Joint Routing and Scheduling Optimization for Injection Time Offsets in Time Sensitive Networking

Cross-Layer Scheduling Optimization of 5G-TSN Integrated Networks Based on QoS Mapping and Joint Scheduling

Fifth-generation (5G) and time-sensitive networking (TSN) are widely recognized as the most promising technologies for future industrial networks. Quality of service (QoS) mapping and traffic scheduling mechanisms are critical to ensuring deterministic transmission in 5G-TSN integrated networks. However, the uncertainty of 5G air-interface delay significantly reduces the deterministic guarantee for traffic in 5G-TSN networks. A QoS mapping algorithm based on incremental mini-batch K-means++ stratified sampling (IMK-S3) is proposed to quickly and accurately determine the 5G QoS identifier (5QI) values of traffic flows, even under dynamic traffic variations. Based on this, combined with cross-layer scheduling optimization, a QoS-mapping-based no-wait latency-balanced joint scheduling (QMLB-JS) algorithm is proposed. QMLB-JS supports hold and forward buffer mechanism in DS-TT and NW-TT, and realizes accurate time-based gating management by orchestrating the time of time-triggered (TT) traffic injection into the network. Simulation results demonstrate that the proposed algorithm improves the end-to-end deterministic transmission capability of the integrated 5G-TSN network.

A Scheduling algorithm for joint flow control and resource allocation for dual-connectivity MQMS systems

This paper proposes a cross-layer scheduling algorithm for joint flow control and resource allocation to optimize the time-average utility function for the dual-connectivity multi-queue multi-server (D-C MQMS) system with a stochastic arrival process. First, we derive the capacity region of the D-C MQMS system by a finite set of linear inequalities. The capacity region characterizes the maximum input rate supported by the system, which is useful for network management. Furthermore, the characterization of the distance between the input rate vector and the boundary of capacity region is exhibited when the channel model is an ON/OFF channel. Then we consider two cases: (I) input rates within the capacity region, and (II) input rates exceeding the capacity region. Two scheduling algorithms for joint flow control and resource allocation, FCRA-I and FCRA-II, are proposed by Lyapunov optimization method. Next, the performance on the utility function and queue delay is analyzed. Finally, by comparing the proposed method with the single-connectivity (S-C) system, we verify the algorithm’s effectiveness by evaluating the average system throughput, system user perceived throughput (SUPT), the throughput-fairness utility function, and the average system backlog.

Adaptive Scheduling for Low-Latency Coordination in Wi-Fi 8 Multi-AP Networks

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).

Hybrid DQN–PPO control for joint queue management and bandwidth allocation under bursty network traffic

A hybrid reinforcement learning (RL) framework that jointly controls queue management and bandwidth allocation in bursty multi-service networks and demonstrates the effectiveness of coordinated learning-based control for stable and QoS-aware operation in bursty networked systems.

DTMC-Based Analysis and Scheduling for Periodic Flows with Proactive HARQ

Ultra-Reliable Low-Latency Communication (URLLC) requires strict reliability and latency guarantees for heterogeneous periodic traffic. Proactive HARQ improves resource efficiency through early termination, but slot-level timing effects, particularly delayed feedback, complicate schedulability analysis. This paper presents a discrete-time Markov chain (DTMC)-based framework for periodic flows with proactive HARQ. By expanding the state space, the model captures HARQ round-trip time and other cross-slot timing effects. The framework determines the transmission opportunities required to satisfy heterogeneous reliability and latency constraints and supports offset-based scheduling through a two-stage genetic algorithm. Simulations with industrial URLLC traffic show that the proposed method achieves higher schedulability than reactive HARQ, K-Repetition, and non-guaranteed proactive HARQ, with acceptable computational overhead.

Journal of Systems Architecture