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Signal Processing for 5G: Algorithms and Implementations
book

Signal Processing for 5G: Algorithms and Implementations

by Fa-Long Luo, Charlie Zhang
October 2016
Intermediate to advanced
610 pages
21h 37m
English
Wiley-IEEE Press
Content preview from Signal Processing for 5G: Algorithms and Implementations

Chapter 4Filter Bank Multicarrier for Massive MIMO

Arman Farhang, Nicola Marchetti and Behrouz Farhang-Boroujeny

  1. 4.1 System Model and FBMC Formulation in Massive MIMO
    1. 4.1.1 Polyphase-based CMT in Massive MIMO
    2. 4.1.2 FS-based CMT in Massive MIMO
  2. 4.2 Self-equalization Property of FBMC in Massive MIMO
    1. 4.2.1 Numerical Study of Polyphase-based CMT in a Massive MIMO Channel
    2. 4.2.2 Numerical Study of FS-based CMT in a Massive MIMO Channel
  3. 4.3 Comparison with OFDM
  4. 4.4 Blind Equalization and Pilot Decontamination
    1. 4.4.1 Simulation Results
  5. 4.5 Conclusion
  6. References

The recent proposal of massive MIMO as a candidate for the fifth generation of wireless communication networks (5G) has sparked a great deal of interest among researchers. This is due to the fact that such technology can greatly increase the capacity of multiuser networks. Massive MIMO is a multiuser technique with concepts somewhat similar to spread-spectrum systems. The spreading gains of different users come from the corresponding channel gains between each mobile terminal (MT) antenna and the base station (BS) antennas. Therefore, a significant processing gain can be achieved by utilizing a massive number of antenna elements at the BS. In other words, the processing gain can arbitrarily grow by increasing the number of antenna elements at the BS. As the pioneering work of Marzetta shows, given perfect channel state information (CSI), as the number of BS antennas, in the limit, tends to infinity, the processing gain of the ...

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Publisher Resources

ISBN: 9781119116462Purchase book