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Distributed MIMO

Distributed MIMO. Patrick Maechler April 2, 2008. Outline. Motivation: Collaboration scheme achieving optimal capacity scaling Distributed MIMO Synchronization errors Implementation Conclusion/Outlook. Throughput Scaling. Scenario: Dense network

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Distributed MIMO

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  1. Distributed MIMO Patrick Maechler April 2, 2008

  2. Outline • Motivation: Collaboration scheme achieving optimal capacity scaling • Distributed MIMO • Synchronization errors • Implementation • Conclusion/Outlook

  3. Throughput Scaling • Scenario: Dense network • Fixed area with n randomly distributed nodes • Each node communicates with random destination node at rate R(n). Total throughput T(n) = nR(n) • TDMA/FDMA/CDMA: T(n) = O(1) • Multi-hop: T(n) = O( ) • P. Gupta and P. R. Kumar, “The capacity of wireless networks,” IEEE Trans. Inf. Theory, vol. 42, no. 2, pp. 388–404, Mar. 2000. • Hierarchical Cooperation: T(n) = O(n) • Ayfer Özgür, Olivier Lévêque and David N. C. Tse, ”Hierarchical Cooperation Achieves Optimal Capacity Scaling in Ad Hoc Networks”, IEEE Trans. Inf. Theory, vol. 53, no. 10, pp. 3549-3572, Oct. 2007

  4. Cooperation Scheme • All nodes are divided into clusters of equal size • Phase 1: Information distribution • Each node splits its bits among all nodes in its cluster

  5. Cooperation Scheme • Phase 2: Distributed MIMO transmissions • All bits from source s to destination d are sent simultaneously by all nodes in the cluster of the source node s

  6. Cooperation Scheme • Phase 3: Cooperative decoding • The received signal in all nodes of the destination cluster is quantized and transmitted to destination d. • Node d performs MIMO decoding.

  7. Hierarchical Cooperation • The more hierarchical levels of this scheme are applied, the nearer one can get to a troughput linear in n.

  8. Outline • Motivation: Collaboration scheme achieving optimal capacity scaling • Distributed MIMO • Synchronization errors • Implementation • Conclusion/Outlook

  9. Distributed MIMO • Independent nodes collaborate to operate as distributed multiple-input multiple-output system • Simple examples: • Receive MRC (1xNr): • Transmit MRC (Ntx1, channel knowledge at transmitter) • Alamouti (2xNr): STBC over 2 timeslots • Diversity gain but no multiplexing gain Alamouti, S.M., "A simple transmit diversity technique for wireless communications ," Selected Areas in Communications, IEEE Journal on , vol.16, no.8, pp.1451-1458, Oct 1998

  10. MIMO Schemes • Schemes providing multiplexing gain: • V-BLAST: Independent stream over each antenna • D-BLAST: Coding across antennas gives outage optimality (higher receiver complexity) [1] P. W. Wolniansky, G. J. Foschini, G. D. Golden, and R. A. Valenzuela. V-BLAST: An architecture for realizing very high data rates over the rich scattering wireless channel. In ISSSE International Symposium on Signals, Systems, and Electronics, pages 295-300, Sept. 1998. [2] G. Foschini. Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas. Bell Labs Technical Journal, 1(2):41-59, 1996.

  11. MIMO Decoders • Maximum likelihood: • Zero Forcing / Decorrelator • MMSE • Balances noise and multi stream interference (MSI) • Successive interference cancelation (SIC)

  12. Error Rate Comparison • MMSE-SIC is the best linear receiver • ML receiver is optimal

  13. Outline • Motivation: Collaboration scheme achieving optimal capacity scaling • Distributed MIMO • Synchronization errors • Implementation • Conclusion/Outlook

  14. Synchronization • Each transmit node has its own clock and a different propagation delay to destination • No perfect synchronization possible. Shifted peaks at receiver • What is the resulting error, if any?

  15. Simulation results • Flat fading channel assumed at receiver • No large BER degradiation for timing errors up to 20% of symbol duration (raised cosine with )

  16. Frequency-selectivity • Synchronization errors make flat channels appear as frequency-selective channels • Receivers for freq.-sel. channels can perfectly compensate synchronization errors • Implementation cost is much higher!

  17. Time Shift - SIC • Promising results for SIC receiver that samples each stream at the optimal point • Compensation of synchronization errors possible for independent streams (V-BLAST)

  18. Outline • Motivation: Collaboration scheme achieving optimal capacity scaling • Distributed MIMO • Synchronization errors • Implementation • Conclusion/Outlook

  19. Implementation • Goal: Show feasibility of distributed MIMO Systems using BEE2 boards • Focus on synchronization algorithms at receiver • Timing synchronization • Frequency synchronization • Channel estimation • Complex decoders requiredAll linear decoders need matrix inversion

  20. Implementation • BEE2 implementation of 2x1 Alamouti (MISO) scheme currently under development

  21. Outline • Motivation: Collaboration scheme achieving optimal capacity scaling • Distributed MIMO • Synchronization errors • Implementation • Conclusion/Outlook

  22. Conclusion/Outlook • Standard flat-channel MIMO decoders useable for synchronization errors up to 20% of symbol duration • More complex decoders can compensate different delays also for higher errors Outlook: • BEE2 implementation of MIMO receiver • Frequency synchronization methods • Measure achievable BER on real system for given synchronization accuracy at transmitters

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