Controlling Adaptive HARQ Erasure Coding for Real-Time Transport Under Channel Model Mismatch

Delay Violation Probability Modeling for 5G Systems with HARQ Operation

Meeting the growing demand for quality-of-service (QoS) guarantees in 5G networks requires an accurate characterization of delay performance, commonly captured by the delay violation probability (DVP) at a specified delay target. Although hybrid automatic repeat request (HARQ) is a fundamental reliability mechanism in wireless systems and is central to supporting QoS, many existing approaches to DVP prediction for HARQ remain overly simplified. In particular, they omit important delay components and adopt assumptions that do not reflect the operation of HARQ in slot-based systems such as 5G. Consequently, these models can substantially underestimate the DVP, especially under stringent latency requirements, where the contribution of the neglected components becomes critical. To address this gap, we develop a tractable DVP characterization for 5G HARQ that accounts for queueing, transmission, decoding, and feedback delay, as well as the contribution of Control Signaling (CS) transmissions to the overall delay, under practical timing assumptions consistent with 3GPP operation. Moreover, we incorporate parallel packet transmissions that proceed without waiting for earlier packets to succeed, an essential HARQ behavior frequently overlooked in prior work. Using tools from queueing theory and Markov analysis, we then derive upper bounds on the DVP and validate them against ns-3 5G-LENA simulations.

From Metric to Mechanism: Designing Wireless Resilience through Finite Blocklength Dynamics

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.

Link-Adaptive Semi-Persistent Scheduling for PDV-Reduced TSN Uplink over 5G NR

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.

Scheduler-Agnostic Adaptive-FEC for MPQUIC: Field Evaluation over Commercial Cellular Paths

Multipath transport has long been studied to improve robustness over heterogeneous networks, and Multipath QUIC (MPQUIC) is particularly attractive for low-latency applications because QUIC Datagram enables transmission without retransmissions. However, this advantage comes with a key drawback: QUIC Datagram is vulnerable to packet loss, especially in vehicular cellular environments with handovers and rapidly varying radio conditions. While prior studies have explored adaptive-FEC and coded multipath designs, practical deployment remains challenging when such mechanisms require substantial modifications to the transport stack or scheduler. In this paper, we present an adaptive Forward Error Correction (FEC) scheme for MPQUIC Datagram that reuses QUIC loss detection signals and an off-the-shelf Reed-Solomon (RS) coding library. The sender estimates smoothed per-path loss rates from QUIC loss signals and adaptively determines the number of parity packets for each FEC block according to the expected packet loss. We implement the proposed scheme on an existing MPQUIC stack and evaluate it through vehicular field experiments over three commercial LTE/5G paths. Under the main 4.0 Mbps setting, the proposed scheme improves both reliability and latency compared with No-FEC: the average one-way delay is reduced from 103.0 ms to 70.8 ms, the 95th-percentile delay from 281.2 ms to 142.3 ms, and the packet loss rate from 1.7 % to 0.8 %, with an average coding rate of 0.94. These results indicate that scheduler-agnostic adaptive-FEC can provide practical latency and reliability gains for MPQUIC Datagram under the measured vehicular mobile conditions.

Modeling and Analysis of Isochronous Communication Under Lifetime- and Flushing-Based Mechanisms

PHY-Layer Modeling and Throughput-Driven Adaptation for Batteryless V2X Networks

Passive overlay communication for batteryless devices is an important enabling capability for next-generation vehicle-to-everything (V2X) networks. However, enabling reliable passive payload delivery without occupying additional spectrum remains challenging, since overlay signaling must be embedded into short and time-varying vehicular packets while preserving the decodability of the legacy host transmission. This paper investigates a packetized batteryless V2X overlay architecture in which a dedicated short-range communications (DSRC)-based packet simultaneously carries conventional V2X data and a passive overlay payload. A compact PHY-layer model is developed to characterize the coupled effects of attenuation depth, embedded-bit rate, and legacy modulation and coding scheme (MCS) on host-link and passive-link reliability, as well as packet-level embedding feasibility. We then formulate a sum-throughput maximization problem that jointly accounts for the legacy packet error rate and passive decoding error rate. We further propose a multi-agent reinforcement learning (MARL)-based adaptive parameter-selection method. Simulation results show that the proposed MARL controller achieves stable convergence and improves the average throughput by 15\%, demonstrating the effectiveness of throughput-driven PHY adaptation for batteryless V2X overlay communications.