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Enhancing Wireless Data Rates with Distributed MIMO Systems

Explore the potential of distributed MIMO systems in boosting data rates over wireless networks. Learn about multiuser MIMO, synchronization challenges, and experimental results. Discover how spectrum reuse and advanced precoding schemes can significantly increase bandwidth. Gain insights into achieving phase synchronization and maximizing system performance. Dive into the future possibilities and challenges of distributed MIMO technology in improving wireless communication. Questions? Let's discuss further.

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Enhancing Wireless Data Rates with Distributed MIMO Systems

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  1. Achieving High Data Rates in a Distributed MIMO System • Horia Vlad Balan Ryan Rogalin • Antonios Michaloliakos Konstantinos Psounis • Giuseppe Caire • USC

  2. Structure of this talk • Motivation • Multiuser MIMO and precoding schemes • Distributed MIMO and synchronization • Experimental results

  3. Motivation • Cellular companies spend billions for more bandwidth • Spectrum reuse is the most promising way to increase wireless transfer rates and distributed MIMO is its ideal implementation • In WiFi networks, with a high number of users, spectrum reuse becomes equally important [Webb - The Future of Wireless Communication]

  4. Enterprise WiFi

  5. Multiuser MIMO

  6. Shannon’s Theory

  7. Increasing the Rate Prelog Factor Increase your bandwidth! Inlog Factor Increase your power exponentially!!!

  8. MIMO Communication interference

  9. Separate the Channels limited interference Dirty Paper Coding provides the achievable rate region

  10. -1 Zero-Forcing

  11. -1 L L U -1 L U U Tomlinson-Harashima Precoding

  12. 4 +2 +2 -5 -4 -3 +3 -2 -2 -1 +4 0 1 2 3 4 14 (mod 5) = 4 5 6 1 2 7 8 9 2 1 1 -9 (mod 5) = 1 Tomlinson-Harashima 3 3 -1 Modulo Compensation

  13. -1 -1 ) mod ( mod ( ) mod mod ( ) mod mod -1 -1 L L U U U U Tomlinson-Harashima Precoding

  14. + + + 72 -1 + + + + + + + + + + + Blind Interference Alignment 3 slots, 4 symbols => 4/3 DoFs

  15. Distributed MIMO

  16. Challenges • Maintaining phase synchronization between the different APs • Gathering channel state information and transmitting before the channel coherence time ends

  17. Subcarriers OFDM Symbol Carrier Cyclic Prefix OFDM Modulation

  18. FFT IFFT OFDM Demodulation

  19. Symbol Alignment FFT Phase Alignment Distributed OFDM TX 1 TX 2 RX

  20. Random Phase Carrier Frequency Offset Timing Offset Distributed OFDM TX 1 TX 2

  21. Phase Alignment

  22. option 1 option 2: coherence time depends on the electronics Phase Alignment What should be the effective channel matrix?

  23. option 1 Phase Alignment What should be the effective channel matrix?

  24. Data User Achieving Phase Synchronization Pilot Signal Master Secondaries

  25. TDMA point-to-point Distributed MIMO Testbed Pilot Signal Master Secondaries Data Clients (4x4 MIMO)

  26. Phase Accuracy Channel Orthogonalization Results ZFBF (2x2 MIMO)

  27. Results Tomlinson Harashima 85% rate increase (85% of the theoretical gain) (2x2 MIMO)

  28. Results Tomlinson Harashima 165% rate increase (55% of the theoretical gain) (4x4 MIMO)

  29. Results Blind Interference Alignment 22% rate increase (66% of the theoretical gain)

  30. MAC Layer Results • Comparing scheduling strategies through simulation in a 4 AP, 8 users scenario • Greedy Zero-Forcing, Tomlinson-Harashima precoding, Blind Interference Alignment • Using TDMA as a reference point

  31. Results 4x4 achievable rates (simulation)

  32. Future Work • improving the accuracy of our estimators • combining distributed MIMO with incremental redundancy schemes • characterize the channel quality variations of BIA in large deployments

  33. Questions?

  34. Thank you!

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