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Scheduled Spatial Reuse with Collaborative Beamforming

Scheduled Spatial Reuse with Collaborative Beamforming. Date: 2010-04-30. Authors:. Abstract. Spatial reuse is a key aim for 802.11ad [1] potentially a major increase in aggregate intra-BSS data throughput multiple STAs transmit simultaneously (co-channel)

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Scheduled Spatial Reuse with Collaborative Beamforming

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  1. Scheduled Spatial Reuse with Collaborative Beamforming Date: 2010-04-30 Authors: Thomas Derham, Orange Labs

  2. Abstract • Spatial reuse is a key aim for 802.11ad [1] • potentially a major increase in aggregate intra-BSS data throughput • multiple STAs transmit simultaneously (co-channel) • mutual interference mitigated by directional antennas • Beamforming capability of phased-array antennascan be used to our advantage • a small amount of CSI feedback to the BSS coordinator allows • collaborative beamforming to minimize mutual interference • centralized scheduling to guarantee QoS for all links • A low-complexity scheme can optimize spatial reuse in TGad Thomas Derham, Orange Labs

  3. 802.11.ad usage scenario • in dense networks, multiple links share the same channel • 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) [2] repeater Media server FTTH BSS Set-top box Home Gateway Thomas Derham, Orange Labs

  4. Scheduled spatial reuse • beacon interval “superframe” beacon scheduled access EDCA-likeaccess CSMA/CA “directional CAP” TDMA “CTAP” • most data transmissions are scheduled • since CSMA/CA is inefficient with directional antennas [3] • BSS coordinator (PCP) schedules and transmits beacon • spatial reuse: PCP may co-schedule links in same time slot (e.g. 2x spatial reuse) PCP = PBSS Control Point [4] STA1 to STA2 STA3 to STA4 STA3 STA1 STA2 STA4 Thomas Derham, Orange Labs

  5. How should spatial reuse be controlled? • scheduling • choose which links to co-schedule based on mutual interference metric • guarantee QoS requirements for each link • beamforming • conventional beamforming gives some “natural” interference reduction • but interference may be high e.g. when multiple Rx are close together • collaborative beamforming jointly optimizes beam patterns to take into account interference due to co-scheduling • allows much higher degree of spatial reuse • requires a small amount of CSI feedback to PCP • with which scheduling can also be performed • => beamforming and scheduling of STA-STA links managed by PCP Thomas Derham, Orange Labs

  6. Scheduling process with spatial reuse • STA sends Channel Time Request to PCP for a scheduled slot • if no free slot, PCP requests certain CSI with Channel Information Request • STAs calculates and sends CSI to PCP • PCP uses CSI to perform collaborative beamforming and scheduling STA1 to STA2 < x Channel Time Request Src STA = 3, Dest STA = 4, Length = x Channel Information Request Links: STA1->STA2, STA3->STA4 Cross-links: STA1->STA4, STA3->STA2 Channel Information H1,2, H3,4, H1, 4, H3, 2 perform collaborative beamforming and scheduling STA1 to STA2 STA3 to STA4 PCP STA3 STA1 STA2 STA4 Channel Time Allocation & Beamforming vectors (1) Src STA = 1, Dest STA = 2, Start = 0, Length=x, TxBeam = w12, RxBeam = p12 (2) Src STA = 3, Dest STA = 4, Start = 0, Length=x, TxBeam = w34, RxBeam = p34 Thomas Derham, Orange Labs

  7. Stage 1: PCP determines subset of “candidate” links for co-scheduling • reduce overhead by only obtaining CSI for a subset of links • based on two metrics that approximately indicate spatial separation • 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

  8. Stage 2: Rx STAs calculate requested CSI • using 60 GHz phased-array beam training mechanism • multiple repetitions of a beam training sequence • each time a different Tx/Rx beam training vector combination is used • beam training vectors defined in orthogonal codebook matrices • a TDMA slot is allocated to each Tx for transmission of sequences • all Rx STAs listen to beam training sequence from all Tx STAs • MIMO CSI determined between every Tx and Rx in subset link cross-link link cross-link STA3 STA1 STA2 STA4 Thomas Derham, Orange Labs

  9. Stage 2: Rx STAs calculate requested CSI (2) • MIMO channel matrices determined • MIMO system model: • element is the complex channel gain on subcarrier i withTx/Rx beam training vectors in the kth and lth columns of W and D • MIMO channel matrix estimate calculated: • limited feedback CSI sent to PCP • quantized or (for certain subcarriers) [preferred] • or, to further reduce overhead • transmit-side covariance matrix • or certain eigenvectors/eigenvalues of MIMO channel Rx training codebook Tx training codebook Thomas Derham, Orange Labs

  10. Stage 3: PCP calculates beamforming vectors • collaborative beamforming based on CSI from all STAs • beamforming vectors are (in general) not entries in training codebook • Tx beamforming vectors • maximize Signal to Leakage plus Noise Ratio (SLNR) [5] • “leakage” is interference that a given Tx causes to all other Rx • Rx beamforming vectors • maximize Signal to Interference plus Noise Ratio (SINR) • conditional on Tx beamforming vectors above own link between Tx of pth link and Rx of qth link eig{X} = dominant eigenvector of X Thomas Derham, Orange Labs

  11. Stage 4: PCP determines co-scheduling • based on SINR of each link assuming subset is co-scheduled • links are co-scheduled if for all q, where is a threshold • chosen according to QoS requirement (e.g. bit rate) for qth link • if SINRs too low, remove link with lowest SLNR from subset and recalculate • repeat until SINRs above thresholds • PCP co-schedules links in same time slot • informs STAs of scheduling (in beacon) and of beamforming vectors Thomas Derham, Orange Labs

  12. System-level simulation setup • conference room model • inter-cluster parameters between all pairs of STAs from ray-tracing [6] • correctly models interference between all STAs • TGad channel model code used for intra-cluster parameters [7] Link-n Thomas Derham, Orange Labs

  13. STA positions randomized on table • 10 pairs of STAs on 2.5 x 1 m table (random orientation) ==> dense network • scheduler rule: co-schedule all links where link SINR > 4 dB (lowest MCS) • beamforming types: (a) conventional training, (b) collaborative beamforming complementary CDF of aggregate throughput • at Pr=0.5, collaborative beamforming increases aggregate throughput byapprox. 4 Gbps (+40%) Thomas Derham, Orange Labs

  14. STA positions randomized on table (2) • 10 pairs of STAs on 2.5 x 1 m table (random orientation) ==> dense network • scheduler rule: co-schedule all links where link SINR > 4 dB (lowest MCS) • beamforming types: (a) conventional training, (b) collaborative beamforming complementary CDF of number of co-scheduled links • at Pr=0.5, collaborative beamforming increases number of links that can be co-scheduled by 50% Thomas Derham, Orange Labs

  15. STA positions as per Evaluation Methodology • 3 STA-STA pairs on table [8]: STA2=>1 (LoS), STA3=>5 (NLoS), STA7=>8 (LoS) • scheduler rule: co-schedule all links where link SINR > 4 dB (lowest MCS) • beamforming types: (a) conventional training, (b) collaborative beamforming complementary CDF of aggregate throughput complementary CDF of number of co-scheduled links no. of co-scheduledlinks increased for approx. 40% ofchannel instances Thomas Derham, Orange Labs

  16. STA positions as per Evaluation Methodology (2) • 3 STA-STA pairs on table [8]: STA2=>1 (LoS), STA3=>5 (NLoS), STA7=>8 (LoS) • scheduler rule: co-schedule all links where link SINR > 16 dB (16QAM, r=3/4) • beamforming types: (a) conventional training, (b) collaborative beamforming complementary CDF of aggregate throughput complementary CDF of number of co-scheduled links probability of spatial reuse increased 9% ==> 26% spatial reuse region Thomas Derham, Orange Labs

  17. Channel tracking and overhead • channel varies over time due to human blockage, mobility, etc • beamforming and co-scheduling should adapt by tracking channel • procedure is repeated at intervals defined by PCP • overhead of beam training mechanism similar to 802.15.3c • “clustered” tracking (reduced overhead) updates just certain elements of • in many usage cases, spatial channel characteristics static over extended time • overhead of CSI feedback to PCP is small • e.g. feedback Y or H on 8 evenly spaced subcarriers, 4x4 antenna arrays,2 bytes per complex element ==> 4 Kbytes per link • feedback of covariance matrix (or its eigenvectors) reduces overhead further • tracking updates may have much lower overhead if is fed back Thomas Derham, Orange Labs

  18. Implementation complexity • additional STA complexity is negligible • regular frequency-domain channel estimation (to obtain Y) • additional PCP complexity reduced due to efficient algorithms • calculate beamforming vectors • 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. Supporting scheduled spatial reuse with collaborative beamforming in 802.11ad • STAs with beamforming capability shall support • channel information request (action frame) • channel information feedback (IE) • beamforming vector feedback (IE) • cross-link beam training mechanism • PCP shall also support • calculation of collaborative beamforming vectors • calculation of SINR for co-scheduling Thomas Derham, Orange Labs

  20. 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 and overhead • limited feedback and closed-form beamforming solutions • flexible to allow STAs with low capabilities to “opt out” • This method optimizes TGad to make the most of itsbeamforming capabilities Thomas Derham, Orange Labs

  21. Strawpoll • Do you support inclusion of the technique “Scheduled Spatial Reuse with Collaborative Beamforming” as described in 10/0487r0 in the TGad draft amendment? • Yes • No • Abstain Thomas Derham, Orange Labs

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

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