wireless information networking group cooperative diversity techniques for wireless networks arun...

Wireless Information Networking Group

Cooperative Diversity Techniquesfor Wireless Networks

Arun ‘Nayagam

Wireless Information Networking Group (WING)Department of Electrical and Computer Engineering

University of Florida

Wireless Information Networking GroupWireless Information Networking Group

Antenna arrays commonly used to achieve receive diversity

Size of the antenna array must be several times the wavelength of the RF carrier

Antenna arrays are an unattractive choice to achieve receive diversity in small handsets/cellular

phones

Alternative: Network-Based Approaches:

An antenna array is inherently present in any wireless network!

DISTRIBUTED ARRAY Different nodes in the network can act like

elements of an antenna array

Introduction


CHALLENGES Array elements are not physically connected Traditional combining techniques (MRC, EGC)

require large amount of information to be sent to the combining node

GOAL Design scalable schemes for achieving receive

diversity with small amount of information exchange

Introduction (contd.)


PreliminariesError Correcting Codes

Adds structured redundancy to the information bits: Exploits temporal diversity!

Example: Repetition code:

Coding

Information bit Coded bits

Other examples: Block codes, Trellis-based codes

Coding

Parity bitsSystematic bits


Preliminaries (contd.)Soft-input Soft-output Decoding

Log-MAP Decoder

a priori LLR+

Received symbols (input)

a posteriori LLR (output)

LLRs referred to as soft information

Hard-decision=sign(output LLR)

Reliability = |output LLR| Reliability is an indication of the

correctness of the hard-decision


User-Cooperation: The early daysInformation theory: The Relay Channel

First studied by van der Meulen (1968) Coding theorems proved by Cover and El Gamal (1979)

Source Destination

Relay

Principle Intermediate nodes called relays process information from the source and retransmit “refinement’’ information to the destination


Information Theory (contd.)Information theory: The Relay Channel

Cover and El Gamal (1979) : - - Facilitation - - Cooperation (limited by rate between source and relay)

- - - Observation



Cover and El Gamal (1979) : - - Facilitation - - Cooperation (limited by rate between source and

relay) - - - Observation



Cover and El Gamal (1979) : - - Facilitation - - Cooperation (limited by rate between source and relay)

- - - Observation


Information Theory (contd.)Other results

Sendonaris, Erkip and Aazhang (2003) :User-cooperation increases sum capacity with knowledge of channel phase at transmitter

Laneman, Wornell and Tse (2003) :

Impossible to increase sum capacity without knowledge of channel at the transmitter

Cooperation using “dumb” relays Decode-and-Forward (does not achieve full diversity) Amplify-and-Forward (full diversity guaranteed)


Information Theory (contd.)

Decode and Forward

Amplify and Forward


Information Theory (contd.)

Drawbacks

Based on repetition coding High overhead

Not scalable to large cooperating groups.


From Theory to PracticeCoded Cooperative Diversity Schemes

Hunter and Nosratinia (2002) : Cooperation using RCPCs

Decode and Forward

Coding


From Theory to Practice (contd.)Coded Cooperative Diversity Schemes

Zhao and Valenti (2003) : Cooperation using Turbo Codes

Decode and Forward


Coded Cooperation (contd.)

Drawbacks

Rely on full decoding at the relay cannot achieve full diversity!

Not scalable to large cooperating groups.


Design cooperative schemes that do not depend on full decoding at any of the relay

achieve full diversity

Cooperation overhead should be small

The scheme should easily scale to large groups of cooperating nodes

Objective (Revisited)


System Model

Distant Transmitter Cluster of Receiving Nodes

COLLABORATIVE DECODINGNodes iterate between a process of information exchange and decoding

SCENARIOS Base station communicating with a group of small

mobile units Battleship broadcasting a message to a

platoon of soldiers


Cooperative Diversity thro’ Reliability Exchange

IDEA Bits with low reliabilities are more likely to be

incorrect and hence need information (from other nodes) to correct them

Bits with high reliabilities are likely to be correct and hence information about these bits can be shared with other nodes

- ‘Nayagam, Shea, Wong, Li (WCNC 2003)


Reliability Exchange (contd.)

Each node identifies the set of least reliable bits and requests for information about these bits from other nodes

Least Reliable Bit (LRB) Schemes

Other nodes reply with their estimate of the

APP LLR (soft output) for those bits

Requester and the other nodes use the

received information as a priori LLRs

For the nodes other than the requester,

information is obtained for a set of bits with random reliabilities

3 iterations of 5% LRB exchange


Reliability Exchange (contd.)

Each node identifies the set of most reliable bit and broadcasts soft output for these bits to other nodes

Most Reliable Bit (MRB) Schemes

Other nodes use the received

information as a priori LLRs

LLR APPs are broadcast for the set of MRBs

about which information was not sent by any node in the previous iteration

In each iteration a new set of bits get a

priori information

3 iterations of 10% MRB exchange


Overhead Comparisons

Number of Nodes

LRB-2 MRB

2 22.5 % 45 %

5 45.0 % 45 %

10 82.5 % 45 %

20 157.5 % 45 %

Overhead per Receiver

(w.r.t MRC)


Reliability Exchange (contd.)MRB and LRB schemes lie in the realm of decode-and-forward;

Relay transmission consists of soft-information

Does not require correct decoding of entire block; Even if few bits decode incorrectly, useful information about other bits can be extracted

Advantages:Scales easily to multiple relaysLow overheadClose to MRC performance on AWGN channels

Disadvantage:Poor performance on block-fading channels


Design Guidelines

In order to obtain full diversity it is necessary to exchange information closest to the RF front end i.e., the received symbol values (soft demodulator outputs).

More information needs to be combined for unreliable trellis sections whereas more reliable sections need less information

Nodes with good channels should share more information than nodes with bad channels.


Water-filling in the Reliability Domain

The cooperation process be controlled by a genie with knowledge of the reliabilities of the information bits at all relays

Genie selects bits from various nodes for combining based on water-filling in the reliability domain : Reliability Filling

An idealized technique similar to MRC

Number of coded symbols combined per - trellis section is reduced based on the - reliability

- ‘Nayagam, Shea, Wong (Allerton 2003)


15 6 6 13 9 11

8 7 13

Reliability Filling 3 node MRC example


15 6 6 13 9 11

8 7 13

Reliability Filling (contd.)3 node reliability filling example (T=10)


Si is the set of all combinations of nodes such that - the sum of reliabilities of bit i at those nodes - exceeds a threshold T

Ni is the minimum number of nodes such that the sum of reliabilities of bit i at those nodes exceeds T.

When Si = , coded symbols are combined from all nodes

When Si ≠ , coded symbols are combined from the smallest number of nodes such that the sum of reliabilities from those nodes is maximized for bit i.

For different trellis sections, information is combined from a different set of nodes

Reliability Filling (contd.)


Simulation Results

Example of reliability filling with eight cooperating nodes

Non-systematic, non-recursive convolutional codes with generator polynomials 1+D2 and 1+D+D2

Block size =900 bits

BPSK modulation

Block fading channel


Simulation ResultsPerformance of reliability filling with eight cooperating nodes


Work completed

Developed Proportional Transmission : A practical iterative technique that mimics the principles of reliability filling

Developed a mathematically tractable - expression for the density function of soft - information to be used in the analysis of - reliability filling

Analysis of two node reliability filling

Analysis of generalized reliability filling ?

Next Step

Space-time overlays for collaborative decoding ?


Simulation ResultsPerformance of proportional transmission with eight

cooperating nodes


Numerical Results

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