Compressive spectrum sensing for 5G cognitive radio networks - LASSO approach

被引：5
|
作者：
Koteeshwari, R. S. [1 ,2 ]
Malarkodi, B. [1 ]
机构：
[1] Natl Inst Technol, Dept Elect & Commun Engn, Trichy 620015, India
[2] EGS Pillay Engn Coll, Dept Elect & Commun Engn, Nagapattinam 611002, India
来源：
HELIYON | 2022年 / 8卷 / 06期
关键词：
5G networks; Compressed sensing; Recovery algorithm; LASSO; SIGNAL RECOVERY; ALGORITHMS;
D O I：
10.1016/j.heliyon.2022.e09621
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
In recent years, the importance of Artificial Intelligence is inevitable for effective performance in communication area. The progressing in standards from beyond 5G networks are compatible gadgets for incorporate wireless communication. Cognitive radio (CR) is a sensible and advanced scientific communication that can effectively handle the radio spectrum applications. Spectrum sensing (SR) is the primary role in CR. In SR, various Wide Band techniques suited for 5G were investigated in this paper. (Least Absolute Shrinkage and Selection Operator) LASSO is the suitable choice for communication in compressive sensing and recovery in wideband 5G networks. The obtained results were correlated with recent report. Further, the relative merit and demerits are discussed significantly.
引用
下载
收藏
页数：5
相关论文
共 50 条
  • [1] A machine learning-based compressive spectrum sensing in 5G networks using cognitive radio networks
    Perumal, Ramakrishnan
    Nagarajan, Sathish Kumar
    INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS, 2022, 35 (16)
  • [2] Energy Efficient Cognitive Radio Spectrum Sensing for 5G Networks – A Survey
    Murugan S.
    Sumithra M.G.
    EAI Endorsed Transactions on Energy Web, 2021, 8 (36) : 1 - 9
  • [3] A Survey on Compressive Spectrum Sensing for Cognitive Radio Networks
    Benazzouza, Salma
    Ridouani, Mohammed
    Salahdine, Fatima
    Hayar, Aawatif
    2019 5TH IEEE INTERNATIONAL SMART CITIES CONFERENCE (IEEE ISC2 2019), 2019, : 535 - 541
  • [4] Dynamic Spectrum Sensing in Cognitive Radio Networks Using Compressive Sensing
    Dantu, Neeraj Kumar Reddy
    PROCEEDINGS OF NINTH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATION AND SENSOR NETWORKS (WCSN 2013), 2014, 299 : 89 - 100
  • [5] Cooperative Spectrum Sensing Deployment for Cognitive Radio Networks for Internet of Things 5G Wireless Communication
    Balachander, Thulasiraman
    Ramana, Kadiyala
    Mohana, Rasineni Madana
    Srivastava, Gautam
    Gadekallu, Thippa Reddy
    TSINGHUA SCIENCE AND TECHNOLOGY, 2024, 29 (03): : 698 - 720
  • [6] 5G Cognitive Radio Networks Using Reliable Hybrid Deep Learning Based on Spectrum Sensing
    Mohanakurup, Vinodkumar
    Baghela, Vishwadeepak Singh
    Kumar, Sarvesh
    Srivastava, Prabhat Kumar
    Doohan, Nitika Vats
    Soni, Mukesh
    Awal, Halifa
    WIRELESS COMMUNICATIONS & MOBILE COMPUTING, 2022, 2022
  • [7] COGNITIVE RADIO SPECTRUM SENSING FRAMEWORK BASED ON MULTI-AGENT ARCHITECTURE FOR 5G NETWORKS
    Zhang, Zhenjiang
    Zhang, Wenyu
    Zeadally, Sherali
    Wang, Yanan
    Liu, Yun
    IEEE WIRELESS COMMUNICATIONS, 2015, 22 (06) : 34 - 39
  • [8] Sparse Code Multiple Access based Cooperative Spectrum Sensing in 5G Cognitive Radio Networks
    Shekhawat, Guman Kanwar
    Yadav, R. P.
    PROCEEDINGS OF THE 2020 5TH INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION AND SECURITY (ICCCS-2020), 2020,
  • [9] Enhanced Cooperative Compressive Spectrum Sensing in Cognitive Radio Networks
    Benzater, Hadj Abdelkader
    Teguig, Djamal
    Lassami, Nacerredine
    Transactions on Emerging Telecommunications Technologies, 2024, 35 (11)
  • [10] Compressive spectrum sensing in centralized vehicular cognitive radio networks
    1600, Science and Engineering Research Support Society, 20 Virginia Court, Sandy Bay, Tasmania, Australia (06):
12345