achieving spectrum efficiency

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Achieving Spectrum Efficiency

Lili QiuUniversity of Texas at Austin

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Motivation• Explosive increasing user traffic

• Spectrum crisis [NewYorkTimes12, CNET12, …]

• Urgent need of new technologies to dramatically increase spectrum efficiency

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Our Work• Spectrum sharing

– Develop an efficient MAC for multiple nodes to efficiently share the medium

• Spectrum access– Develop and implement multi-point to

multi-point MIMO

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Our Work• Spectrum sharing

– Develop an efficient MAC for multiple nodes to share the medium

• Spectrum access– Develop and implement multi-point to

multi-point MIMO

CRMA: Collision Resistant Multiple

AccessJoint work with

Tianji Li, Mi Kyung Han, Apurv Bhartia, Eric Rozner, Yin Zhang, Brad Zarikoff

ACM MobiCom 20115

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Motivation• Traditional MAC protocols avoid collisions

– FDMA, TDMA, FTDMA, CSMA, …

• Avoid collisions large overhead– FDMA: unoccupied channel and guard band– TDMA: global coordination and centralized scheduling– CSMA: carrier sense overhead, hidden terminals,

exposed terminals• 1500-byte frame: 29% for 802.11a (54Mbps) and

72% for 802.11n (600Mbps)• TCP ACK: 77% overhead for 802.11a and 83%

overhead for 802.11n– …

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Collision Avoidance Collision Resistance

Let collisions happen naturally and decode collisions

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Our Contributions• New encoding/decoding to allow multiple

signals transmitted on the same channel

• Collision resistant medium access protocol (CRMA) based on the encoding/decoding

• Evaluation to show CRMA is a promising direction for spectrum sharing

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CRMA: An Illustrating Example

Channel 1

Channel 2

S1 R1

S2 R2Randomly pick a channel? - 50% collisions!

Coordinate to avoid using the same channel? - Large overhead especially for lots of dynamic flows

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CRMA: An Illustrating Example

Channel 1

Channel 2

Frame 1

Frame 2

Frame 1

Frame 2

S1 R1

S2 R2

𝑅 (2 )=h (2,1 )×𝑐 (2,1 )×𝑥1+h (2,1 )×𝑐 (2,2 )×𝑥2

𝑅 (1 )=h (1,1 )×𝑐 (1,1 )×𝑥1+h (1,2 )×𝑐 (1,2 )×𝑥 2

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CRMA: An Illustrating Example

Channel 1

Channel 2

Frame 1

Frame 2

Frame 1

Frame 2

𝐑=𝐀𝐱

S1 R1

S2 R2

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CRMA: Research QuestionsChannel 1

Channel 2

Frame 1Frame 2

Frame 1Frame 2

A B

C D

𝑅 (1 )=h (1,1 )×𝑐 (1,1 )×𝑥1+h (1,2 )×𝑐 (1,1 )×𝑥 2𝑅 (2 )=h (2,1 )×𝑐 (2,1 )×𝑥1+h (2,1 )×𝑐 (2,2 )×𝑥2

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CRMA: Research QuestionsChannel 1

Channel 2

Frame 1Frame 2

Frame 1Frame 2

A B

C D

- What is the code c?- How do the sender and receiver agree on the code?

𝑅 (1 )=h (1,1 )×𝑐 (1,1 )×𝑥1+h (1,2 )×𝑐 (1,1 )×𝑥 2𝑅 (2 )=h (2,1 )×𝑐 (2,1 )×𝑥1+h (2,1 )×𝑐 (2,2 )×𝑥2

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CRMA: Research QuestionsChannel 1

Channel 2

Frame 1Frame 2

Frame 1Frame 2

A B

C D

- What is the code c?- How do the sender and receiver agree on the code?- How to decode transmissions?- How to handle decoding failures?- How to decode misaligned collisions?- How to limit # transmissions in a collision?- How to enhance spectrum utilization?

𝑅 (1 )=h (1,1 )×𝑐 (1,1 )×𝑥1+h (1,2 )×𝑐 (1,1 )×𝑥 2𝑅 (2 )=h (2,1 )×𝑐 (2,1 )×𝑥1+h (2,1 )×𝑐 (2,2 )×𝑥2

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CRMA- What is the code c?- How do the sender and receiver agree on the

code?- How to decode transmissions?- How to handle decoding failures?- How to decode misaligned collisions?- How to limit # transmissions in a collision?- How to enhance spectrum utilization?

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Code Selection• We use a binary code for simplicity

– C(i,f)=1 if transmitter i uses channel f, otherwise 0

Channel 1

Channel 2

Channel 3

C =

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Code Selection• We use a binary code for simplicity

– C(i,f)=1 if transmitter i uses channel f, otherwise 0

Channel 1

Channel 2

Channel 3C = ) =

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Code Selection• We use a binary code

– c(i,f)=1 if transmitter i uses channel f, otherwise 0

Channel 1

Channel 2

Channel 3

𝑅=𝐴𝑥=[ h11 h210h31

h220 ]𝑥

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Code Selection (Cont.)• For a collision to be decodable (, matrix A

should be full rank– # transmissions in a collision # channels– Rows in matrix A are linearly independent

• Different flows select different sets of channels

– Selecting random or least used channels• Different flows that select the same channel

see different channel coefficients– Validated by previous work on location

distinction and secure communication• Use a non-binary code to further increase

the chance of having a full rank A

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CRMA- What is the code c?- How do the sender and receiver agree on the

code?- How to decode transmissions?- How to handle decoding failures?- How to decode misaligned collisions?- How to limit # transmissions in a collision?- How to enhance spectrum utilization?

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Code Establishment• Using control channel

– The sender and receiver negotiate the code on a separate control channel

• In band notification– Each frame has two PN sequences to denote

sender and receiver IDs– A receiver correlates the received signal

• with its ID to determine if the frame is destined to itself

• with senders’ IDs to determine who send traffic– Correlation is close to 0 except when perfectly

aligned with the IDs works under collisions!– In-band processing but no need for control

channel

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CRMA- What is the code c?- How do the sender and receiver agree on the

code?- How to decode transmissions?- How to handle decoding failures?- How to decode misaligned collisions?- How to limit # transmissions in a collision?- How to enhance spectrum utilization?

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Decoding Transmissions• Detect frame arrival and departure

– Correlate the received signal with the preamble and postamble

– Correlation is close to 0 except when perfectly aligned with preamble or postamble

Accurate preamble detection (e.g., false positive and false negative ratios are 0 when SINR=-2).

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Decoding Transmissions (Cont.)• Detect sender and receiver IDs

– Correlate with PN sequences corresponding to their IDs– Consistently identifies correct IDs when SINR varies from -6 to

6• Solve

– Find is minimized• Obtain channel estimation

– Clean preamble: the standard approach– Preamble with data: iterative decoding [ZigZag]

• Solve for data x assuming A and R are known• Plug x back to Ax=R, and solve for channel coefficients

• Adapt to channel change during frame transmission: iterative decoding

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Handling Decoding Failures• Use ACKs and retransmissions to

enhance reliability

• ACKs are sent in the same way as data frames – Receiver sends an ACK on the same set

of selected channels– Sender decodes the ACK by solving a

linear system (as decoding data frame)

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Problem of Misaligned Collisions

Symbol 1

Symbol 2

FFT window

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Handling Misaligned Collisions

CPi-1

CPi CPi+1

CPi-1 CPi CPi+1

Symbol i-1

Symbol i-1

Symbol i-1

Symbol i-1

Symbol i+1

Symbol i+1

offset

FTT window

• Cyclic prefix (CP) allows collided symbols fall in the same FFT window

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Handling Misaligned Collisions

CPi-1CPi CPi+1

CPi-1 CPi CPi+1

Symbol i-1

Symbol i-1

Symbol i-1

Symbol i-1

Symbol i+1

Symbol i+1

offset

FTT windowsame

• Cyclic prefix (CP) allows collided symbols fall in the same FFT window

• Timing offset results in a phase shift after receiver FFT

• Signals perfectly align: solve • With misalignment of • Use synchronization to keep the offset within

[nT, nT + CP] (e.g., SourceSync, FICA)

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Other Design Components• Limiting overlapping transmissions

– Different from IEEE 802.11, CWmin = 0– Use exponential backoff upon loss

• Enhancing spectrum utilization– When # flows # channels , each physical flow

creates multiple virtual flows so that is close to to increase utilization

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Evaluation Methodology• Testbed experiments show feasibility

– Implement CRMA on top of the default OFDM implementation in USRP

– 5 GHz, BPSK, 200 subcarriers, each 1.95KHz

• Qualnet simulations evaluate efficiency– Compare CRMA w/ and wo/ virtual flows,

CSMA/CA (multiple channels), WiFi (one channel), random access

– 1000-byte frames, 16 QAM, 20MHz total spectrum divided into 10 channels

– 700 MHz for long distance, and 5 GHz for short distance networks

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Testbed Experiments (Cont.)

SINR (dB) High SINR Low SINR0 25% 25%1 98% 95%3 98% 95%5 100% 98%7 99% 0.09%

Delivery rate of 1000-byte frames

• High decoding rate when 1 ≤ SINR ≤ 5, and degrades as SINR approaches 0 or too high.

• The latter could be improved by partial packet recovery.

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Testbed Experiments (Cont.)

CRMA accurately decodes collisions up to 140 sample offsets.

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Qualnet Simulation: Varying # flows in long distance

networks

CRMA-VF > CRMA > other schemes.

0

10

20

30

40

50

0 5 10 15 20

Total

thro

ughp

ut (M

bps)

Number of flows

CRMA-VFCRMACSMAR_ACCESSWIFI

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Qualnet Simulation: Varying # flows in short distance

networks

10 20 30 40 50 60 70

0 2 4 6 8 10 12 14 16 18

Total

thro

ughp

ut (M

bps)

Number of flows

CRMA-VFCRMACSMAR_ACCESSWIFI

CRMA-VF > CRMA > other schemes.

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Qualnet Simulation:Varying data rate

CRMA significantly out-performs the other schemes and its benefit increases with data rate.

0.3 0.4 0.5 0.6 0.7 0.8 0.9

1

1 2 4 8 16 32 64

Norm

alize

d Thr

ough

put

Data Rate (Mbps)

CRMACSMAR_ACCESSWIFI

Related Work• Decoding collisions

– Successive interference cancellation, ZigZag, analog network coding, …

– CRMA: a MAC protocol based on ability to decode collisions

• CDMA– Synchronous CDMA: handful orthogonal codes and

requires tight synchronization– Asynchronous CDMA: suffers Multiple Access

Interference (MAI)• Channel assignment and channel hopping

– Try to avoid collisions– CRMA: a new perspective on spectrum sharing

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Summary of CRMA• CRMA: a new direction for spectrum sharing

– A new encoding and decoding scheme– A new MAC protocol based on it– Experimental evaluation to show it can achieve

high efficiency without fine-grained coordination

• Future work – Robust to channel estimation errors– Effectively support high data rate– More graceful degradation as # transmissions

exceeds # channels

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Our Work• Spectrum sharing

– Develop an efficient MAC for multiple nodes to share the medium

• Spectrum access– Develop and implement multi-point to

multi-point MIMO

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Multi-point to Multi-point MIMO in WLANs

Joint work with Sangki Yun and Apurv Bhartia

Under submission

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Overview• Motivation

– MIMO promises a dramatic capacity increase

• 802.11n, 802.11ac, …– But usually limited by # antennas at an AP

– Multi-point to multi-point MIMO achieves a

higher capacity and overcomes the limitations

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Multi-point to Multi-point MIMO

AP1 AP2 APn

Client1 Client2 Clientn

n concurrent downlinkor uplink streams

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Multi-point to Multi-point MIMO

AP1 AP2 APn

Client1 Client2 Clientn

n concurrent downlink

Is it feasible?

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Our Contributions• Demonstrate the feasibility and

effectiveness of multi-point to multi-point MIMO on USRP and SORA

• Design multi-point to multi-point MIMO-aware MAC

• Extensions to support general traffic and network conditions

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Our Contributions• Demonstrate the feasibility and

effectiveness of multi-point to multi-point MIMO on USRP and SORA

• Design multi-point to multi-point MIMO-aware MAC

• Extensions to support general traffic and network conditions

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Point-to-Point MIMOAP

Client

[𝑦 1𝑦 2]=H [𝑝1

𝑝2]

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Multi-point to Multi-point MIMO: Downlink

AP1 AP2 APn

Client1 Client2 Clientn

n concurrent downlink

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Multi-point to Multi-point MIMO in Downlink

• Clients can not cooperate• APs perform joint precoding (e.g.,

zero-force beamforming)– Such that the combined precoded

signals arriving at the clients can be modulated as usual

– Precoding matrix W = H-1

– Received signal HWp=HH-1p= p• Is it feasible in practice?

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Practical Challenges• Each AP has its own clock different

carrier frequency offset (CFO)

• Challenges– Phase synchronization– Time synchronization

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Phase Synchronization (I)• Motivation

Transmitted signals:

Received signals:

The effective channel is not diagonalized as intended and the receiver cannot decode it!

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Phase Synchronization (II)• Phase is a function of time and CFO

• Measure CFO and initial phase of lead sender

LTS

Leader AP L ead er A P

Time

LTS

LTS Co -senders

Client

Feed back

T im eC li tb ac k

DATA symbols

DATA symbols

DATA symbols

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Phase Synchronization (III)• Synchronizing the initial phase

– Multiply precoded signals by

• Synchronizing remaining signals’ phase– Multiply n-th signal by

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Distributed MIMO in UplinkShare the received signals over the Ethernet

Client 1

AP 2AP 1

Client 2

APs share their received signals and jointly decodey1 = h11 p1 + h12 p2

y2 = h21 p1 + h22 p2

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MAC Design• Rate adaptation• Support ACKs• Deal with losses and collisions• Schedule transmissions• Limit Ethernet overhead• Obtain channel estimation

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MAC Design• Rate adaptation• Support ACKs• Dealing with losses and collisions• Scheduling transmissions• Limiting Ethernet overhead• Obtaining channel estimation

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Rate Adaptation (I)• Challenges

– Receiver receives a combination of signals from all the transmitting APs

– Per link SNR based rate adaptation does not work

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Rate Adaptation (II)

• Error vector magnitude (EVM) based SNR– Distance between the received symbol and

the closest constellation point– Incorporate frequency diversity by

computing this metric for each subcarrier to derive BER and effective SNR

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Support ACKs• ACKs enjoy the same spatial

multiplex in the reverse direction• Downlink

– Data: APs multiplex to clients via precoding

– ACK: clients multiplex to APs and APs jointly decode

• Uplink– Data: clients multiplex to APs and APs

jointly decode– ACK: APs multiplex to clients via

precoding

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Evaluation• Implement downlink on USRP

– SORA transmitter has random initial phase and makes it hard to support phase sync.

• Implement uplink on SORA– Both USRP and SORA support uplink and

use SORA for higher capacity

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Downlink Phase Misalignment

0

0.2

0.4

0.6

0.8

1

0.00 0.05 0.10 0.15

CDF

Phase misalignment ( radian angle )

Median phase misalignment is 0.075 radianand reduces SNR by 0.4 dB.

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Downlink Throughput

Downlink throughput almost linearly increases with # antennas across different APs or clients.

0

10

20

30

40

116QAM

2QPSK

3QPSK

4BPSK

5BPSK

Throu

ghpu

t (Mbp

s)

Location ID

individual2x2 downlink3x3 downlink

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Uplink Throughput

0

10

20

30

116QAM

2QPSK

3QPSK

4 5BPSK

Throu

ghpu

t (Mbp

s)

Location ID

individual2x2 uplink3x3 uplink

Uplink throughput almost linearly increases with # antennas across different APs or clients.

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Ran adaptation in Downlink

0

1

2

0 100 200 300

Throu

ghpu

t (Mbp

s)

Packet Trace Index (x 20)

Best fixed ESNR

Achieves close to 96% throughput of best fixed rate.

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Summary• First step towards multi-point to

multi-point MIMO

• This new transmission method opens up– New network optimization – New network management– New applications

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Thank you!

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Qualnet Simulation:Varying payload size

CRMA out-performs the other schemes and its benefit is larger for small packets.

10 20 30 40 50 60 70 80

100 200 400 600 1000 1200 1500

Total

thro

ughp

ut (M

bps)

Payload size (Bytes)

CRMACSMAR_ACCESSWIFI

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Testbed Experiments (Cont.)

Phase shift correctly compensate for the offset signal.

Ethernet

Client 1

Commander

Client 2 Client n

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Frequency Synchronization• Motivation

– Each AP has itw own clock and has different carrier frequency offset (CFO)

• Our approach– Measurement phase: all senders

transmit preambles in order and let a receiver estimate CFO

– Compensation phase: each transmiter except the lead sender multiples to its data signals, where n is the signal index

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Multi-point to Multi-point MIMO in Downlink

• Clients can not cooperate• APs perform joint precoding (e.g.,

zero-force beamforming)– Such that the combined precoded

signals arriving at the clients can be modulated as usual

– Precoding matrix W = HT(HHT)-1

– Received signal HWx=HHT(HHT)-1x= x• Is it feasible in practice?

70

Overview• Motivation

– MIMO promises a dramatic capacity increase

• 802.11n, 802.11ac, …– But usually limited by # antennas at an AP

– Multi-point to multi-point MIMO achieves a

higher capacity and overcomes the limitations

• State-of-art– Theory: significant work on distributed

MIMO– Practice: not clear how well it works in

reality