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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
Deterministic Scheduling of Periodic Messages for Low Lat...
Dominique Barth, Maël Guiraud, Yann Strozecki · 2018-01-22 · via cs.DS updates on arXiv.org

Cloud-RAN (C-RAN) is a cellular network architecture where processing units, previously attached to antennas, are centralized in data centers. The main challenge in meeting protocol time constraints is minimizing the latency of periodic messages exchanged between antennas and processing units. We demonstrate that statistical multiplexing introduces significant logical latency due to buffering at network nodes to prevent collisions. To address this, we propose a deterministic scheme for periodic message transmission without collisions, eliminating latency caused by buffering. We develop several algorithms to compute such schemes for star-routed networks, a common topology where all antennas share a single link. First, we show that deterministic transmission is possible without buffering when routes are short or network load is low. Under high load, we allow buffering at processing units and introduce the Periodic Minimal Latency Scheduling (PMLS) algorithm, adapted from classical scheduling methods. Experimental results indicate that even at full load, PMLS finds deterministic transmission schemes with negligible logical latency, whereas statistical multiplexing incurs substantial delays. Moreover, PMLS runs in polynomial time and scales efficiently to hundreds of antennas. Building on this approach, we also derive low-latency periodic transmission schemes that coexist with additional random network traffic. This article extends previous work presented at ICT.