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Scheduled Spatial Reuse with Collaborative Beamforming

Scheduled Spatial Reuse with Collaborative Beamforming. Date: 2010-05-16. Authors:. Abstract. Spatial reuse is a key aim for 802.11ad [1] potentially a major increase in aggregate data throughput within a PBSS mutual interference mitigated by directional antennas

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Scheduled Spatial Reuse with Collaborative Beamforming

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  1. Scheduled Spatial Reuse with Collaborative Beamforming Date: 2010-05-16 Authors: Thomas Derham, Orange Labs

  2. Abstract • Spatial reuse is a key aim for 802.11ad [1] • potentially a major increase in aggregate data throughput within a PBSS • mutual interference mitigated by directional antennas • Beamforming capability of phased-array antennas can be usedto our advantage to optimally mitigate interference • Antenna Weight Vectors (AWVs) can be determined that are optimizedtaking into account mutual interference due to spatial reuse • based on simple extension of Beam Refinement Procedure (BRP) [2] • resulting SINR of each link can be accurately predicted and used by PCP asthe basis for co-scheduling (in addition to Directional Channel Quality reports) • Substantial increase in throughput; low complexity and impact Thomas Derham, Orange Labs

  3. A typical 802.11ad usage scenario • in dense networks, multiple links must share the same channel • but one uncompressed HD video link can exhaust all bandwidth in one channel • video links tend to be continuously active over extended periods • spatial reuse necessary to support all links (e.g. video + data) [3] repeater Media server FTTH BSS Set-top box Home Gateway Thomas Derham, Orange Labs

  4. Scheduled spatial reuse • beacon interval (BI) beacon service periods (SPs) contention-based period (CBP) CSMA/CA TDMA • high-QoS data transmissions are generally scheduled in SPs • since CSMA/CA is inefficient with directional antennas [4] • PCP (PBSS Control Point) schedules SPs and transmits the beacon [5] • spatial reuse: PCP may co-schedule overlapping SPs (e.g. 2x spatial reuse) STA1 => STA2 STA3 => STA4 STA3 STA1 STA2 STA4 Thomas Derham, Orange Labs

  5. Example of co-scheduling process • STA3 requests SPs for link STA3=>STA4 by sending “Extended mmWave TSPEC” to PCP • If no free time in BI, PCP sends “Spatial Reuse BRP Request” to the STAs ofa subset of links (STA1=>STA2, STA3=>STA4) that it is considering to co-schedule • STAs perform BRPs with other STAs in the subset to determine optimal AWVs • Receiver STAs (STA2, STA4) send “Spatial Reuse SINR Report” to PCP • PCP determines scheduling (all/part of subset), broadcasts “Extended Schedule” STA1 => STA2 < x STA1 => STA2 Extended mmWave TSPEC (Src STA = 3), Dest STA = 4, Length = x Spatial Reuse BRP Request Co-scheduled links: STA1=>STA2, STA3=>STA4 STA3 => STA4 Spatial Reuse SINR Report SINR1,2, SINR3,4 STA3 STA1 STA2 PCP STA4 Extended Schedule (1) Src STA = 1, Dest STA = 2, Start = 0, Length=x’ (2) Src STA = 3, Dest STA = 4, Start = 0, Length=x Thomas Derham, Orange Labs

  6. PCP determines a subset of links that it is considering to co-schedule • based on two metrics that approximately indicate spatial separation • metrics already known by PCP, so no additional overhead • large receiver spatial separation => greater chance links can be co-scheduled range separation is channel strength forPCP<=>Rx STA of ith link angular separation is beamforming vector used by PCP for PCP<=>Rx STA of ith link PCP Thomas Derham, Orange Labs

  7. Each Tx STA performs BRP with all Rx STAs in the subset to determine optimal Tx AWV • Tx STA initiates transmit beam refinement with each Rx STA in turn • i.e. Tx STA1 performs transmit BRP for both its own link (STA1=>STA2)and the “cross-link” it may interfere with (STA1=>STA4) • Rx STA shall fix its beam to the best known AWV for its own link • all links have already done conventional beam training • i.e. Rx STA4 fixes its beam to AWV chosen in previous SLS/BRP with STA3 • Tx STA cycles through an orthogonal codebook matrix of AWVs • CSI “Channel Measurement”/“Tap Delay” fed back for each AWV in codebook link cross-link STA3 STA1 Rx beam fixed to best AWV for STA3=>STA4 link STA2 TRN-T transmitted M times(number of Tx elements) STA4 Thomas Derham, Orange Labs

  8. Each Tx STA performs BRP with all Rx STAs in the subset to determine optimal Tx AWV (2) • Tx STA estimates effective MISO channel • e.g. MIMO channel model for subcarrier i: • transmit-side spatial covariance matrix of MISO channel given by: • Tx STA calculates optimal AWV using max-SLNR criterion • Signal to Leakage & Noise Ratio: “leakage” is interference caused to other Rx MIMO channel fixed Rx AWV codebook of Tx AWVs channel estimates for Tx AWVs (eig{X} is dominant eigenvector of X) “cross-link” to qth Rx STA own link Thomas Derham, Orange Labs

  9. Each Rx STA performs BRP with all Tx STAs in the subset to determine optimal Rx AWV • Rx STA initiates receive beam refinement with each Tx STA in turn • i.e. Rx STA2 performs receive BRP for both its own link (STA1=>STA2)and the “cross-link” that may cause it interference (STA3=>STA2) • Tx STA shall fix its beam to the best known AWV for its own link • determined in previous stage • i.e. Tx STA3 fixes its beam to AWV chosen for use with STA4 • Rx STA cycles through an orthogonal codebook matrix of AWVs cross-link link TRN-R transmitted N times(number of Rx elements) STA3 STA1 Tx beam fixed to best AWV for STA3=>STA4 link STA2 STA4 Thomas Derham, Orange Labs

  10. Each Rx STA performs BRP with all Tx STAs in the subset to determine optimal Rx AWV (2) • Rx STA estimates effective SIMO channel • e.g. MIMO channel model for subcarrier i: • receive-side spatial covariance matrix of SIMO channel given by: • Rx STA calculates optimal AWV using max-SINR criterion • Signal to Interference & Noise Ratio: “interference” is caused by other Tx MIMO channel codebook of Rx AWVs fixed Tx AWV channel estimates for Rx AWVs (eig{X} finds dominant eigenvector of X) “cross-links” from pth Tx STA own link Thomas Derham, Orange Labs

  11. Each Rx STA sends SINR report to PCPand PCP determines scheduling • Rx STA calculates SINR assuming subset is co-scheduled • PCP schedules links based on these SINRs • links are co-scheduled (overlapping SPs) if all SINR are above a threshold • threshold may be chosen according to QoS requirement for that link • if one or more SINRs are too low, two choices: • (a) remove dominant interfering pair (lowest SLNR) and retry, or • (b) perform iteration of Tx-side and Rx-side BRP and AWV calculation • since optimal Rx AWV are conditional on Tx AWV, and vice versa own link “cross-links” from pth Tx STA Thomas Derham, Orange Labs

  12. System-level simulation setup • conference room model • inter-cluster parameters between all pairs of STAs from ray-tracing [7] • correctly models interference between all STAs • TGad channel model code used for intra-cluster parameters [8] Link-n Thomas Derham, Orange Labs

  13. STA positions as per Evaluation Methodology • 3 STA-STA pairs on table [9]: STA2=>1 (LoS), STA3=>5 (NLoS), STA7=>8 (LoS) • STA-AP links ignored; STA5=>STA3 ignored since direct leakage with STA3=>5 not modeled • beamforming: (a) conventional training, (b) collaborative beamforming SINR threshold: 4 dB complementary CDF of aggregate throughput complementary CDF of number of co-scheduled links aggregate throughput increased by 40% @Pr=0.5 no. of co-scheduledlinks increased in approx. 80% ofchannel instances Thomas Derham, Orange Labs

  14. STA positions randomized on table • 10 pairs of STAs on 2.5 x 1 m table (random orientation) ==> dense network • note: not all pairs are simultaneously active (due to scheduler SINR rule) • beamforming types: (a) conventional training, (b) collaborative beamforming SINR threshold: 4 dB complementary CDF of aggregate throughput complementary CDF of number of co-scheduled links aggregate throughput increased by 70% @Pr=0.5 Thomas Derham, Orange Labs

  15. STA positions randomized on table • 10 pairs of STAs on 2.5 x 1 m table (random orientation) ==> dense network • note: not all pairs are simultaneously active (due to scheduler SINR rule) • beamforming types: (a) conventional training, (b) collaborative beamforming SINR threshold: 9.5 dB complementary CDF of aggregate throughput complementary CDF of number of co-scheduled links aggregate throughput increased by 60% @Pr=0.5 Thomas Derham, Orange Labs

  16. Supporting scheduled spatial reuse with collaborative beamforming in 802.11ad • provide basic framework to allow implementation • (1) a field in BRP request which tells the responder STA to fix its AWV to the best known beam for communicating with a specified STA • (2) a supporting STA should maintain a table of best known AWVs for communicating with each STA when specified other links are co-scheduled • e.g. transmitter AWVs receiver AWVs Thomas Derham, Orange Labs

  17. Supporting scheduled spatial reuse with collaborative beamforming in 802.11ad (2) • to allow control by PCP • (1) support “Spatial Reuse BRP Request” for both Tx and Rx sides • from PCP to Tx/Rx STAs • (2) support “Spatial Reuse SINR Report” • from Rx STAs to PCP • complementary to existing beamforming (SLS/BRP) and measurement reports • predicted SINR reports are more accurate than Directional Channel Quality • not affected by bursty traffic • based on the responder AWV that will actually be used Thomas Derham, Orange Labs

  18. Overhead and complexity • small additional overhead to perform cross-link BRPs, but... • can allow PCP implementation (or STA) to manage overhead tradeoff • spatial reuse always involves some additional measurements, so is best targeted to low mobility channels (many realistic 11ad scenarios) • all Rx STAs could simultaneously “listen” to TRN if framework supported it • computational complexity reduced due to efficient algorithms • calculate AWVs • division by Hermitian matrix, e.g. Cholesky factorization • find dominant eigenvector, e.g. power iteration method • calculate SINRs for scheduling • matrix multiplication Thomas Derham, Orange Labs

  19. Conclusion • A method of scheduled spatial reuse with collaborative beamforming is proposed • significantly increases aggregate throughput • significantly increases the number of concurrent links that are supported • scheduling guarantees the QoS of each link • Low complexity, overhead and impact • only provide framework to enable implementations • shown that low complexity implementations are possible • complementary to existing beamforming and measurement mechanisms Thomas Derham, Orange Labs

  20. References • [1] C. Cordeiro et al, “Spatial Reuse and Interference Mitigation in 60 GHz”, 802.11-09/0782r0 • [2] C. Cordeiro et al, “PHY/MAC Complete Proposal Specification”, 802.11-10/0433r0 • [3] M. Park et al, “QoS Considerations for 60 GHz Wireless Networks, Globecom 2009 • [4] S. Nandagopalan et al, “MAC Channel Access in 60 GHz”, 802.11-09/0572r0 • [5] C. Cordiero et al, “Implications of Usage Models on TGad Network Architecture”, 802.11-09/0391r0 • [6] M. Lim et al, “Spatial Multiplexing in the Multi-user MIMO Downlink Based on Signal-to-Leakage Ratios”, Globecom 2007 • [7] M. Park et al, “TGad Interference Modeling for MAC Simulations”, 802.11-10/0067r0 • [8] A. Maltsev et al, “Channel Models for 60 GHz WLAN Systems”, 802.11-09/0334r7 • [9] E. Perahia et al, “Evaluation Methodology”, 802.11-09/0296r16 Thomas Derham, Orange Labs

  21. Appendixan additional usage case - two STAs in each device • effectively MxN spatial multiplexing with max. rank 2 • “SU-MIMO” for additional throughput withstrong multipath channel • e.g. “bottlenecks” to/from repeater • “MU-MIMO” to allow simultaneouscommunication between central point andtwo different destinations • e.g. multi-stream video from media server • the same collaborative beamforming techniqueoptimizes AWVs to minimize interference Thomas Derham, Orange Labs

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