Re Real-tim time e Dis istr trib ibuted ed MIM IMO Sy - - PowerPoint PPT Presentation

Re Real-tim time e Dis istr trib ibuted ed MIM IMO Sy Systems

Hariharan Rahul Ezzeldin Hamed, Mohammed A. Abdelghany, Dina Katabi

De Dense W Wir irele less Ne Networks

• Stadiums
• Concerts
• Airports
• Malls

Us User 1 Et Ethernet AP AP1 Us User 2 AP AP2 Us User 3 AP AP3 Us User N AP AP N

… …

Total Wireless Throughput Stays Constant à Each AP gets 1/N of the total throughput

In Interf erfer eren ence L e Limits Wi ts Wirel eless T ess Throu

• ughput

APs cannot transmit at the same time, in the same frequency à Take turns to avoid collisions

Us User 1 Et Ethernet AP AP1 Us User 2 AP AP2 Us User 3 AP AP3 Us User N AP AP N

… …

N APs à N times higher throughput

Dis Distrib ibuted M MIMO is is t the H Holy ly Gr Grail ail

Distributed protocol for APs to act as a huge MIMO transmitter with sum of antennas

Mu Much recent work rk in mo moving distri ributed MI MIMO MO fr from theory y to practice Ho However er, we e still till do do no not t ha have e real eal-tim time e dis istr trib ibuted ed MI MIMO MO systems ms operating on independent de devi vices es wi with h thei heir own wn clocks!

Wh Why y aren’t n’t we the here 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
• perations in real-time.

Wh Why y aren’t n’t we the here 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
• perations in real-time.

Us User 1 Et Ethernet AP AP1 Us User 2 AP AP2 Us User 3 AP AP3 Us User N AP AP N

… …

N Channel Estimation Packets N2 Channel Measurements Need to do this periodically as environment changes

Ch Channel Es Estima mation and Feedback

Ch Channel Feedback k Ov Overh rhead

Re Reciprocity in Traditional MIMO

• 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. Client

ℎ"#,% ℎ&'(),% ℎ"#,* ℎ&'(),*

Access Point

AP[1] AP[2]

Wha What ha happe ppens ns wi with h Distribut buted d MIMO? O?

Separate devices à Different Crystals à RF chains have oscillator offset relative to each other

The “constant” is no longer constant, but changes rapidly with time.

Tr Traditional Reciprocity does not work with Dis Distrib ibuted M MIMO

where 𝐷*(𝑢) = 𝐷*(0)×𝑓3*∆567 Theorem: The downlink and uplink channel ratios can be written as:

89:;<,6 89:;<,= = 𝐷* 𝑢 × 8>?,6 8>?,=

Re 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

Me MegaMI MIMO MO 2. 2.0 0 Ca Calibration for r Reciprocity

• Avoids the overhead of repeated calibration
• Distributed mechanism for updating calibration

parameters at slaves with no overhead

Me MegaMI MIMO MO 2. 2.0 0 Ca Calibration Formu rmulation

𝐷*(𝑢) = 𝐷*(0)×𝑓3*∆567 Master AP Slave AP

• Compute the initial calibration parameter, 𝐷*(0)
• Update the calibration parameter at time t by estimating 𝑓3*∆567

Me MegaMI MIMO MO 2. 2.0 0 Initial Ca Calibration

ℎ* ℎ%

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 𝐷* 0 as 4. At slave, store 𝐷* 0 and ℎ% as ℎ%(0)

𝐷* 0 = ℎ* ℎ% Master AP Slave AP

Me MegaMI MIMO MO 2. 2.0 0 Ca Calibration Update

Client

𝑢 𝐵𝑑𝑙

1. Client transmits packet à Master and Slave measure uplink channels from client 2. Master sends sync trailer

3. Slave measures channel ℎ% 𝑢 from master. ℎ% 𝑢 = ℎ% 0 ×𝑓3∆567

4. Recall that each slave has ℎ% 0 . Each slave computes 𝑓3*∆567 = 5. Each slave computes the updated calibration parameter 6. Each slave computes the corrected downlink channel using the updated calibration parameter

Master AP Slave AP

ℎ%(𝑢)

ℎ%(𝑢) ℎ%(0)

*

𝐷*(𝑢) = 𝐷*(0)×𝑓3*∆567

Packet

(Can leverage Wi-Fi ack)

Consistent channel estimates using reciprocity at all APs

• 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

Me MegaMI MIMO MO 2. 2.0 0 Procedure

Wh Why y aren’t n’t we the here 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
• perations in real-time.

Th The Need for Automatic Gain Control (AGC)

RF Chain Digital Processing ADC

Converts analog signal to digital samples Decodes digital samples Works in analog domain

• ADC accepts signals in a specific range
• RF chain converts received signal to ADC range
• AGC is an adaptive algorithm to perform this conversion

+1 V

• 1 V

h11

11

h12

AG AGC in Traditional MIMO

h13 h14

14

h21 h22 h23 h24 h31 h32 h33 h34 h41 h42 h43 h44

AP applies the same gain to all receive antennas

h11

11

h12

AG AGC in Traditional MIMO

h13 h14

14

h21 h22 h23 h24 h31 h32 h33 h34 h41 h42 h43 h44 𝜷 𝜷 𝜷 𝜷 𝜷 𝜷 𝜷 𝜷 𝜷 𝜷 𝜷 𝜷 𝜷 𝜷 𝜷 𝜷

AP applies the same gain to all receive antennas

h11

11

h12

AG AGC in Distributed MIMO

h13 h14

14

h21 h22 h23 h24 h31 h32 h33 h34 h41 h42 h43 h44 𝜷11

11

𝜷12

12

𝜷13

13

𝜷14

14

𝜷21

21

𝜷22

22

𝜷23

23

𝜷24

24

𝜷31

31

𝜷32

32

𝜷33

33

𝜷34

34

𝜷41

41

𝜷42

42

𝜷43

43

𝜷44

44

Each AP-client link has an independent gain We need a protocol for ensuring that the multipliers are the same despite being applied on different boxes

• AGC typically has a coarse power setting à Need to convert

to a complex 𝛽 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.

Co Comp mpensa sating for r the AGC

Wh Why y aren’t n’t we the here 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
• perations in real-time.

• 802.11 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.

Me MegaMI MIMO MO 2. 2.0 0 PHY-MA MAC C Architecture

Pe Performance

Im Implem plemen entatio tion

• 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

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

Ev Evaluation

Re Reciprocity vs. Feedback

Re Reciprocity vs. Feedback

Re Reciprocity vs. Feedback

Re Reciprocity matches feedback across the range of SNRs à Ca Calibration is accurate

Me MegaMI MIMO MO 2. 2.0 0 vs. Traditional 802. 802.11 11

Me MegaMI MIMO MO 2. 2.0 0 vs. Traditional 802. 802.11 11

Me MegaMI MIMO MO 2. 2.0 0 vs. Traditional 802. 802.11 11

Me MegaMI MIMO MO 2. 2.0 0 with h reci ecipr proci city pr provides des the he expect pected ed scaling ng gains ns acr cross the he ra range of SNRs

3.3-3.6x

Environmental Movement Client Mobility

Reciprocity Th Throughput Gain with Mobility

Client Mobility

Reciprocity Th Throughput Gain with Mobility

Environmental Movement

Reciprocity outperforms explicit feedback. No single feedback interval is optimal across all scenarios.

• 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.

Co Conclusi sion