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ITMANET FLoWS Focus Talk

ITMANET FLoWS Focus Talk. Interference in MANETs: Friend or Foe? Andrea Goldsmith. Joint work with Dabora, Gunduz, Kramer, Liu, Maric, Poor, Shamai. MANET Characteristics. Peer-to-peer communications All transmissions interfere due to broadcast nature of radio Highly dynamic

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ITMANET FLoWS Focus Talk

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  1. ITMANET FLoWS Focus Talk Interference in MANETs: Friend or Foe? Andrea Goldsmith Joint work with Dabora, Gunduz, Kramer, Liu, Maric, Poor, Shamai

  2. MANET Characteristics Peer-to-peer communications All transmissions interfere due to broadcast nature of radio Highly dynamic Nodes can cooperate to forward data Can introduce feedback to improve performance

  3. Interference in MANETs • Radio is a broadcast medium • Radios in the same spectrum interfere • MANET capacity in unknown for all canonical networks with interference (even when exploited) • Z Channel • Interference Channel • Relay Channel • General MANETs

  4. Interference: Friend or Foe? Increases BER, Reduces capacity Multiuser detecion (MUD) and precoding can completely remove interference Common coding strategy to approach capacity • If treated as noise: Foe • If decodable or precodable: Neutral • Neither friend nor foe

  5. If exploited via coding, cooperation, and cognition Interference: Friend or Foe? Friend Especially in a network setting

  6. Exploiting Interference through Coding The Z Channel • Capacity of Z channel unknown in general • We obtain capacity for a class of Z channels • Korner/Marton technique applicable • Enough to consider superposition encoding • Han/Kobayashi achievable region is capacity region • Yields capacity for large class of Gaussian interference channels

  7. Exploiting Interference through Cognition RX1 CR RX2 NCR • Cognitive user has knowledge of other user’s message and/or encoding strategy • Used to help noncognitive transmission • Used to presubtract noncognitive interference Joint with Maric, Kramer, Shamai

  8. Proposed Transmission Strategy To allow each receiver to decode part of the other node’s message  reduces interference Cooperationat CR TX CooperationatCR TX Removes the NCR interference at the CR RX Cooperationat CR TX Precoding againstinterferenceat CR TX To help in sending NCR’smessage to its RX Precoding againstinterferenceat CR TX Rate splitting We optimally combine these approaches into one strategy

  9. More Precisely: Transmission for Achievable Rates The NCR uses single-user encoder The CRuses - Rate-splittingto allow receiver 2 to decode part of cognitive user’s message and thus reduce interference at that receiver - Precodingwhile treating the codebook for user 2 as interference to improve rate to its own receiver - Cooperationto increase rate to receiver 2 RX1 CR Rate split RX2 CR NCR NCR

  10. Upper Bounds How far are the achievable rates from the outer bound? • Follows from standard approach: • Invoke Fano’s inequality • Reduces to outer bound for full cooperation for R2=0 • Has to be evaluated for specific channels

  11. outer bound • our scheme • prior schemes Performance Gains from Cognitive Encoding • CR • broadcast bound

  12. RX1 TX1 X1 Y4=X1+X2+X3+Z4 relay Y3=X1+X2+Z3 X3= f(Y3) Y5=X1+X2+X3+Z5 X2 TX2 RX2 Exploiting Interference through Relaying • Relaying strategies: • Relay can forward all or part of the messages • Much room for innovation • Relay can forward interference • To help subtract it out Joint with Maric, Dabora, Medard

  13. encoder 1 dest1 relay encoder 2 dest2 Achievable Rates withInterference Forwarding for any distribution p(p(x1)p(x2,x3)p(y1,y2|x1,x2,x3) • The strategy to achieve these rates is: • - Single-user encoding at the encoder 1 to send W1 • - Decode/forward at encoder 2 and the relay to send message W2 • This region equals the capacity region when the interference is strong and the channel is degraded

  14. Capacity Gains fromInterference Forwarding

  15. Diversity-Multiplexing Tradeoffs inMulti-Antenna MANETs • Focus on (M1, M2, M3) • Quasi-static Rayleigh fading channel • Channel state known only at the receivers Joint with Gunduz, Poor

  16. Diversity-Multiplexing Tradeoff inPoint-to-Point MIMO Channels • - Multiplexing gain r: - Diversity gain d

  17. DMT for Full-duplex Relays • The relay can receive and transmit simultaneously • The DMT for (M1,M2,M3) full-duplex system is • The hop with the minimum diversity gain is the bottleneck • Achieved by decode-and-forward relaying with block Markov structure • Follows easily since DF achieves capacity

  18. Half-duplex Relay Static Protocols: • The source transmits during the first aT channel uses, 0<a<1 The relay tries to decode the message and forwards over the remaining (1-a)T channel uses: decode-and-forward with fixed allocation (fDF)‏ • The DMT for half-duplex (M1,M2,M3) system with fixed time allocation a • Optimize a for each multiplexing gain: decode-and-forward with variable allocation (vDF)‏

  19. Dynamic Decode-and-Forward (DDF) for Half-duplex Relay • Introduced by Azarian et al. (IT’05): • Relay listens until decoding • Transmits only after decoding • Achieves the best known DMT for half-duplex relay channels, yet short of the upper bound • We show: Achieves optimal DMT in multi-hop relay channels • Not piece-wise linear, no general closed form expression • Can be cast into a convex optimization problem

  20. Multiple Relay Networks • Multiple full-duplex relays: • DMT dominated by hop with minimum diversity gain. • Multiple half-duplex relays: • Odd and even numbered relays transmit in turn. • DDF (with time limitation for successive hops) is DMT optimal. • DMT dominated by 2consecutive hops with min. diversity gain

  21. End to End Distortion • Use antennas for multiplexing: • Use antennas for diversity ST Code High Rate High-Rate Quantizer Decoder ST Code High Diversity Low-Rate Quantizer Decoder We optimize the point on the DMT tradeoff curve to minimize distortion

  22. Exploiting Interference reducesEnd-to-End Distortion • Interference exploitation at the physical layer improves end-to-end distortion • We have proved a separation theorem for a class of interference channels • Separate source and channel coding optimal • We found the operating point on the DMT multihop region for minimal distortion • Under delay constraints, optimization needed

  23. Summary • Fundamental performance limits of MANETS requires understanding and exploiting interference • Interference can be exploited via coding/relaying, cooperation, or cognition • The right strategy depends on CSI, dynamics, network topology, and node capabilities. • Exploiting interference leads to higher capacity, more robustness, and better end-to-end performance • MIMO adds a new degree of freedom at each node • Use antennas for multiplexing, diversity, or IC?

  24. Final Comments: Startup Lessons Learned • Communication and network theory can be implemented in a real system in 3-9 months • Information Theory heavily influences current and next-gen. wireless systems (mainly at the PHY & MAC layers) • Idealized assumptions have been liberating • Wireless network design above PHY/MAC layer is ad-hoc • The most effective way to do tech transfer is to start a company People in industry read our papers and implement our ideas

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