An Intelligent Method for Resource Management in Wireless Networks

被引:0
|
作者
Hosseini, Seyed Mostafa [1 ]
Mozayeni, Naser [2 ]
机构
[1] Islamic Azad Univ, Sci & Res Branch, Dept Artifitial Intelligence, Tehran, Iran
[2] Iran Univ Sci & Technol, Dept Artifitial Intelligence, Tehran, Iran
关键词
Cellular Wireless Network; Multi-Agent System; Call Allocation; Mobility Management; Handover; CHANNEL ALLOCATION; CELLULAR NETWORKS;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In wireless cellular network, resource constraint has become a critical and important issue. Users always and everywhere expect telecommunication systems with the best quality. They also need visual and multimedia communication. So, an intelligent wireless network that has the ability to adapt to environment in different network traffics is needed. The intelligent network has the capability to decide and modify itself. One of these intelligent methods is the use of multi-agent system. The main criteria considered in resource management of cellular networks are rate of dropped calls and blocked calls. Blocked calls include new calls and dropped calls include calls which are made by transition a mobile from a cell to another cell. In this project, we take a look at former techniques and then we will propose solutions to reduce the two mentioned criteria based on intelligent agents. The main purposes of this article are: reducing dropped calls, reducing blocked calls and decreasing traffic load variance between several cells and balancing among them. The results show that implementing multi-agnet system concerning intelligence and using agnets in proposed method has noticeable improvement than other methods in decreasing blocked calls and dropped calls.
引用
收藏
页码:371 / 376
页数:6
相关论文
共 50 条
  • [1] Resource management in wireless networks using intelligent agents
    Al Agha, Khaldoun
    International Journal of Network Management, 2000, 10 (01) : 29 - 39
  • [2] INTELLIGENT RESOURCE MANAGEMENT SCHEMES FOR HETEROGENEOUS WIRELESS NETWORKS
    Huang, Chenn-Jung
    Li, Ching-Yu
    Chen, Yu-To
    Lee, Che-Yu
    Liao, Jia-Jian
    Chang, Chi-Hsuan
    Chen, I-Fan
    Hu, Kai-Wen
    Chen, Hong-Xin
    Chen, You-Jia
    INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL, 2011, 7 (08): : 4765 - 4777
  • [3] Wireless Resource Management in Intelligent Semantic Communication Networks
    Xia, Le
    Sun, Yao
    Li, Xiaoqian
    Feng, Gang
    Imran, Muhammad Ali
    IEEE INFOCOM 2022 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS), 2022,
  • [4] Reflection Resource Management for Intelligent Reflecting Surface Aided Wireless Networks
    Gao, Yulan
    Yong, Chao
    Xiong, Zehui
    Zhao, Jun
    Xiao, Yue
    Niyato, Dusit
    IEEE TRANSACTIONS ON COMMUNICATIONS, 2021, 69 (10) : 6971 - 6986
  • [5] Service-Oriented Wireless Virtualized Networks: An Intelligent Resource Management Approach
    Hu, Yun
    Chang, Zheng
    Chen, Yanhui
    Han, Zhu
    IEEE VEHICULAR TECHNOLOGY MAGAZINE, 2022, 17 (01): : 57 - 65
  • [6] Intelligent Resource Management for eMBB and URLLC in 5G and Beyond Wireless Networks
    Sohaib, Rana M.
    Onireti, Oluwakayode
    Sambo, Yusuf
    Swash, Rafiq
    Ansari, Shuja
    Imran, Muhammad A.
    IEEE ACCESS, 2023, 11 : 65205 - 65221
  • [7] Intelligent resource allocation in wireless networks: Predictive models for efficient access point management
    Frank, Lucas R.
    Galletta, Antonino
    Carnevale, Lorenzo
    Vieira, Alex B.
    Silva, Edelberto Franco
    COMPUTER NETWORKS, 2024, 254
  • [8] Efficient resource management method for IMS services in hybrid wireless networks
    Kim, Jong-deug
    Jeon, Taehyun
    International Journal of Control and Automation, 2013, 6 (03): : 313 - 320
  • [9] Radio Resource Management for Wireless Networks
    Neznik, Dominik
    Dobos, Lubomir
    Papaj, Jan
    2019 29TH INTERNATIONAL CONFERENCE RADIOELEKTRONIKA (RADIOELEKTRONIKA), 2019, : 317 - 322
  • [10] Intelligent Resource Allocation Method for Wireless Communication Networks Based on Deep Learning Techniques
    Hui, Hancheng
    JOURNAL OF SENSORS, 2021, 2021