Jul 2026· International Conference on Smart Communications and Networking· pp. 1-4· 0 citations· 17 references
Abstract
Achieving deterministic latency for time-sensitive flows within integrated 5G and Time-Sensitive Networking (TSN) ecosystem requires the active mitigation of stochastic delays inherent in 5G New Radio (NR). While existing research typically relies on pessimistic guard bands or over-provisioned time-domain resources via wired TSN mechanisms, these approaches fail to adaptively reserve NR resources under dynamic channel conditions to suppress Packet Delay Variation (PDV). This work addresses this gap by proposing a joint NR MAC scheduling and Link Adaptation (LA) framework. We introduce Link Adaptive Semi-Persistent Scheduling (LA-SPS), a framework that ensures cycle-synchronous uplink opportunities by dynamically reconfiguring resource budgets and modulation parameters from real-time channel feedback. To manage the combinatorial complexity of joint resource allocation, we employ a Graph Neural Network (GNN) to encode scalable network states and Proximal Policy Optimization (PPO) for stable, real-time decision-making. This modular framework functions as a radio-side control loop designed for seamless coupling with end-to-end Time-Aware Shaper (TAS) scheduler, enabling a fully co-adaptive industrial network.
The integration of Low Earth Orbit (LEO) Non-Terrestrial Networks (NTNs) into 5G and upcoming 6G architectures introduces various challenges, including severe propagation delays, ultra-high base station mobility, and channel non-stationarity, complicating radio resource management of heterogeneous network slices. In this paper, we propose a deep reinforcement learning (DRL) meta-scheduler for twin-timescale resource allocation. Our solution adopts a decoupled Open Radio Access Network (RAN) architecture, in which a strategic 100 ms meta-scheduler selects scheduling policies for the different network slices using stale telemetry, while a fast-timescale MAC packet scheduler processes per-TTI user requests. The resulting Markov Decision Process captures non-stationary orbital dynamics and heterogeneous SLAs constraints via a TD3 agent. Simulation results under varying traffic load show that, unlike other solutions, the proposed meta-scheduler explicitly trades a statistically insignificant 1% capacity fraction (p>0.05) to strictly bound the variance and overall magnitude of RLC-layer queuing delay for Mission-Critical (MC) traffic. Crucially, it enforces this isolation without inducing the broadband slice starvation characteristic of standard maximum-CQI heuristics, establishing a robust foundation for 6G O-RAN NTN resource allocation.
Víctor Vilchez, T. P. C. de Andrade, Edward Hinojosa et al.· 0 citations
Coordinated beamforming in distributed 5G networks relies on the timely exchange of inter-cell scheduling information, but backhaul latency makes this information stale. Even a single transmission time interval (TTI) of delay can reduce CBF-SLNR performance below the uncoordinated baseline, because the precoder suppresses interference toward users that are no longer active. Coordination on stale information is therefore worse than no coordination at all. To address this, we propose a two-stage predictive framework in which a Spectral Temporal Graph Neural Network (StemGNN) predicts future user equipment (UE) scheduling states from delayed historical observations, and the predictions replace stale inputs to the CBF-SLNR precoder. Evaluated on a three-cell massive MIMO downlink with 60 UEs and 64 antennas per base station under Quadriga Urban Micro (UMi) channels and a proportional fair scheduler, StemGNN achieves a mean scheduling prediction accuracy of 87.57%, outperforming LSTM, GRU, Simple RNN, and Markov chain baselines at all evaluated horizons, with gains of up to 7.71% over LSTM at longer horizons where inter-UE structural dependencies dominate over temporal autocorrelation. When integrated into coordinated beamforming, the predictions recover 57-73% of the sum rate loss caused by one TTI of backhaul delay, improving sum rate by 9.58-14.35% over the no-prediction baseline and recovering up to 83% of the Lag-1 fairness loss for cell-edge users, with fairness gains persisting at higher lag values where throughput gains diminish. These results show that treating backhaul latency as a spatio-temporal forecasting problem is an effective approach for robust inter-cell coordination in delay-constrained networks.
P. Singh, Shubham Vaishnav, A. Gokceoglu et al.· 0 citations
Current end-to-end congestion-control feedback is too slow to track rapid wireless dynamics in cellular networks. We identify Grant-to-Buffer Ratio (GBR)—the ratio of base-station uplink grants to mobile-reported demand—as a millisecond-scale RAN signal of uplink resource scarcity. Measurements across AT&T, Verizon, and T-Mobile LTE/5G FDD/TDD deployments show that GBR tracks base-station uplink load and reveals congestion earlier than end-to-end feedback. Because GBR is derived from the mandatory BSR-grant exchange, it requires no base-station changes and captures scheduler decisions at their native timescale. We then design GBR-CC, a dual-loop controller that updates the sender rate on each GBR sample, using GBR for fast adaptation and end-to-end delay trends as a conservative fallback. This design lets the sender react before queues inflate while still handling non-radio bottlenecks through the outer loop. GBR-CC runs on commodity mobile devices without extra hardware or external tools. Experiments on commercial cellular networks show that GBR-CC improves average throughput over GCC by 50%, while reducing median playout latency by 32–53% and freeze rate by 60%; compared with BBR, it improves average throughput by 5% and halves median RTT.
Qiang Wu, Yuxin Liu, Tianyang Zhang et al.· Conference on Applications,...· 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
The integration of Time-Sensitive Networking (TSN) and 5G is essential for deterministic industrial communication over mixed wired–wireless networks. In 3GPP TSN–5G integration, the 5G System (5GS) is modeled as a logical TSN bridge for Time-Aware Shaper (TAS) scheduling. However, Time Division Duplex (TDD) operation in 5GS introduces the transmission waiting time, and conventional bridge-delay-based scheduling must rely on conservative worst-case assumptions.In this paper, we analyze the periodic structure of TDD-induced waiting time and show that its worst-case value is determined by the TDD pattern, TSN flow period, and their phase offset. Based on this insight, we propose a phase-aware TAS scheduling method that derives a mapping between phase offsets and worst-case waiting times and incorporates it into E2E TAS scheduling. Simulations under multiple TSN flow periods and TDD patterns show that the proposed method consistently reduces worst-case waiting time compared with the conventional approach. Multiple flow evaluations also show that multiple TSN flows can be scheduled while satisfying E2E delay constraints under the evaluated configuration. This enables more flexible deployment of time-critical industrial IoT applications, including mobile robot control, motion control, and factory automation over mixed wired–wireless networks.
Keita Kuwayama, H. Kawata, Hironao Abe et al.· International Conference on...· 0 citations
Simulation results demonstrate that the proposed QoS-mapping-based no-wait latency-balanced joint scheduling (QMLB-JS) algorithm improves the end-to-end deterministic transmission capability of the integrated 5G-TSN network.
He Li, Shihui Duan, Fangmin Xu et al.· IEEE Open Journal of the Com...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
