radio resource management

This work addresses the inherent lack of deterministic communication support in native 5G networks, which struggles to meet the stringent bounded latency and high reliability requirements of Time-Sensitive Networking (TSN) in industrial scenarios. The paper proposes a deeply integrated 5G-TSN configuration grant scheduling scheme that achieves, for the first time, cross-domain joint scheduling. By leveraging key characteristics of TSN traffic—such as periodicity, packet size, and arrival timing—the authors design a deterministic resource allocation algorithm. This approach overcomes the capacity and flexibility limitations of existing solutions, significantly enhancing the network’s ability to support heterogeneous TSN flows with diverse periods while simultaneously guaranteeing end-to-end latency bounds and improving resource utilization efficiency.

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 work addresses the challenge that conventional configured grant (CG) scheduling struggles to meet the bounded latency requirements of deterministic communication under variable traffic conditions. To overcome this limitation, the paper proposes a novel CG scheduling mechanism that integrates traffic prediction with robust optimization, explicitly incorporating prediction uncertainty into resource pre-allocation decisions for the first time. The approach dynamically adapts to the heterogeneous latency constraints of mixed traffic types while ensuring bounded end-to-end delays. By jointly optimizing resource allocation under uncertainty, the method significantly improves resource utilization without compromising timing guarantees. Extensive evaluations demonstrate that the proposed scheme maintains superior performance even in highly dynamic and diverse traffic scenarios, thereby substantially enhancing the system’s capability to support deterministic services.

To address the challenge of QoS differentiation across multi-service scenarios (eMBB, mMTC, URLLC) in O-RAN-enabled 5G networks, this paper proposes and implements a pluggable, dynamic TDD-aware xApp scheduler integrated into an ns-3+O-RAN simulation platform. Our approach is the first to jointly model the O-RAN near-real-time RIC interface and dynamic TDD scheduling within ns-3, enabling comparative evaluation of MT, PF, and RR scheduling policies with real-time resource adaptation. Experimental results demonstrate that MT and PF significantly outperform RR in both throughput and fairness; MCS selection, symbol allocation, and TTI assignment align closely with measured throughput; and overall resource utilization improves by 37%. This work overcomes the limitations of conventional static schedulers and establishes a reproducible, extensible validation framework for native intelligent scheduling in O-RAN.

Ensuring deterministic uplink performance for time-sensitive traffic in industrial 5G–Time-Sensitive Networking (TSN) convergence under mobility remains challenging. Method: This paper proposes a heterogeneous wireless resource joint-scheduling architecture, pioneering the use of 5G TDD base stations as transparent TSN bridges to enable end-to-end time-aware scheduling. It integrates static configuration with dynamic scheduling (Proportional Fair/Max C/I), jointly modeling bridging latency, time-aware traffic shaping, and flow filtering & policing to guarantee deadline compliance for periodic flows. Contribution/Results: Experiments demonstrate a 28% improvement in radio resource efficiency over Configured Grant baseline; 100% of time-sensitive flows meet their deadlines; and non-deterministic traffic throughput increases significantly. The work establishes, for the first time, the feasibility of deploying 5G infrastructure as transparent TSN bridges and delivers a verifiable, deterministic uplink scheduling framework for industrial 5G–TSN integration.

This study addresses the challenge of jointly guaranteeing end-to-end latency and reliability in the integration of 5G and Time-Sensitive Networking (TSN) within Industry 4.0 scenarios. The authors propose a novel 5G configured grant scheduling mechanism that, for the first time, incorporates TSN traffic characteristics into 5G scheduling decisions to achieve deep cross-domain coordination. By leveraging the temporal properties of TSN traffic flows, the method dynamically optimizes 5G radio resource allocation, significantly enhancing the system’s support for deterministic communication. Experimental results demonstrate that the proposed scheme substantially increases the number of TSN traffic flows that can be successfully accommodated, effectively meeting the stringent low-latency and high-reliability requirements of industrial applications.

This work addresses the challenge that static resource unit (RU) scheduling fails to meet the deterministic communication requirements of Time-Sensitive Networking (TSN) under dynamic traffic conditions. To overcome this limitation, the authors propose a dynamic RU allocation algorithm that, for the first time, maps TSN traffic classes to Wi-Fi 6 quality-of-service (QoS) mechanisms—such as Enhanced Distributed Channel Access (EDCA)—and enables coordinated scheduling with the Ethernet TSN domain to achieve cross-domain deterministic communication. Experimental evaluation using the ns-3 DetNetWiFi simulation framework demonstrates that, compared to static allocation, the proposed approach significantly reduces latency, jitter, and packet loss, thereby substantially improving the transmission efficiency and reliability of time-sensitive traffic in hybrid industrial networks.

This study addresses the challenge of excessive uplink latency in existing 5G dynamic scheduling—caused by signaling overhead—which hinders support for ultra-reliable low-latency communication (URLLC) requirements in Industry 4.0. The authors present the first implementation and validation of the 5G NR Configured Grant (CG) mechanism within the open-source system-level simulator ns-3 5G-LENA. By pre-allocating uplink resources, CG eliminates per-packet scheduling requests, while enhanced OFDMA modeling improves the fidelity of 5G NR’s flexibility. This work fills a critical gap in open-source platforms for simulating URLLC-enabling features and provides a reproducible framework for scheduling research. Simulation results align closely with theoretical analysis, demonstrating that CG significantly reduces latency, meets industrial reliability demands, and underscores the importance of efficient radio resource utilization.

This work addresses the challenge of coordinating multiple radio access technologies (RATs) in Beyond 3G heterogeneous wireless systems to meet the quality-of-service (QoS) requirements of multimedia traffic. The paper proposes a unified wireless resource management strategy that, for the first time, applies linear programming to jointly optimize RAT selection and radio resource allocation within a cross-RAT scheduling framework. By formulating a linear objective function, the approach dynamically assigns each user an optimal RAT type and corresponding resource amount, effectively satisfying multimedia QoS constraints while significantly improving overall system resource utilization efficiency.

This work addresses the challenge that static time-slot configurations in industrial 5G TDD networks struggle to accommodate dynamic asymmetric traffic and diverse QoS requirements. To overcome this limitation, the authors propose FLEX, a scheduler that dynamically adjusts the uplink-downlink ratio of flexible TDD slots to align with industrial traffic characteristics while preserving bidirectional QoS priorities. FLEX incorporates a downlink buffer-state-aware scheduling mechanism that leverages the deterministic nature of industrial traffic to prevent starvation of high-priority downlink flows and achieve low-latency transmission. Simulations based on 5G LENA and ns-3 demonstrate that FLEX meets stringent bidirectional QoS constraints with high fidelity, introduces less than one slot of additional latency for deterministic traffic, and maintains throughput comparable to existing schedulers.