Machine Learning-Enhanced Tabu Search for Tactical Wireless Network Design
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The paper introduces TELGEN, a traffic engineering algorithm that uses graph neural networks to predict an optimal TE algorithm rather than a direct solution. TELGEN generalizes across diverse network topologies and traffic patterns, achieving less than a 3% optimality gap on networks up to 5,000 nodes and 3.6 million links, while reducing solving time by up to 84% and training time by up to 79.6% compared to existing methods.
arXiv:2606. 04328v1 Announce Type: cross Abstract: Future wireless networks demand rapid adaptation to highly heterogeneous environments and dynamic task configurations, necessitating a shift from conventional rule-based and optimization-driven radio resource management (RRM) toward artificial intelligence (AI)-driven RRM.
arXiv:2503. 03137v3 Announce Type: replace Abstract: Constructive neural combinatorial optimization (NCO) offers a promising paradigm for solving vehicle routing problems (VRPs) by directly learning to construct approximate optimal solutions, thereby reducing reliance on expert knowledge for algorithm design.
The paper introduces NICO, a learned 2‑opt local search policy for routing problems that improves upon initial neural solutions by repeatedly applying local moves. NICO addresses three shortcomings of prior policies—incumbent representation, input feature scaling, and move‑gain computation—by using edge‑based tokens without positional encodings, scale‑free features, and exact move gains added to logits. Experiments show that NICO generalizes to larger instances, achieving a 1.1% gap on 1000‑node Euclidean TSP and outperforming both greedy 2‑opt and previous learned policies, while also enhancing tours from constructive and diffusion‑based solvers on road‑network and CVRP benchmarks.
arXiv:2507. 19712v3 Announce Type: replace-cross Abstract: In this paper, we explore mission assignment and task offloading in an Open Radio Access Network (Open RAN)-based intelligent transportation system (ITS), where autonomous vehicles leverage mobile edge computing for efficient processing.
arXiv:2607. 14176v1 Announce Type: new Abstract: Reliable, low-latency uplink connectivity is a key requirement for C-V2X networks in dense urban environments, where fast channel variations and blockages often degrade direct vehicle-to-infrastructure links.