Tài liệu Distributed MIMO - Patrick Maechler docx

Motivation: Collaboration scheme achieving optimal capacity scaling 2... Tse, ”Hierarchical Cooperation Achieves Optimal Capacity Scaling in Ad Hoc Networks”, IEEE Trans... Cooperation S

Distributed MIMO

Patrick Maechler

April 2, 2008

1. Motivation: Collaboration scheme achieving

optimal capacity scaling

2. Distributed MIMO

3. Synchronization errors

4. Implementation

5. Conclusion/Outlook

Throughput Scaling

● Scenario: Dense network

– Fixed area with n randomly distributed nodes

at rate R(n) Total throughput T(n) = nR(n)

● TDMA/FDMA/CDMA: T(n) = O(1)

● Multi-hop: T(n) = O( )

– P Gupta and P R Kumar, “The capacity of wireless networks,” IEEE Trans Inf Theory, vol 42,

no 2, pp 388–404, Mar 2000.

● Hierarchical Cooperation: T(n) = O(n)

– Ayfer Özgür, Olivier Lévêque and David N C Tse, ”Hierarchical Cooperation Achieves Optimal Capacity Scaling in Ad Hoc Networks”, IEEE Trans Inf Theory, vol 53, no 10, pp 3549-3572, Oct 2007

n

Cooperation Scheme

● All nodes are divided into clusters of equal size

● Phase 1: Information distribution

– Each node splits its bits among all nodes in its cluster

Cooperation Scheme

● Phase 2: Distributed MIMO transmissions

– All bits from source s to destination d are sent

simultaneously by all nodes in the cluster of the source node s

Cooperation Scheme

● Phase 3: Cooperative decoding

– The received signal in all nodes of the destination cluster

is quantized and transmitted to destination d

Hierarchical Cooperation

● The more hierarchical levels of this scheme are

applied, the nearer one can get to a troughput linear

in n

1. Motivation: Collaboration scheme achieving

optimal capacity scaling

2. Distributed MIMO

3. Synchronization errors

4. Implementation

5. Conclusion/Outlook

Distributed MIMO

● Independent nodes collaborate to operate as

distributed multiple-input multiple-output system

● Simple examples:

– Receive MRC (1xN r ):

– Transmit MRC (N t x1, channel knowledge at transmitter)

– Alamouti (2xN r ): STBC over 2 timeslots

● Diversity gain but no multiplexing gain

w x h

y h

h x

n x h

*

Alamouti, S.M., "A simple transmit diversity technique for wireless communications ," Selected Areas in Communications, IEEE Journal on , vol.16, no.8, pp.1451-1458, Oct 1998

MIMO Schemes

● Schemes providing multiplexing gain:

optimality (higher receiver complexity)

n x H

y  

[1] P W Wolniansky, G J Foschini, G D Golden, and R A Valenzuela V-BLAST: An architecture for realizing very high data rates over the rich scattering wireless channel.

In ISSSE International Symposium on Signals, Systems, and Electronics, pages 295-300, Sept 1998.

[2] G Foschini Layered space-time architecture for wireless communication in a fading environment when using multi-element antennas Bell Labs Technical Journal, 1(2):41-59, 1996.

MIMO Decoders

● Maximum likelihood:

● Zero Forcing / Decorrelator

– Balances noise and multi stream interference (MSI)

● Successive interference cancelation (SIC)

* 1

* ) (

I SNR

H H

min arg

Error Rate Comparison

● MMSE-SIC is the best linear receiver

● ML receiver is optimal

1. Motivation: Collaboration scheme achieving

optimal capacity scaling

2. Distributed MIMO

3. Synchronization errors

4. Implementation

5. Conclusion/Outlook

● Each transmit node has its own clock and a different propagation delay to destination

– No perfect synchronization possible.

 Shifted peaks at receiver

– What is the resulting error, if any?

Simulation results

● Flat fading channel assumed at receiver

● No large BER degradiation for timing errors up to 20% of symbol duration (raised cosine with )  0 22

● Synchronization errors make flat channels appear

as frequency-selective channels

● Receivers for freq.-sel channels can perfectly

compensate synchronization errors

● Implementation cost is much higher!

Time Shift - SIC

● Promising results for SIC receiver that samples each stream at the optimal point

– Compensation of synchronization errors possible for independent streams (V-BLAST)

1. Motivation: Collaboration scheme achieving

optimal capacity scaling

2. Distributed MIMO

3. Synchronization errors

4. Implementation

5. Conclusion/Outlook

● Complex decoders required

All linear decoders need matrix inversion

● BEE2 implementation of 2x1 Alamouti (MISO) scheme currently under development

1. Motivation: Collaboration scheme achieving

optimal capacity scaling

2. Distributed MIMO

3. Synchronization errors

4. Implementation

5. Conclusion/Outlook

● BEE2 implementation of MIMO receiver

● Frequency synchronization methods

● Measure achievable BER on real system for given synchronization accuracy at transmitters