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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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Scheduled Spatial Reuse with Collaborative Beamforming

Date: 2010-05-16

Authors:

Thomas Derham, Orange Labs


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


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


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


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


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


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


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


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


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


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


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


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


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


    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


    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


    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


    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


    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


    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


    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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