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Filtered-MultiCarrier Modulation Techniques for Vehicle-to-Vehicle Communication

EasyChair Preprint no. 5069

10 pagesDate: March 1, 2021


The high demand of wireless applications is increased day after day because of the increasing use of mobile devices, Internet-of-Things (IoT), machine types, and wireless nodes in Wireless Sensors Networks (WSN). This increasing demand makes the consideration of using advanced technology to enhance the network performance especially in Fifth Generation (5G) applications such as Vehicular communication that is called Vehicle-to-Everything (V2x) communication where the Vehicle-to-Vehicle (V2V) is one of its types.. The use of multicarrier waveforms to overcome this problem is essential such as using Orthogonal Frequency Division Multiplexing (OFDM). The problems appear from using OFDM are the high Out-of-Band (OOB) and the high Peak to Average Power Ratio (PAPR). This leads to the attention of using other types of multicarrier waveforms to enhance the OFDM responses. In this paper, the use of four different types of Multi-Carrier Modulation (MCM) is used which are Filter Bank Multi-Carrier (FBMC), Generalized Frequency Division Multiplexing (GFDM), Universal Filtered Multi-Carrier (UFMC) and Filtered OFDM (F-OFDM). These types of MCM based on the filtering process of symbols and subcarriers to eliminate the Cyclic Prefix (CP) effect on the OFDM performance in terms of Bit Error Rate (BER) and throughput. The simulation results show that the BER performance of the FBMC is better than any other simulated MCM in this paper while the throughput of the GFDM is enhanced at high SNR and the UFMC throughput is the better at low SNR values.


BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Nather Abdulhakeem Hamid and Ahmed Thair Al-Heety and Abbas Alaa Hammoodi Alsabbagh and Sefer Kurnaz},
  title = {Filtered-MultiCarrier Modulation Techniques for Vehicle-to-Vehicle Communication},
  howpublished = {EasyChair Preprint no. 5069},

  year = {EasyChair, 2021}}
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