Joint Processing MU-MIMO

Slides:



Advertisements
Similar presentations
Interference Cancellation for Downlink MU-MIMO
Advertisements

Submission March 2012 doc.: IEEE Slide 1 SINR and Inter-STA Interference Indication Feedback in DL MU-MIMO Date: Authors:
Doc.: IEEE /1234r0 Submission November 2009 Sameer Vermani, QualcommSlide 1 Interference Cancellation for Downlink MU-MIMO Date: Authors:
Doc.: IEEE /0099 Submission Payload Symbol Size for 11ax January 2015 Ron Porat, BroadcomSlide 1 Date: Authors:
Submission doc.: IEEE /1452r0 November 2014 Leif Wilhelmsson, EricssonSlide 1 Frequency selective scheduling in OFDMA Date: Authors:
Phase Tracking During VHT-LTF
Doc.: IEEE /0099 Submission Payload Symbol Size for 11ax January 2015 Ron Porat, BroadcomSlide 1 Date: Authors:
Submission doc.: IEEE /0824r0 July 2015 Slide 1 Pilot Design for 11ax Downlink Transmissions Date: Authors: Yujin Noh, Newracom.
Submission doc.: IEEE /1088r0 September 2015 Daewon Lee, NewracomSlide 1 LTF Design for Uplink MU-MIMO Date: Authors:
Submission September 2015 doc.: IEEE /1091r0 September 2015 Considerations on Range Extension with SIG-A Repetition Date: Authors:
Doc.: IEEE /1051r0 Submission September 2013 Ron Porat, Broadcom Evaluation Methodology Date: Authors: Slide 1.
Interdigital Communications Submission doc.: IEEE /1333r1 November 2015 Feasibility of SU-MIMO under Array Alignment Method Date: Slide.
Doc.: IEEE /0626r1 Submission Feedback Element Compression for ax May 2016 Slide 1 Date: Authors: Kome Oteri (InterDigital)
11ax PAR Verification using UL MU-MIMO
Open Loop vs Closed Loop SU-MIMO for 11ay
Discussions on 11ac PHY Efficiency
Implicit Sounding for HE WLAN
Comparisons of Simultaneous Downlink Transmissions
Maximum Tone Grouping Size for ax Feedback
Feedback Element Compression for ax
Considerations on AP Coordination
Maximum Tone Grouping Size for ax Feedback
AP Coordinated Beamforming for EHT
MU-MIMO channel access flow for 11ay
Considerations on AP Coordination
Feedback Element Compression for ax
Maximum Tone Grouping Size for ax Feedback
Discussions on 11ac PHY Efficiency
Constrained Distributed MU-MIMO
RBIR-based PHY Abstraction with Channel Estimation Error
Further Discussion on Beam Tracking for ay
HARQ Gain Studies Date: Authors: November 2018 Name
Consideration on multi-AP coordination for EHT
Channel Dimension Reduction in MU Operation
AP Coordination in EHT Date: Authors: Name Affiliations
Further Discussions on PHY Abstraction
Consideration on multi-AP coordination for EHT
Initial Distributed MU-MIMO Simulations
Update on “Channel Models for 60 GHz WLAN Systems” Document
Synchronization Requirements
Discussions on 11ac PHY Efficiency
Multi-AP Transmission Procedure
Discussions on 11ac PHY Efficiency
Joint Processing MU-MIMO – Update
Multi-AP Transmission Procedure
Performance Gains from CCA Optimization
AP Coordination in EHT Date: Authors: Name Affiliations
White Space Regulatory Issues
Joint Processing MU-MIMO – Update
On TX EVM Date: Authors: September 2017 Month Year
Distributed MU-MIMO and HARQ Support for EHT
Performance Investigation on Multi-AP Transmission
Joint Transmissions: Backhaul and Gain State Issues
AP Coordination in EHT Date: Authors: Name Affiliations
Performance Investigation on Multi-AP Transmission
PHY designs for NGV Date: Authors:
Multi-AP Transmission Procedure
Comparison of Coordinated BF and Nulling with JT
Consideration on Multi-AP Sounding
Consideration on System Level Simulation
Discussions of Multi-AP JT
Consideration on Joint Transmission
VHT LO Leakage Requirement
Consideration on Multi-AP Sounding
VHT LO Leakage Requirement
Coordinated Spatial Reuse Performance Analysis
Measurements for Distributed-MU-MIMO
Coordinated Spatial Reuse Performance Analysis
Multi-AP backhaul analysis
Sounding for AP Collaboration
Presentation transcript:

Joint Processing MU-MIMO Month Year doc.: IEEE 802.11-yy/xxxxr0 January 2019 Joint Processing MU-MIMO Date: 2019-01-14 Authors: Name Affiliations Address Phone email Ron Porat Broadcom   ron.porat@broadcom.com Srinath Puducheri Ron Porat (Broadcom) John Doe, Some Company

January 2019 Abstract In the September meeting contribution number 1439 we introduced a framework for joint processing (previously called distributed) MU-MIMO operation using wireless synchronization techniques, building on mechanisms that exist in 11ax. In this contribution we provide simulation results for the proposed framework Problems reminder – Problem A - from the time of NDP (when channel feedback is collected) to the time of joint data transmission, each AP drifts based on its own (e.g. 20ppm) clock. In addition, AP acquisition timings vary each packet. Problem B – during joint data transmission, APs start drifting again due to residual CFO error Proposed solutions reminder – Problem A - slave triggers are sent before the NDP and before joint data transmissions to enable drift estimation. The slave trigger enables capturing the exact phase drift which will include the impact of CFO and phase noise. Problem B – we propose accurate CFO estimation Ron Porat (Broadcom)

Problem A - Phase alignment before the joint data transmission January 2019 Problem A - Phase alignment before the joint data transmission Ron Porat (Broadcom)

January 2019 Estimation Each slave AP uses the channel estimates from the slave triggers, denoted here by the vectors h_ndp (from the trigger before the NDP) and h_data (from the trigger before the joint data transmission) to calculate two parameters related to the phase drift: Timing errors (acquisition error) – these are manifested as a linear phase drift across frequency. Common phase drift due to different RF clocks and phase noise between the Master and each slave AP We assume here for tone i where all other impairments are lumped into the noise component (incl. channel aging) And then form, as an example, the following metric: With MIMO configuration, this metric is summed up across the receive antennas It is straightforward to form metrics to estimate theta and phi from h_diff. Ron Porat (Broadcom)

Simulation Methodology January 2019 Simulation Methodology We employ two sets of simulations – the first simulation is used to estimate phase and timing error. The second simulation performs joint MU-MIMO transmission whereby each slave AP has an offset in the amount of the residual estimation error relative to the master AP. Both sets of simulations use the 11nD channel model with -30dBc aging and BW=80MHz. Residual common and linear phase errors are simulated across 1000 channel realizations using one beamformed 4x LTF In the joint MU-MIMO simulation each slave AP is assumed to have as input the same 10% worst residual phase error for all channel instantiations. We calculate average MU SINR per STA per spatial stream with and without impairments and plot them. Ron Porat (Broadcom)

Simulation Configurations January 2019 Simulation Configurations We believe the vast majority of cases for mesh deployments will involve 4-antenna AP. Hence we focus on two configurations: Two 4-antenna AP (one master one slave) Four 4-antenna AP (one master three slaves) Each configuration is 75% loaded - 2 streams to three or six 2-antenna STA for a total of 6 or 12 streams In terms of SNR we have a 2D problem – AP-AP SNR and STA-AP SNR. For simplicity we ran simulations assuming 2 fixed AP-AP SNR values – 10 and 20 dB and varied the STA-AP SNR. We assume all STA have the same SNR to all AP. Ron Porat (Broadcom)

January 2019 Results Two AP Four AP No significant impact on the average per-STA MU-SINR is seen as the residual timing/phase estimation error are small Ron Porat (Broadcom)

Problem B - Controlling the drift during joint data transmission January 2019 Problem B - Controlling the drift during joint data transmission Ron Porat (Broadcom)

High Level Description January 2019 High Level Description At the beginning of the joint data transmission, all the APs are phase/time-synchronized with very small residual error based on the description of problem A. However, during the joint transmission the APs will start drifting again which will cause the phase offset between them to increase with time. In the following slides we estimate the performance degradation with various fixed phase offsets between the APs and provide a result for CFO estimation. Simulation scenarios – same configuration as problem A. Simulation methodology - we compare the joint processing MU-MIMO performance of multiple APs relative to a baseline comprising of TDMA between single APs: The baseline also assumes 75% loading MU-MIMO We assume equal sum power for joint processing as for the baseline single AP (in the following slide the X-axis means total power across all AP) This is similar to assumptions made for MU-MIMO but in this case alternatively per-AP fixed power can be assumed, yielding higher gains. Ron Porat (Broadcom)

January 2019 Results Two AP Four AP 8 degrees of phase offset still provide substantial gains Ron Porat (Broadcom)

CFO Estimation January 2019 Accurate CFO estimation at the slave AP is important to minimizing the phase drift between slave and master AP during the joint data transmission. For an example here, we have leveraged the 4x LTF in 11ax to improve CFO estimation beyond the performance available from the L-LTF by assuming in exactly the same manner that we have two identical and adjacent 4x LTF (in the HE-LTF part) and estimated the phase drift between them. For the AP configuration assumed throughout (4-ant AP for master and slave) we got 30Hz residual CFO error in moderate SNR Ron Porat (Broadcom)

January 2019 Summary Our investigation demonstrates the feasibility of this scheme by proving that estimation techniques can compensate for the drift between slave and master APs. This is achieved using the following two components: First, estimating and compensating the total phase drift between the NDP and the joint data transmission Second, estimating CFO accurately to minimize phase drift during the joint data transmission Ron Porat (Broadcom)