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<p>In high-bandwidth network environments, multiple users and devices frequently share similar high-frequency bands, but signal propagation limitations lead to unstable network links. These can affect the throughput of high-bandwidth wireless networks, reducing the continuity and efficiency of data transmission. Therefore, this paper constructs a high-bandwidth wireless network throughput performance enhancement model based on the Double Deep Q-Network algorithm (Double DQN). This method designs a mathematical model for high-bandwidth wireless networks, visually presents node connections using undirected graphs, and constructs a throughput performance enhancement model based on channel allocation and link scheduling by combining interference model analysis. In addition, this method introduces the Double DQN algorithm, integrates the reinforcement learning framework, and realizes dynamic adjustment of optimal channel allocation and link scheduling strategies through continuous interaction and learning between the agent and the environment. Experimental results confirm that this model significantly improves the throughput of high-bandwidth wireless networks to above 2500&#xa0;kb/s and reduces the call drop rate of voice and video network services to 0.01–0.02%, meeting the demand for high data continuous transmission.</p>

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Throughput Enhancement of High-Bandwidth Wireless Network Using Deep Double Q-Network Algorithm

  • 期刊论文

  • 发表日期：

    2025年4月16日

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  • Chengman Wang,
  • Abeer Aljohani,
  • Fazlullah Khan

摘要

In high-bandwidth network environments, multiple users and devices frequently share similar high-frequency bands, but signal propagation limitations lead to unstable network links. These can affect the throughput of high-bandwidth wireless networks, reducing the continuity and efficiency of data transmission. Therefore, this paper constructs a high-bandwidth wireless network throughput performance enhancement model based on the Double Deep Q-Network algorithm (Double DQN). This method designs a mathematical model for high-bandwidth wireless networks, visually presents node connections using undirected graphs, and constructs a throughput performance enhancement model based on channel allocation and link scheduling by combining interference model analysis. In addition, this method introduces the Double DQN algorithm, integrates the reinforcement learning framework, and realizes dynamic adjustment of optimal channel allocation and link scheduling strategies through continuous interaction and learning between the agent and the environment. Experimental results confirm that this model significantly improves the throughput of high-bandwidth wireless networks to above 2500 kb/s and reduces the call drop rate of voice and video network services to 0.01–0.02%, meeting the demand for high data continuous transmission.