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Signal Processing for 5G: Algorithms and Implementations by Charlie Zhang, Fa-Long Luo

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Chapter 9Massive MIMO for 5G: Theory, Implementation and Prototyping

Ove Edfors, Liang Liu, Fredrik Tufvesson, Nikhil Kundargi and Karl Nieman

  1. 9.1 Introduction
  2. 9.2 Massive MIMO Theory
    1. 9.2.1 Downlink
    2. 9.2.2 Linear Precoding Schemes
    3. 9.2.3 Uplink
    4. 9.2.4 Linear Detection Schemes
    5. 9.2.5 Channel Estimation
  3. 9.3 Massive MIMO Channels
    1. 9.3.1 Existing Conventional MIMO Models
    2. 9.3.2 Necessary Model Extensions
    3. 9.3.3 A Massive MIMO Extension of the COST 2100 Channel Model
  4. 9.4 Massive MIMO Implementation
    1. 9.4.1 Antennas and Analog Front-ends
    2. 9.4.2 Baseband Processing
    3. 9.4.3 Prototyping
    4. 9.4.4 Deployment Scenarios
  5. 9.5 Testbed Design
    1. 9.5.1 Hierarchical Overview
    2. 9.5.2 Streaming IO Rates
    3. 9.5.3 Architecture and Functional Partitioning
    4. 9.5.4 Antenna Array
    5. 9.5.5 Mechanical Structure and Electrical Characteristics of LuMaMi Testbed
  6. 9.6 Synchronization
    1. 9.6.1 Types of Synchronization
  7. 9.7 Future Challenges and Conclusion
  8. Acknowledgments
  9. References

9.1 Introduction

Modern communication systems rely upon multiple antennas at the transmitter and/or receiver to enhance link performance. This class of techniques, known as multiple input, multiple output (MIMO), exploits the spatial dimension by employing spatial encoding and/or decoding.

Since their emergence in the mid-1990s and perhaps earlier, MIMO technologies have been successful in practice, leading to deployments in WiFi and cellular systems. More specifically, MIMO methods introduced in 802.11ac support up to 8 transmit and 8 receive antennas. Cellular ...

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