Capacity Improvement for VoIP based Two-Tier CRN Using Space-Time Spectrum Sensing

被引:0
|
作者
Kumar, Vineet [1 ]
Trivedi, Aditya [1 ]
机构
[1] ABV Indian Inst Informat Technol & Management, Gwalior 474015, India
关键词
Cognitive radio network(CRN); Space time spectrum sensing; Spectrum Utilization; Three regions; Voice over IP (VoIP); Spectrum Sensing; COGNITIVE RADIO NETWORKS; SYSTEM;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Cognitive Radio Network (CRN) provides a way for the improvement in the capacity and spectrum utilization with the use of idle spectrum band of licensed users (PUs). Currently, CRN has come to its upper bound limit which may restricts the progress in the area of CRN. This paper tries to take care of this issue by further exploring the idea of 'Two Tier CRN (TT-CRN)' by using space-time spectrum sensing (STSS). In TT-CRN, the secondary users (SUs) of tier1 transmit their data based on Voice over IP (VoIP) communication. When these users are not transmitting the data (silence suppression), then SUs of tier2 transmit in that spectrum band. The TT-CRN increases the capacity and throughput of the network and also improves the spectrum utilization up to some extent. The space-time spectrum sensing based two tier CRN (STSS TT-CRN) mechanism further increases the utilization and the capacity of the existing TT-CRN. In this mechanism, different regions are divided into the tiers of the SUs and PUs. These regions are region 1 (REG1), region 2 (REG2), and region 3 (REG3). With the help of analytical results, the throughputs of basic CRN, TT-CRN, and STSS TT-CRN are compared. The performance evaluation confirms the improvement in the link utilization and results in higher throughput of STSS TT-CRN.
引用
收藏
页码:153 / 158
页数:6
相关论文
共 50 条
  • [21] Intelligent Dynamic Spectrum Resource Management Based on Sensing Data in Space-Time and Frequency Domain
    Yun, Deok-Won
    Lee, Won-Cheol
    [J]. SENSORS, 2021, 21 (16)
  • [22] User Classifying-based Hybrid Spectrum Allocation in Two-tier OFDMA Femtocell Networks
    Li, Sainan
    Xia, Hailun
    Zeng, Zhimin
    Huang, Zhenglei
    Wu, Hao
    [J]. 2012 IEEE VEHICULAR TECHNOLOGY CONFERENCE (VTC FALL), 2012,
  • [23] Exploring the spectrum of QCD using a space-time lattice
    Morningstar, C
    [J]. INTERNATIONAL JOURNAL OF MODERN PHYSICS A, 2006, 21 (04): : 843 - 846
  • [24] Linear predictive spectrum estimation algorithm based on space-time two-dimensional
    Zhang, Ze
    Chen, Hui
    Wang, Yongliang
    [J]. Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics, 2019, 41 (09): : 1937 - 1944
  • [25] Time Reversal-based Transmissions with Distributed Power Allocation for Two-Tier Networks
    Vu Tran-Ha
    Quang-Doanh Vu
    Een-Kee Hong
    [J]. 2015 IEEE 29TH INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS WORKSHOPS WAINA 2015, 2015, : 181 - 186
  • [26] Two-Tier PSO Based Data Routing Employing Bayesian Compressive Sensing in Underwater Sensor Networks
    Chen, Xuechen
    Xiong, Wenjun
    Chu, Sheng
    [J]. SENSORS, 2020, 20 (20) : 1 - 22
  • [27] Spectrum Sensing for Cognitive Radios in Space-Time Doubly Selective Fading Channels
    Li, Bin
    Hou, Jia
    Zhao, Chenglin
    [J]. 2015 INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS & SIGNAL PROCESSING (WCSP), 2015,
  • [28] Three Regions for Space-Time Spectrum Sensing and Access in Cognitive Radio Networks
    Wei, Zhiqing
    Feng, Zhiyong
    Zhang, Qixun
    Li, Wei
    [J]. 2012 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2012, : 1283 - 1288
  • [29] Three Regions for Space-Time Spectrum Sensing and Access in Cognitive Radio Networks
    Wei, Zhiqing
    Feng, Zhiyong
    Zhang, Qixun
    Li, Wei
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2015, 64 (06) : 2448 - 2462
  • [30] A CSGC Algorithm Based Partial Spectrum Sharing Scheme in Cognitive LTE-A Two-tier Heterogeneous Networks
    Liu, Wen-Ji
    Hu, Bin-Jie
    Wei, Zong-Heng
    Lv, Ji-Man
    Tan, Tuo
    Liu, Xing
    [J]. 2016 IEEE MTT-S INTERNATIONAL WIRELESS SYMPOSIUM (IWS), 2016,