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Integrating 5G with the Internet of Vehicles (IoV) leverages Multiple-Input Multiple-Output (MIMO) technology to boost communication efficiency. However, current IoV systems emphasize intelligent task processing, which requires understanding semantic information, not just data transmission. Traditional resource allocation methods often fall short in addressing this, limiting the potential of 5G-enabled IoV. To address this, we propose a semantic importance-based resource allocation method. Using image classification as a case study, we assess the impact of semantic features on task performance to enable dynamic compression, reducing data load while supporting MIMO transmission. Our method combines one-dimensional enumeration with a Deep Q-Network (DQN) algorithm for joint optimization of semantic compression and resource allocation, maximizing vehicle task success rates. Experimental results show our approach significantly improves task performance by \(32.6\%\) compared to semantic-based methods and by \(185.5\%\) over traditional methods.

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MIMO-Based Resource Allocation Algorithm Using Semantic Importance in 5G Intelligent Vehicular Networks

  • 会议论文

  • 发表日期：

    2025年

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  • Hongzhi Luan,
  • Xiaoman Cao,
  • Yi Li,
  • Chuanjie Qian,
  • Qiong Mei

摘要

Integrating 5G with the Internet of Vehicles (IoV) leverages Multiple-Input Multiple-Output (MIMO) technology to boost communication efficiency. However, current IoV systems emphasize intelligent task processing, which requires understanding semantic information, not just data transmission. Traditional resource allocation methods often fall short in addressing this, limiting the potential of 5G-enabled IoV. To address this, we propose a semantic importance-based resource allocation method. Using image classification as a case study, we assess the impact of semantic features on task performance to enable dynamic compression, reducing data load while supporting MIMO transmission. Our method combines one-dimensional enumeration with a Deep Q-Network (DQN) algorithm for joint optimization of semantic compression and resource allocation, maximizing vehicle task success rates. Experimental results show our approach significantly improves task performance by \(32.6\%\) compared to semantic-based methods and by \(185.5\%\) over traditional methods.