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Scheduling Considerations for Multi-User MIMO

Scheduling Considerations for Multi-User MIMO. Sae-Young Chung Wireless Communications Lab KAIST 05/19/2005. Overview. Introduction Multi-user MIMO Dirty paper coding Optimal schedulers Summary. Small-Scale Fading. Channel Knowledge. Assume perfect CSI at Tx and at Rx

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Scheduling Considerations for Multi-User MIMO

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  1. Scheduling Considerations for Multi-User MIMO Sae-Young Chung Wireless Communications Lab KAIST 05/19/2005 Wireless Communications Lab, KAIST

  2. Overview • Introduction • Multi-user MIMO • Dirty paper coding • Optimal schedulers • Summary Wireless Communications Lab, KAIST

  3. Small-Scale Fading Wireless Communications Lab, KAIST

  4. Channel Knowledge • Assume perfect CSI at Tx and at Rx • Requires CSI feedback from Rx to Tx • Is it a realistic assumption? • If packet duration << coherence time • E.g., 3km/h, 2GHz: ~ 30 msec • Packet duration: ~> 1 msec in 3G • If packet duration >> coherence time • Channel coding provides time diversity • CSI feedback consumes resource • Worse for MIMO • Penalty due to time delay • Estimation errors Wireless Communications Lab, KAIST

  5. Single-User MIMO • Capacity increases as at high SNR • Dimension-limited regime • # of spatial dimensions = w.p. 1 • Capacity increases as at low SNR • Uses only one spatial dimension, i.e., beamforming • Only the quality of the best spatial channel matters Wireless Communications Lab, KAIST

  6. Multi-User MIMO Broadcast Multiple access Wireless Communications Lab, KAIST

  7. The quick brown fox jumps over the lazy dog The quick brown fox jumps over the lazy dog The quick brown fox jumps over the lazy dog quick quick 키스의 고유 조건은 입술끼리 만나야 되고 특별한 요령은 필요치 않다 키스의 고유 조건은 입술끼리 만나야 되고 특별한 요령은 필요치 않다 키스의 고유 조건은 입술끼리 만나야 되고 특별한 요령은 필요치 않다 The The brown brown fox fox jumps jumps over over the the lazy lazy dog dog Dirty Paper Coding Wireless Communications Lab, KAIST

  8. Dirty Paper Coding • DPC: M. Costa ’83 • DPC achieves capacity of Gaussian MIMO broadcast channel • H. Weingarten, Y. Steinberg, S. Shamai ’04 • Practical schemes • Interference cancelling at the transmitter • Erez, Shamai, Zamir ’00 • But, complicated to implement • More practical schemes are yet to be discovered Wireless Communications Lab, KAIST

  9. Single Tx Antenna • Channels become degraded BC • DPC is equivalent to SIC • Sum capacity is achievable with TDM • Other boundary points are not achievable by TDM in general • E.g., rates achieved by PF scheduler Wireless Communications Lab, KAIST

  10. Scheduling Gain • Three sources of scheduling gain in wireless • Channel variation over time • Channel variation over frequency • Channel variation over space • Optimal scheduling • Allocates dimensions and power optimally across time, frequency and space • Peak or average power constraints • Constant power allocation or optimal power allocation over • Time, frequency, and space Wireless Communications Lab, KAIST

  11. Opportunistic Scheduling Wireless Communications Lab, KAIST

  12. Optimal Scheduler • Scheduler maximizes the following for each channel state • It maximizes • Therefore the following is on the boundary of the capacity region Wireless Communications Lab, KAIST

  13. PF Scheduler • achieves PF since for all T implies for all T • Therefore, PF scheduler should maximize for each channel state, where is the measured throughput of user k • This generalizes Qualcomm’s PF scheduler • H. Viswanathan, S. Venkatesan, H. Huang ’03 • Equivalent to max sum-rate scheduler if channel statistics are the same for all users • DPC and PF scheduling can be combined Wireless Communications Lab, KAIST

  14. Other Schedulers • Maximize sum-rate: • Fair (equal throughput): • Same as max min • Max harmonic mean throughput • Circuit capacity PF scheduler Sum-rate Fair scheduler Wireless Communications Lab, KAIST

  15. Calculation of DPC Capacity • Convert to a convex optimization by using duality between BC and MAC • S. Viswanath, N. Jindal, A. Goldsmith ’02 Wireless Communications Lab, KAIST

  16. DPC Capacity Example • 4x1 (solid) or 4x2 (dashed) MIMO • 10 users • Simultaneously scheduled users: 1, 2, 3, or 4 (from bottom to up) • Plots sum capacity, i.e., scheduler maximizes sum throughput Wireless Communications Lab, KAIST

  17. Asymptotic Behavior • Low SNR • Power limited, dimension irrelevant • Picking one user is enough (i.e., TDM) • Pick the best eigen mode for the chosen user • High SNR • Dimension limited, power irrelevant • Number of dimensions: • Number of users: • Maximum number of users scheduled simultaneously: • Picking users is enough • High # of users Wireless Communications Lab, KAIST

  18. Current Research Areas at WCL • Practical multi-user MIMO schemes • Beamforming • Combined with LDPC codes • Iterative decoding techniques • Limited CSI feedback • Cross layer optimization • Scheduler design • OFDM • Network information theory • Relay channels • Interference channels • Ad-hoc networks Wireless Communications Lab, KAIST

  19. Summary • MIMO can increase capacity • Multi-user MIMO can increase capacity further • Good practical schemes are desirable • Optimal scheduling for multi-user MIMO • Many research problems Wireless Communications Lab, KAIST

  20. Thank You Wireless Communications Lab, KAIST

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