Cooperative Diversity Techniques for Wireless Networks

1. Cooperative Diversity Techniquesfor Wireless Networks Arun ‘Nayagam Wireless Information Networking Group (WING) Department of Electrical and Computer Engineering University of Florida

2. Introduction • 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 Wireless Information Networking Group

3. Introduction (contd.) • 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

4. Preliminaries • Error 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 Systematic bits Parity bits

5. Preliminaries (contd.) Soft-input Soft-output Decoding Log-MAP Decoder a posteriori LLR (output) a priori LLR + Received symbols (input) • 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

6. User-Cooperation: The early days • Information theory: The Relay Channel • First studied by van der Meulen (1968) • Coding theorems proved by Cover and El Gamal (1979) Relay Source Destination • Principle • Intermediate nodes called relays process • information from the source and retransmit • “refinement’’ information to the destination

7. Information Theory (contd.) • Information theory: The Relay Channel • Cover and El Gamal (1979) : • -- Facilitation- • - Cooperation (limited by rate between source and relay)--- Observation

8. Information Theory (contd.) • Information theory: The Relay Channel • Cover and El Gamal (1979) : • -- Facilitation - • - Cooperation(limited by rate between source and relay)--- Observation

9. Information Theory (contd.) • Information theory: The Relay Channel • Cover and El Gamal (1979) : • -- Facilitation - • - Cooperation (limited by rate between source and relay)--- Observation

10. 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)

11. Information Theory (contd.) Decode and Forward Amplify and Forward

12. Information Theory (contd.) • Drawbacks • Based on repetition coding High overhead • Not scalable to large cooperating groups.

13. From Theory to Practice • Coded Cooperative Diversity Schemes • Hunter and Nosratinia (2002) : Cooperation using RCPCs Coding Decode and Forward

14. From Theory to Practice (contd.) • Coded Cooperative Diversity Schemes • Zhao and Valenti (2003) : Cooperation using Turbo Codes Decode and Forward

15. Coded Cooperation (contd.) • Drawbacks • Rely on full decoding at the relay cannot achieve full diversity! • Not scalable to large cooperating groups.

16. Objective (Revisited) • 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

17. System Model Distant Transmitter Cluster of Receiving Nodes • COLLABORATIVE DECODING • Nodes 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

18. Cooperative Diversity thro’ Reliability Exchange - ‘Nayagam, Shea, Wong, Li (WCNC 2003) • 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

19. Reliability Exchange (contd.) Least Reliable Bit (LRB) Schemes • Each node identifies the set of least reliable bits and requests for information about these bits from other nodes • Other nodesreply 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 otherthan the requester, information is obtained for a set of bits with random reliabilities 3 iterations of 5% LRB exchange

20. Reliability Exchange (contd.) Most Reliable Bit (MRB) Schemes • Each node identifies the set of most reliable bit and broadcasts soft output for these bits to other nodes • 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

21. Overhead Comparisons Overhead per Receiver (w.r.t MRC)

22. 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 relays • Low overhead • Close to MRC performance on AWGN channels • Disadvantage: • Poor performance on block-fading channels

23. 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.

24. Water-filling in the Reliability Domain - ‘Nayagam, Shea, Wong (Allerton 2003) • 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

25. Reliability Filling 3 node MRC example 8 7 13 15 6 6 13 9 11

26. Reliability Filling (contd.) 3 node reliability filling example (T=10) 8 7 13 15 6 6 13 9 11

27. Reliability Filling (contd.) • 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

28. 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

29. Simulation Results • Performance of reliability filling with eight cooperating nodes

30. 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 • Next Step • Analysis of generalized reliability filling ? • Space-time overlays for collaborative decoding ?

31. Simulation Results • Performance of proportional transmission with eight • cooperating nodes

32. Numerical Results