Interference in MANETs: Friend or Foe? Andrea Goldsmith 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 Nodes can cooperate to forward data Can introduce feedback to improve performance

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

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

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

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

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

Proposed Transmission Strategy To allow each receiver to decode part of the other node’s message  reduces interference Cooperation at CR TX Cooperation atCR TX Removes the NCR interference at the CR RX Cooperation at CR TX Precoding against interference at CR TX To help in sending NCR’s message to its RX It is CLEAR that it needs Precoding against interference at CR TX Rate splitting We optimally combine these approaches into one strategy

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

Upper Bounds 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 How far are the achievable rates from the outer bound?

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

Exploiting Interference through Relaying TX1 TX2 relay RX2 RX1 X1 X2 Y3=X1+X2+Z3 Y4=X1+X2+X3+Z4 Y5=X1+X2+X3+Z5 X3= f(Y3) 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

Achievable Rates with Interference Forwarding dest1 dest2 encoder 1 encoder 2 relay 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

Capacity Gains from Interference Forwarding

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

Diversity-Multiplexing Tradeoff in Point-to-Point MIMO Channels - Multiplexing gain r: - Diversity gain d

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

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)‏

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

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 2 consecutive hops with min. diversity gain

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

Exploiting Interference reduces End-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

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?

Final Comments: Startup Lessons Learned People in industry read our papers and implement our ideas 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