested conditions, the mechanism remains throughput-neutral, with the number of transmitted MPSs closely matching the number of delivered messages, while significantly reducing wasted slot capacity and improving the adaptive use of protection features. Under congestion, dynamic MPS selection enables efficient multi-message packing within TDMA slots, resulting in substantial throughput gains and improved slot utilization. Under jammer interference, throughput decreases due to increased packet loss; however, the extended mechanism preserves a higher absolute delivery rate than static packing by systematically increasing the use of redundancy-enabled and combined protection MPSs, reflecting a shift from capacity-oriented to resilience-oriented adaptation under degraded channel conditions. Overall, the proposed framework provides a practical, standards-compliant runtime optimization for Link 16 systems, enabling improved efficiency under light load, enhanced throughput under congestion, and adaptive resilience under interference.
The next generation of wireless systems extends ultra-reliable low-latency communications (URLLC) to the realm of massive connections, termed mURLLC. To address the inherent conflict between stringent quality of service (QoS) requirements in URLLC and the problem of severe and highly fluctuating interference behind demands of massive connectivity, effective fast fading (FF) mitigation and resource allocation strategies are crucial. Through in-depth analysis of FF characteristics, this paper derives optimized configurations for two FF mitigation approaches: protection margin reservation and $K$ -repetition. Furthermore, we integrate these FF mitigation strategies into a hierarchical-clustering (HC)-based resource allocation algorithm for configured-grant in mURLLC. This results in a highly practical and efficient algorithm for managing radio resources and interference in mURLLC scenarios. Simulation results demonstrate that our proposed algorithm achieves over 65% reduction in resource consumption without compromising reliability, significantly enhancing network capacity to support demanding mURLLC applications.
Yi-Chen Guo, Lili Xu, Yihang Cheng et al.· IEEE Transactions on Wireles...· 0 citations
The rapid evolution of heterogeneous telecommunication networks has increased the difficulty of maintaining communication performance while operating within spectrum and transmission constraints. Existing network optimization approaches primarily focus on throughput, routing, and interference mitigation, whereas regulatory compliance is commonly treated as a separate post-processing activity. This separation limits the ability of network-control mechanisms to respond proactively to compliance risks. This study develops a Compliance-Aware AI Framework (CA-AIF) that integrates Coalition Formation Games (CFG), variance-aware DUCBQ adaptive routing, and Proximal Policy Optimization (PPO) with a policy-constrained compliance monitoring layer. The framework represents compliance risk through telemetry-derived violation indicators associated with spectrum occupancy, transmission power, and interference conditions and incorporates these indicators into network decision-making. A controlled simulation environment is developed using synthetically generated mobility, interference, channel, and spectrum-occupancy data. The proposed framework is evaluated against Q-learning, Dyna-Q, UCBQ, and DUCBQ using throughput, packet acceptance, routing stability, convergence, compliance violation rate, and computational cost. Across repeated simulations, the proposed framework achieves higher throughput and packet acceptance while reducing compliance violations relative to the evaluated baselines. The results demonstrate that incorporating compliance risk into network optimization can improve the stability of communication decisions without treating compliance as an independent post-processing task. The study contributes a reproducible simulation-based framework for compliance-aware intelligent networking and provides a basis for future validation using operational spectrum-monitoring and regulatory datasets.
Godfrey Wandwi, Dennis Numi Madaha· Methods in Science and Techn...· 0 citations
Spectrum admission in low-latency mobile networks increasingly relies on fast control loops under time-varying traffic and radio conditions. A key challenge in such settings is reconfiguration churn: small score fluctuations near the admission boundary can repeatedly flip the marginal admitted request, even when the resulting utility gain is negligible. These boundary-level replacements are disproportionately expensive because admission and eviction trigger higher-layer control procedures, signaling exchanges, and coordination overhead. To address this problem, we propose TOA-S, a churn-aware admission primitive for latency-bounded mobile control. TOA-S targets cellular radio access network (RAN) deployments operating over licensed spectrum, where control loops of the ultra-reliable low-latency communication class at the near-real-time RAN Intelligent Controller must complete within sub-second decision windows. TOA-S preserves the greedy allocation structure for the responsive core of the admitted set and applies stabilization only at the admission boundary, in a single pass per decision epoch, without iterative optimization or learning. We show that TOA-S incurs only an $\varepsilon $ -bounded utility deviation, modifies at most one membership decision per epoch, and suppresses oscillatory replacements under persistent boundary near-tie conditions. Simulations on synthetic workloads with measurement-verified parameters show that TOA-S substantially reduces reconfiguration churn while maintaining spectrum utilization and latency-compatible execution.
Chi-Jen Wu· IEEE Open Journal of the Com...· 0 citations
The extensive deployment of the Power Internet of Things (PIoT) relies on dual-mode communication (HPLC + HRF) for robust data acquisition. However, under massive bursty traffic, conventional static MAC superframe scheduling struggles to reconcile high throughput with stringent reliability constraints. To mitigate this, we propose a dynamic adaptive scheduling scheme. Initially, a joint PHY-MAC layer dual-mode system architecture is proposed. At the MAC layer, a dual-link parallel multiplexing contention access mechanism is applied; at the physical layer, a capacity bottleneck determination model is established, incorporating log-normal–Bernoulli–Gaussian mixed noise and multipath fading. Subsequently, an extended two-dimensional Markov chain analytically derives key performance indicators, including equivalent collision probability, joint outage probability, access delay, and network throughput. Building upon this, a Q-learning-based algorithm is proposed. By constructing an asymmetric penalty–reward function, the central coordinator (CCO) autonomously optimizes the Contention Access Period (CAP) to Contention-Free Period (CFP) ratio under dynamic node scales. Simulations demonstrate this methodology effectively averts channel congestion during extreme concurrent traffic surges. Ultimately, it strictly preserves service reliability while substantially augmenting the concurrent carrying capacity and resource utilization of the dual-mode network.
Yue Zhao, Bo Jiang, Zhixiong Chen· Electronics· 1 citation
Next-generation wireless networks must maintain reliable operation under abrupt and severe disruptions, particularly in ultra-reliable low-latency communication (URLLC) scenarios where strict time constraints dominate system design. This work addresses network resilience from a time-centric perspective by explicitly integrating finite blocklength (FBL) communication, thereby exposing transmission duration as a controllable resource for system recovery. To this end, we propose a unified cross-layer framework that jointly couples queue dynamics, rate adaptation, and blocklength optimization, enabling the system to actively absorb, adapt to, and recover from diverse resilience events. To systematically evaluate these mechanisms, we introduce an interpretable resilience metric that decomposes disruption impact into absorption loss, adaptation efficiency, and recovery behavior, enabling a direct and intuitive assessment of system resilience. Building on this framework, we develop a three-stage alternating optimization approach that jointly optimizes PHY-layer parameters, including beamforming, reconfigurable intelligent surface (RIS) phase shifts, and blocklength, revealing the importance of time-aware resource allocation in the FBL regime. Numerical results demonstrate strong resilience performance under repeated channel disruptions and AI-driven traffic surges, highlighting the effectiveness of cross-layer resource adaptation. Finally, the proposed resilience metric enables an intuitive and consistent comparison of resilience performance across different approaches and disruption types, while revealing their respective strengths and limitations.
K. Weinberger, Aydin Sezgin, Mehdi Bennis· 0 citations
Reliable low-latency communication is a critical requirement in enterprise wireless networks such as hospitals, offices, and campuses. This paper proposes an earliest deadline first (EDF)-Lyapunov-Robbins-Monro (ELR), a stochastic scheduling algorithm for IEEE 802.11bn (Wi-Fi 8) Multi-Access Point Coordination Coordinated-Spatial Reuse (MAPC C-SR) networks that jointly accounts for queue stability and deadline-aware latency regulation under bursty traffic. A Lyapunov drift-based criterion for a group is adopted to ensure queues remain stable under varying traffic loads. Since the optimal balance between queue backlog and deadline urgency cannot be determined a priori under bursty traffic, EDF term is incorporated into the selection metric with a tunable balance parameter $\alpha$, governed by Robbins-Monro stochastic approximation scheme. The proposed algorithm addresses the inability of existing schedulers to track sudden congestion under bursty traffic, by dynamically adjusting $\alpha$ to suppress sharp delay spikes. Simulations over a four-access point (AP) enterprise deployment under bursty Markov-Modulated Poisson Process (MMPP) traffic demonstrate that ELR achieves 14.23%, 13.26%, and 7.97% reduction in 99th percentile delay over maximum number of packets (MNP), oldest packet (OP), and traffic alignment tracker (TAT) respectively under high load with 16 stations (STAs).
Hiya Shah· International Conference on...· 0 citations
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