Scholarlink

There is a growing demand of users for high data rates in wireless communication networks upgrading to advanced techniques such as Device-to-Device (D2D). The spectrum-sharing sources produce interference in D2D communication in difficult, specifically in D2D model. To overcome this problem, Cumulative Reinforcement Learning (CRL) is proposed based on channel assignment in uplink wireless communication system. The CRL enables various agents to collaborate and share information which leads to effective solution space and enhances convergence to RA. By managing power and channel allocation, network easily optimizes throughput which ensures efficient data transmission in wireless communication. These constraints adopt various environmental conditions such as maintaining consistency and changing interference levels. The channel constraint allows available frequency spectrum effectively which minimizes the interference likelihood and enhances user quantity in a certain bandwidth. The CRL obtains accuracy of 99.56% for 500,000 number of D2D when compared to Autonomous Power Efficient Resource Allocation Algorithm (APERAA).

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Cumulative Reinforcement Learning-Based Channel Assignment in Uplink Wireless Communication System

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • S. Padmakala,
  • Rajiv Avacharmal,
  • Monika Pream,
  • M. Sowmya,
  • R. Rana Veer Samara Sihman Bharattej

摘要

There is a growing demand of users for high data rates in wireless communication networks upgrading to advanced techniques such as Device-to-Device (D2D). The spectrum-sharing sources produce interference in D2D communication in difficult, specifically in D2D model. To overcome this problem, Cumulative Reinforcement Learning (CRL) is proposed based on channel assignment in uplink wireless communication system. The CRL enables various agents to collaborate and share information which leads to effective solution space and enhances convergence to RA. By managing power and channel allocation, network easily optimizes throughput which ensures efficient data transmission in wireless communication. These constraints adopt various environmental conditions such as maintaining consistency and changing interference levels. The channel constraint allows available frequency spectrum effectively which minimizes the interference likelihood and enhances user quantity in a certain bandwidth. The CRL obtains accuracy of 99.56% for 500,000 number of D2D when compared to Autonomous Power Efficient Resource Allocation Algorithm (APERAA).