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Found 6 papers, 1 papers with code
Replication: Contrastive Learning and Data Augmentation in Traffic Classification Using a Flowpic Input Representation
Over the last years we witnessed a renewed interest toward Traffic Classification (TC) captivated by the rise of Deep Learning (DL).
User-aware WLAN Transmit Power Control in the Wild
Yet each network comes with its unique distribution of users in space, calling for a power control that adapts to users' probabilities of presence, for example, placing the areas with higher interference probabilities where user density is the lowest.
Cross-network transferable neural models for WLAN interference estimation
Airtime interference is a key performance indicator for WLANs, measuring, for a given time period, the percentage of time during which a node is forced to wait for other transmissions before to transmitting or receiving.
Neural combinatorial optimization beyond the TSP: Existing architectures under-represent graph structure
Recent years have witnessed the promise that reinforcement learning, coupled with Graph Neural Network (GNN) architectures, could learn to solve hard combinatorial optimization problems: given raw input data and an evaluator to guide the process, the idea is to automatically learn a policy able to return feasible and high-quality outputs.
A hybrid primal heuristic for Robust Multiperiod Network Design
Given the intrinsic difficulty of the problem, which proves challenging even for state-of-the art commercial solvers, we propose a hybrid primal heuristic based on the combination of ant colony optimization and an exact large neighborhood search.
A Fast Hybrid Primal Heuristic for Multiband Robust Capacitated Network Design with Multiple Time Periods
We investigate the Robust Multiperiod Network Design Problem, a generalization of the Capacitated Network Design Problem (CNDP) that, besides establishing flow routing and network capacity installation as in a canonical CNDP, also considers a planning horizon made up of multiple time periods and protection against fluctuations in traffic volumes.
