repeated contention scheduling

This work addresses the persistent resource conflicts and limited adaptability of conventional semi-persistent scheduling (SPS) and dynamic scheduling (DS) in NR-V2X sidelink communications under high-density, dynamic scenarios. To overcome these limitations, the authors propose RCS, a fully distributed, multi-round feedback-based competitive scheduling algorithm that eliminates long-term resource reservations and instead enables efficient allocation through reservation-free, feedback-driven contention. The scheme innovatively incorporates a distributed multi-round competition mechanism and is experimentally validated on a software-defined radio (SDR) platform. Both simulations and real-world measurements demonstrate that RCS significantly outperforms SPS and DS under high traffic loads, exhibiting superior robustness and scalability across key performance metrics—including transmission success probability, collision and packet loss rates, and information timeliness as measured by inter-packet delay and age of information.

In wireless multihop networks, link capacity exhibits nonlinear, environment-dependent behavior due to stochastic channel contention, rendering classical minimum-cost flow approaches ineffective. To address this, we propose a differentiable Network Digital Twin (NDT) framework: (i) it integrates the weighted Luby algorithm into conflict graph modeling to accurately capture distributed random access; (ii) it derives an analytical expression for link duty cycle and resolves the cyclic dependency among duty cycle, capacity, and contention probability via implicit function iteration; and (iii) it enables end-to-end differentiable link scheduling optimization. Experiments demonstrate that NDT achieves low prediction error for duty cycles and congestion patterns, accelerates computation by 5,000× over packet-level simulation, and significantly reduces network congestion and RF resource utilization.

This paper addresses persistent high-amplitude oscillations in path-aware networks, arising from uncoordinated, greedy path selection by multiple endpoints. We quantitatively analyze their impact on efficiency, fairness, and convergence. To model the coupled dynamics of path selection and congestion control, we propose a novel dynamic system framework integrating game theory and control theory. We establish the first axiomatic analytical framework to formally classify periodic oscillation patterns and uncover a new mechanism enabling joint optimization of efficiency, convergence, and packet-loss avoidance. Theoretically, we show that user migration induces desynchronization, enhancing system stability—a finding that challenges conventional trade-off assumptions. Comprehensive simulations validate our theoretical predictions and yield quantifiable design principles for path-aware Internet protocols.

To address the challenge of deterministic low-latency scheduling for periodic message transmission between antennas and remote processing units in Cloud-RAN—where strict protocol deadlines must be met while avoiding buffering and collision delays induced by statistical multiplexing—this paper pioneers the application of deterministic conflict-free scheduling to Cloud-RAN fronthaul networks. We propose two algorithms: (i) an analytical zero-buffer scheduling algorithm tailored for short-path or light-load scenarios, and (ii) PMLS (Periodic Message Latency Scheduling), a heuristic algorithm supporting bounded buffering. We theoretically prove that a zero-buffer feasible schedule always exists under short-path or low-load conditions. Experimental results demonstrate that PMLS achieves zero-delay deterministic schedules with high probability even under full load, significantly enhancing latency predictability and resource utilization.

To address the resource allocation challenge for ultra-reliable low-latency communication (URLLC) in smart factories—characterized by fragmented spectrum, absence of instantaneous channel state information (CSI), and strong external interference—this paper proposes a robust resource allocation method based on a *shareability graph*. We pioneer the adaptation of a graph-theoretic framework originally designed for shared mobility to wireless URLLC scenarios. Relying solely on network topology and statistical channel knowledge, we construct the shareability graph and solve for its maximum-weight matching to enable periodic, reliable, low-latency transmissions from devices to sink nodes. The approach jointly optimizes spectral efficiency and fairness: compared to an optimal benchmark, it achieves a 50% gain in spectral efficiency while simultaneously improving fairness metrics—demonstrating that high efficiency and fairness are mutually attainable.

This work proposes a semantic-aware, intent-based wireless resource orchestration approach tailored for Open RAN architecture and evaluates its performance under realistic observability constraints. To this end, the authors develop a scalable ns-3-based simulation framework integrating a RAN Intelligent Controller (RIC) with distributed applications (dApps), enabling intent-driven orchestration across multiple time scales, validated through a wireless resource management use case. A novel Intent Satisfaction Score (ISS) metric is introduced, combining distortion and perception-oriented measures to achieve, for the first time in Open RAN simulations, semantic-aware closed-loop control. Experimental results demonstrate that the proposed method significantly reduces radio resource consumption and computational overhead while effectively improving intent satisfaction, at the cost of only moderate degradation in packet delivery ratio and throughput.

This work addresses the deterministic communication requirements of time-sensitive applications in Beyond 5G networks by proposing a predictive dynamic wireless resource scheduling mechanism. Integrating traffic prediction with dynamic scheduling, the approach intelligently reserves resources likely to be needed in the near future while satisfying current latency constraints, thereby overcoming the limitations of conventional semi-static scheduling. By proactively managing prediction uncertainty, the proposed scheme significantly enhances resource utilization efficiency under mixed traffic loads and diverse QoS requirements, while effectively guaranteeing bounded latency and service quality.

This study addresses the problem of distributed throughput optimization in dense multi-access point (Multi-AP) IEEE P802.11be networks by constructing a packet-level system model that incorporates CSMA/CA, RTS/CTS, beam training overhead, directional millimeter-wave interference, SINR-driven MCS selection, and retransmission mechanisms. The configuration optimization is formulated as a combinatorial multi-armed bandit (CMAB) problem with multiple groups. To efficiently navigate the high-dimensional discrete configuration space, the authors propose an innovative exploration strategy guided by Hadamard matrices and a grouped combinatorial Successive Accept-Reject (CSAR) algorithm. Experimental results demonstrate that the proposed approach significantly improves both aggregate and per-AP throughput across various AP densities and reduces throughput convergence time by approximately 49%.

This work addresses the lack of systematic understanding of resource utilization and performance characteristics of distributed applications (dApps) in O-RAN across diverse deployment environments, which hinders efficient deployment decisions. We present the first comprehensive evaluation of representative dApps, quantifying trade-offs in latency, scalability, and resource efficiency between bare-metal and containerized deployments. Furthermore, we investigate real-time performance optimization through hardware acceleration using Smart NICs. Through extensive benchmarking and fine-grained resource monitoring, we characterize the performance gaps between the two deployment paradigms, identify critical bottlenecks, and propose a Smart NIC-based task offloading scheme that significantly enhances both real-time responsiveness and resource efficiency of dApps.

This work addresses the challenge of throughput degradation in multi-hop wireless IoT networks caused by transmission collisions under fluctuating link quality, a common issue in conventional contention-based data dissemination protocols. To overcome this limitation, the authors propose the EDRP protocol, which integrates real-time link quality estimation into the CSMA backoff mechanism (LQ-CSMA) and couples it with a machine learning–driven fountain code block size selection algorithm (ML-BSS). This joint approach dynamically optimizes both transmission scheduling and coding efficiency. By coordinating node behavior through distributed delay timers, EDRP achieves a 39.43% average improvement in effective throughput over existing protocols in real-world experimental environments, demonstrating significant performance gains while maintaining practical deployability.