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Cooperative Diversity Techniques for Wireless Networks. Arun ‘Nayagam Wireless Information Networking Group (WING) Department of Electrical and Computer Engineering University of Florida. Introduction. Antenna arrays commonly used to achieve receive diversity
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Cooperative Diversity Techniquesfor Wireless Networks Arun ‘Nayagam Wireless Information Networking Group (WING) Department of Electrical and Computer Engineering University of Florida
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
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
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
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
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
Information Theory (contd.) • Information theory: The Relay Channel • Cover and El Gamal (1979) : • -- Facilitation- • - Cooperation (limited by rate between source and relay)--- Observation
Information Theory (contd.) • Information theory: The Relay Channel • Cover and El Gamal (1979) : • -- Facilitation - • - Cooperation(limited by rate between source and relay)--- Observation
Information Theory (contd.) • Information theory: The Relay Channel • 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 Practice • Coded Cooperative Diversity Schemes • Hunter and Nosratinia (2002) : Cooperation using RCPCs Coding Decode and Forward
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.
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
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
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
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
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
Overhead Comparisons 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 relays • Low overhead • Close 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 - ‘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
Reliability Filling 3 node MRC example 8 7 13 15 6 6 13 9 11
Reliability Filling (contd.) 3 node reliability filling example (T=10) 8 7 13 15 6 6 13 9 11
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
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 Results • Performance 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 • Next Step • Analysis of generalized reliability filling ? • Space-time overlays for collaborative decoding ?
Simulation Results • Performance of proportional transmission with eight • cooperating nodes