Real-time Distributed MIMO Systems. Hariharan Rahul Ezzeldin Hamed, Mohammed A. Abdelghany, Dina Katabi

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1 Real-time Distributed MIMO Systems Hariharan Rahul Ezzeldin Hamed, Mohammed A. Abdelghany, Dina Katabi

2 Dense Wireless Networks Stadiums Concerts Airports Malls

3 Interference Limits Wireless Throughput APs cannot transmit at the same time, in the same frequency à Take turns to avoid collisions Ethernet AP1 AP2 AP3 AP N User 1 User 2 User 3 User N Total Wireless Throughput Stays Constant à Each AP gets 1/N of the total throughput

4 Distributed MIMO is the Holy Grail Distributed protocol for APs to act as a huge MIMO transmitter with sum of antennas Ethernet AP1 AP2 AP3 AP N User 1 User 2 User 3 User N N APs à N times higher throughput

5 Much recent work in moving distributed MIMO from theory to practice However, we still do not have real-time distributed MIMO systems operating on independent devices with their own clocks!

6 Why aren t we there yet? Distributed MIMO needs accurate channel estimation à High overhead process that could eat up all the gains. Need distributed power control. Need an architecture that can support these complex operations in real-time.

7

8 Why aren t we there yet? Distributed MIMO needs accurate channel estimation à High overhead process that could eat up all the gains. Need distributed power control. Need an architecture that can support these complex operations in real-time.

9 Channel Estimation and Feedback Ethernet AP1 AP2 AP3 AP N User 1 User 2 User 3 User N N Channel Estimation Packets N 2 Channel Measurements Need to do this periodically as environment changes

10 Channel Feedback Overhead

11 Reciprocity in Traditional MIMO h "#,% h &'(),% Access Point h "#,* h &'(),* Client Reciprocity is the property that the ratio of downlink channels is equal to the ratio of uplink channels up to a constant. This constant is the ratio between hardware chains of AP antennas. Allows us to estimate this constant once and use it for all future uplink transmissions and across clients.

12 What happens with Distributed MIMO? AP[1] AP[2] Separate devices à Different Crystals à RF chains have oscillator offset relative to each other

13 Traditional Reciprocity does not work with Distributed MIMO The constant is no longer constant, but changes rapidly with time. Theorem: The downlink and uplink channel ratios can be written as: 8 9:;<,6 8 9:;<,= = C * t 8 >?,6 8 >?,= where C * (t) = C * (0) e 3* 5 67

14 Reciprocity and Distributed MIMO Calibration Calibration Parameter is rapidly time varying à Cannot do one-time calibration Need to repeatedly calibrate: for uplink transmissions from every client at every AP

15 MegaMIMO 2.0 Calibration for Reciprocity Avoids the overhead of repeated calibration Distributed mechanism for updating calibration parameters at slaves with no overhead

16 MegaMIMO 2.0 Calibration Formulation Master AP Slave AP C * (t) = C * (0) e 3* 5 67 Compute the initial calibration parameter, C * (0) Update the calibration parameter at time t by estimating e 3* 5 67

17 MegaMIMO 2.0 Initial Calibration Master AP h * h % Slave AP 1. Measure channel h % from Master AP to Slave AP 2. Measure channel h * from Slave AP to Master AP 3. Compute Initial Calibration Parameter C * 0 as 4. At slave, store C * 0 and h % as h % (0) C * 0 = h * h %

18 MegaMIMO 2.0 Calibration Update Ack t Master AP Packet Client h % (t) Slave AP 1. Client transmits packet à Master and Slave measure uplink channels from client 2. Master sends sync trailer (Can leverage Wi-Fi ack) 3. Slave measures channel h % t from master. h % t = h % 0 e h % (t) h % (0) 4. Recall that each slave has h % 0. Each slave computes e 3* 567 = Consistent channel estimates using reciprocity 5. Each slave computes the updated calibration parameter C * (t) = C * (0) e 3* 5 at Each slave computes the corrected downlink channel using the updated calibration parameter all APs *

19 MegaMIMO 2.0 Procedure Preparing Calibration Constants Master AP transmits a reference packet All slaves follow with a response Each slave calculates its calibration parameter Channel Estimation Performed for each uplink transmission from a client The master AP follows with an ACK (Sync trailer) Each slave calculates its downlink channel using the corrected calibration parameter Joint Transmission The same as MegaMIMO 1.0

20 Why aren t we there yet? Distributed MIMO needs accurate channel estimation à High overhead process that could eat up all the gains. Need distributed power control. Need an architecture that can support these complex operations in real-time.

21 The Need for Automatic Gain Control (AGC) RF Chain +1 V ADC Digital Processing Works in analog domain -1 V Converts analog signal to digital samples Decodes digital samples ADC accepts signals in a specific range RF chain converts received signal to ADC range AGC is an adaptive algorithm to perform this conversion

22 AGC in Traditional MIMO AP applies the same gain to all receive antennas h 11 h 12 h 13 h 14 h 21 h 22 h 23 h 24 h 31 h 32 h 33 h 34 h 41 h 42 h 43 h 44

23 AGC in Traditional MIMO AP applies the same gain to all receive antennas α h 11 α h 12 α h 13 α h 14 α h 21 α h 22 α h 23 α h 24 α h 31 α h 32 αh 33 α h 34 α h 41 α h 42 α h 43 α h 44

24 AGC in Distributed MIMO Each AP-client link has an independent gain α 11 h 11 α 12 h 12 α 13 h 13 α 14 h 14 α 21 h 21 α 22 h 22 α 23 h 23 α 24 h 24 α 31 h 31 α 32 h 32 α 33 h 33 α 34 h 34 α 41 h 41 α 42 h 42 α 43 h 43 α 44 h 44 We need a protocol for ensuring that the multipliers are the same despite being applied on different boxes

25 Compensating for the AGC AGC typically has a coarse power setting à Need to convert to a complex α value. This conversion is not known a priori. MegaMIMO 2.0 learns this conversion factor. Each antenna transmits a signal. Receiver sets gain to a particular coarse value, and measures received channel Repeats across all coarse gain settings Needs to be recalibrated infrequently to account for drift of analog components.

26 Why aren t we there yet? Distributed MIMO needs accurate channel estimation à High overhead process that could eat up all the gains. Need distributed power control. Need an architecture that can support these complex operations in real-time.

27 MegaMIMO 2.0 PHY-MAC Architecture PHY is a complex system: power adaptation, rate adaptation, encoding and decoding at various modulations and code rates etc. Traditional PHY layers only have local control and coordination with an on-board MAC. Distributed MIMO requires distributed control and coordination across multiple transmit and receive chains. We design an architecture that provides hooks to/from the PHY to enable this distributed control efficiently in hardware.

28 Performance

29 Implementation Implemented on Zed Board and FMCOMMS2 RF Front End PHY and real time MAC implemented on Zynq FPGA Control Plane implemented on embedded ARM core

30 Indoor Testbed simulating a conference room 4 APs transmitting to 4 clients Line of sight and non line of sight scenarios Mobility Environment Users Evaluation Metrics SNR obtained by users during joint transmission Total throughput

31 Reciprocity vs. Feedback

32 Reciprocity vs. Feedback

33 Reciprocity vs. Feedback Reciprocity matches feedback across the range of SNRs à Calibration is accurate

34 MegaMIMO 2.0 vs. Traditional

35 MegaMIMO 2.0 vs. Traditional

36 MegaMIMO 2.0 vs. Traditional x MegaMIMO 2.0 with reciprocity provides the expected scaling gains across the range of SNRs

37 Reciprocity Throughput Gain with Mobility Environmental Movement Client Mobility

38 Reciprocity Throughput Gain with Mobility No single feedback interval is optimal across all scenarios. Environmental Movement Client Mobility Reciprocity outperforms explicit feedback.

39 Conclusion MegaMIMO 2.0 is the first real-time distributed MIMO PHY layer operating across devices with independent clocks. Adapts to changing channel conditions in real-time with no overhead. Demonstrated with a hardware implementation.

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