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Theoretical Limits of Language Model Alignment $f$-Divergence Regularized RLHF: Two Tales of Sampling and Unified Analyses A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models When Can Voting Help, Hurt, or Change Course? Exact Structure of Binary Test-Time Aggregation When Semantic Communication Meets Queueing: Cross-Layer Latency and Task Fidelity Optimization Convexity in Disguise: A Theoretical Framework for Nonconvex Low-Rank Matrix Estimation Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees Sharp Capacity Thresholds in Linear Associative Memory: From Winner-Take-All to Listwise Retrieval Expert Routing for Communication-Efficient MoE via Finite Expert Banks Contextual Memory-Enhanced Source Coding for Low-SNR Communications Realizable Bayes-Consistency for General Metric Losses Leveraging Code Automorphisms for Improved Syndrome-Based Neural Decoding A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learning Dueling DDQN-Based Adaptive Multi-Objective Handover Optimization for LEO Satellite Networks The Causal Description Gap: Information-Theoretic Separations Across Pearl's Hierarchy Optimization of CV-QKD Under Practical Constraints Benchmarking Wireless Representations: High-Dimensional vs. Compressed Embeddings for Efficiency and Robustness Real-Time Text Transmission via LLM-Based Entropy Coding over Fixed-Rate Channels SwiftChannel: Algorithm-Hardware Co-Design for Deep Learning-Based 5G Channel Estimation Evolving Token Communication with Parametric Memory Network Remote Action Generation: Remote Control with Minimal Communication The (Marginal) Value of a Search Ad: An Online Causal Framework for Repeated Second-price Auctions Stabilizing Private LASSO under Heterogeneous Covariates via Anisotropic Objective Perturbation Linear-Readout Floors and Threshold Recovery in Computation in Superposition Soft Graph Diffusion Transformer for MIMO Detection Hierarchical Federated Learning for Networked AI: From Communication Saving to Architecture-Aware Design Exponential families from a single KL identity MIFair: A Mutual-Information Framework for Intersectionality and Multiclass Fairness Diffusion-OAMP for Joint Image Compression and Wireless Transmission Decoupled Descent: Exact Test Error Tracking Via Approximate Message Passing
Mobile Service-Based Cooperative Scheduling for High-Mobi...
Yu Zhang, Ke Xiong, Pingyi Fan, Xianwei Zhou · 2015-11-24 · via cs.IT updates on arXiv.org

This paper investigates the downlink scheduling for relay-aided high-mobility vehicular networks, where the vehicles with good vehicle-to-infrastructure (V2I) links are employed as cooperative relay nodes to help the ones with poor V2I links forward information via vehicle-to-vehicle (V2V) links. In existing works, instantaneous achievable information rate was widely adopted to perform the link scheduling, but it is not efficient for vehicular networks, especially for high-mobility scenarios. Different from them, in this paper, we introduce the mobile service to describe the mobile link capacity of vehicular networks and then we propose a mobile service based relaying scheduling (MSRS) for high mobility vehicular networks. In order to explore the system information transmission performance limit, we formulate an optimization problem to maximize the mobile service amount of MSRS by jointly scheduling the V2I and V2V links. Since it is a combinational optimization problem which is too complex to solve, we design an efficient algorithm with low-complexity for it, where Sort-then-Select, Hungarian algorithm and Bisection search are employed. Simulation results demonstrate that our proposed MSRS is able to achieve the optimal results with an optimal approximation ratio larger than 96.5%. It is also shown that our proposed MSRS is much more efficient for high-mobility vehicular systems, which can improve the system average throughput with increment of 3.63% compared with existing instantaneous achievable information rate based scheduling method, and with 15% increment compared with traditional non-cooperation scheduling method, respectively.