cooperative diversity techniques for wireless networks
TRANSCRIPT
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
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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.)
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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
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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
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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
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Information Theory (contd.)Information theory: The Relay Channel
•Cover and El Gamal (1979) : - - Facilitation - - Cooperation (limited by rate between source and relay) - -
- Observation
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Information Theory (contd.)Information theory: The Relay Channel
•Cover and El Gamal (1979) : - - Facilitation - - Cooperation (limited by rate between source and relay) - -
- Observation
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Information Theory (contd.)Information theory: The Relay Channel
•Cover and El Gamal (1979) : - - Facilitation - - Cooperation (limited by rate between source and relay) - -
- Observation
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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)
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Information Theory (contd.)
Decode and Forward
Amplify and Forward
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Information Theory (contd.)
Drawbacks
Based on repetition coding High overhead
Not scalable to large cooperating groups.
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From Theory to PracticeCoded Cooperative Diversity Schemes
•Hunter and Nosratinia (2002) : Cooperation using RCPCs
Decode and Forward
Coding
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From Theory to Practice (contd.)Coded Cooperative Diversity Schemes
•Zhao and Valenti (2003) : Cooperation using Turbo Codes
Decode and Forward
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Coded Cooperation (contd.)
Drawbacks
Rely on full decoding at the relay cannot achieve full diversity!
Not scalable to large cooperating groups.
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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)
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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
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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)
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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 reliabilities3 iterations of 5% LRB exchange
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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
information3 iterations of 10% MRB exchange
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Overhead Comparisons
45 %82.5 %10
45 %45.0 %5
45 %22.5 %2
45 %157.5 %20
MRB LRB-2Number of Nodes
Overhead per Receiver
(w.r.t MRC)
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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
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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.
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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)
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15 6 6 13 9 11
8 7 13
Reliability Filling 3 node MRC example
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15 6 6 13 9 11
8 7 13
Reliability Filling (contd.)3 node reliability filling example (T=10)
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•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.)
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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
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Simulation ResultsPerformance of reliability filling with eight cooperating nodes
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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 ?
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Simulation ResultsPerformance of proportional transmission with eight
cooperating nodes
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Numerical Results