proportional-fair scheduling

研究通过结合MaxWeight算法和UCB估计，解决了用户关联、调度与速率自适应跨时间尺度联合设计问题，以最大化网络吞吐量并确保用户间公平性。

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 inability of the Credit-Based Shaper (CBS) to guarantee deterministic end-to-end latency in 5G/6G radio access networks (RAN), arising from NR’s discrete-slot scheduling and modulation-dependent resource allocation, this paper proposes two novel mechanisms: Slot-Level Discrete-Time CBS (CBS-DT) and Partial-Update CBS (CBS-PU)—the first CBS variants natively aligned with NR slot-level scheduling. Our approach introduces UE-granularity queue modeling, TBS-aware byte-level credit deduction, a partial-byte credit update algorithm, and an enhanced TSN-QoS/5G-QoS mapping framework. These innovations preserve bandwidth reservation and bounded latency while significantly improving resource efficiency. Simulation results demonstrate that CBS-PU achieves ≤10 ms TSN-grade deterministic latency, increases downlink resource utilization by 37%, and reduces latency jitter by 52%.

This work addresses the inefficiencies of traditional Dominant Resource Fairness (DRF) in multi-tenant, multi-resource systems, where fixed resource proportion assumptions lead to significant waste and suboptimal performance under resource overcommitment and inter-resource dependencies. To overcome these limitations, the authors propose Dependency-aware Dominant Resource Fairness (DDRF), a novel allocation mechanism that explicitly models real-world resource dependencies and dynamically equalizes dominant shares among active tenants on congested resources, thereby relaxing DRF’s rigid proportionality constraints. DDRF preserves Pareto efficiency while substantially mitigating resource fragmentation caused by low-demand tenants. Implemented within a centralized orchestration framework, DDRF demonstrates substantial improvements over baseline approaches in both EC2 and vRAN scenarios, achieving up to 80% higher user satisfaction, 60% less resource waste, and a greater than 15% increase in the Jain fairness index.

This study addresses the significant degradation in system throughput caused by conventional throughput-based fairness mechanisms in multi-rate WLANs, where heterogeneous channel conditions lead to inefficient resource allocation. To resolve this issue, the paper introduces the concept of time fairness and proposes TBR (Time-based Regulator), a novel algorithm implemented at the access point that enforces fair scheduling by regulating each node’s channel occupancy time. Operating within the constraints of the existing DCF MAC protocol, TBR achieves a balanced trade-off between fairness and aggregate throughput. Experimental results demonstrate that the proposed mechanism effectively enhances overall network throughput while preserving fundamental per-node access performance in single-rate scenarios.

This work addresses the inherent unfairness in uplink NOMA-ISAC systems, where conventional resource allocation strategies favor strong users at the expense of weak users, resulting in persistently low throughput and poor fairness for the latter. To overcome this limitation, the authors propose a novel Proportional Fairness-based Joint User Grouping and Power Allocation (PF-JUGPA) method, which uniquely incorporates users’ historical service rates into both scheduling and resource allocation. By jointly optimizing user grouping and power allocation under a proportional fairness criterion, the proposed approach achieves an effective trade-off between system throughput and user fairness while preserving sensing performance. Experimental results demonstrate that PF-JUGPA significantly improves the Jain fairness index and the average rate of weak users, with only a marginal reduction in total system throughput.

This work addresses dynamic, heterogeneous, and potentially budget-exceeding demand in multi-location dual-service scenarios by proposing a two-level adaptive capacity allocation algorithm. The first level proportionally redistributes surplus and deficit capacities within each service class across locations, while the second level enables elastic cross-class capacity borrowing to handle bursty loads. This approach uniquely integrates intra-class proportional reallocation with inter-class elastic borrowing, achieving stateless, single-round convergence under a fixed budget—thereby overcoming throughput maximization limitations in contention-prone settings. With computational complexity O(KN) for K service classes and N locations, the algorithm supports real-time scheduling. Experiments demonstrate that, in CDN-based defense against traffic attacks, it satisfies 66%–93% of high-priority requests—matching the performance of single-class linear programming optima—while consistently avoiding both underutilization and over-provisioning even when total demand exceeds the budget. A prototype validates its efficacy under real HTTP traffic.

This work addresses a critical limitation in existing multimedia streaming resource allocation methods, which overlook users’ asymmetric sensitivity to throughput fluctuations—particularly their loss aversion. To bridge this gap, the study introduces prospect theory into the domain for the first time, formulating a utility model that jointly captures the average throughput and the asymmetric impact of its variations. Building upon multi-queue system theory, the authors devise a dynamic threshold-based scheduling policy that gradually reduces resource allocation as availability transitions from abundant to scarce, while preserving user priority considerations. The proposed approach enables online, rapid optimization of thresholds and demonstrates significant performance gains over conventional fair allocation schemes in simulations. Results confirm that it better aligns with real-world user satisfaction and offers strong potential for practical deployment.

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 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.