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Found 6 papers, 1 papers with code
Are LLMs Effective Backbones for Fine-tuning? An Experimental Investigation of Supervised LLMs on Chinese Short Text Matching
The recent success of Large Language Models (LLMs) has garnered significant attention in both academia and industry.
UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control
Traffic congestion is a persistent problem in urban areas, which calls for the development of effective traffic signal control (TSC) systems.
ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement Learning
In this paper, we propose a novel reinforcement learning approach with augmented data (ADLight) to train a universal model for intersections with different structures.
Rate-Splitting Multiple Access for Multi-Antenna Joint Radar and Communications
In this work, we propose a powerful and unified multi-antenna DFRC transmission framework, where an additional radar sequence is transmitted apart from communication streams to enhance radar beampattern matching capability, and Rate-Splitting Multiple Access (RSMA) is adopted to better manage the interference.
Multi-Antenna Joint Radar and Communications: Precoder Optimization and Weighted Sum-Rate vs Probing Power Tradeoff
The tradeoffs between WSR and probing power at target are compared among both proposed transmissions and two practically simpler dual-function implementations i. e., time division and frequency division.
Signal Shaping for Non-Uniform Beamspace Modulated mmWave Hybrid MIMO Communications
Specifically, we firstly propose a joint optimization based signal shaping (JOSS) approach, in which the symbol vector sets used for all analog precoder activation states are jointly optimized by solving a series of quadratically constrained quadratic programming (QCQP) problems.
