Breaking the Interference Barrier

1. Breaking the Interference Barrier David Tse Wireless Foundations University of California at Berkeley Mobicom/Mobihoc Plenary Talk September 13, 2007 TexPoint fonts used in EMF: AAAA

2. The Interference Barrier • Lots of recent advances in physical layer wireless communication (multiple antennas MIMO, space-time codes, opportunistic scheduling, turbo codes, hybrid ARQ….) • From theory to practice in a decade. • Gains pertain mainly to point-to-point or multiple access performance. • But performance of many wireless systems ultimately limited by interference. • Breaking this interference barrier will be the next step.

3. Examples of Interference Barrier • Cellular networks: inter-cell interference • Ad hoc networks: interference from simultaneous transmissions • Wireless LANS: interference between adjacent networks • Cognitive networks: interference between primary and secondary users and between multiple secondary systems

4. Talk Outline We discuss several speculative approaches to break the interference barrier: • cooperative distributed MIMO • exploiting mobility to localize interference • interference alignment Key message: Solving the interference problem requires a combination of physical layer and architectural ideas.

5. Traditional Interference Management in Cellular Systems • Narrowband (eg. GSM) • Inter-cell interference made negligible at the price of poor frequency reuse • Wideband (eg. CDMA, OFDM) • Universal frequency reuse but system is interference-limited.

6. Example: WiMax is Interference-Limited SIR = 2 dB SNR = 20 dB Universal Reuse : 6 dominant interferers SIR to SNR gap = 18dB Source: Intel WiMax simulations

7. Fractional Reuse: A Partial Solution • Universal reuse for cell-interior users. • Orthogonal bands for cell-edge users. • But cell-edge users are still the bottleneck. f2 f3 f0 f0

8. Tale of Two Cell-Edge Users • keep users on orthogonal bands: lose half the effective bandwidth but avoid interference • Best of both worlds? • Yes, base-stations can cooperate to form a distributed MIMO array.

9. MIMO in One Slide Signal space at Rx array (M=2) direction of signal from Tx antenna 1 M by M MIMO system with a sufficiently random channel supports M simultaneous data streams.

10. Infrastructure Cooperation • Base stations cooperate to form a macro-array to jointly decode in the uplink and transmit in the downlink. • Turns harmful inter-cell interference into useful signals • High-speed connectivity to a central processing unit.

11. Simulation in a Hexagonal Cellular System cooperation single-cell processing (Alessandro et al 06) Rise-over-thermal = 6dB 2 Rx antennas per BS

12. Cooperation in Ad Hoc Networks • Capacity of ad hoc networks limited by mutual interference between simultaneous transmissions. • How can cooperation between mobiles improve capacity? • Unlike infrastructure-based cellular systems, such cooperation comes at an over-the-air transmission cost. • Will the overhead swamp the cooperation gain?

13. Scaling Law Formulation (Gupta-Kumar 00) • n nodes randomly located in a fixed area. • n randomly assigned source-destination pairs. • Each S-D pair demands the same data rate. • How does the total throughput T(n) of the network scale with n?

14. How much can Cooperation Help? Courtesy: David Reed ? Can we get linear scaling with more sophisticated cooperation? Arbitrarily closely. (Ozgur,Leveque,T. 06)

15. Gupta-Kumar Capacity is Interference-Limited • Long-range transmission causes too much interference. • Multi-hop means each packet is transmitted many times. • To get linear scaling, must be able to do many simultaneous long-range transmissions. • How to deal with interference? • A natural idea: distributed MIMO! • But cooperation overhead is bottleneck. What kind of cooperation architecture minimizes overhead?

16. A 3-Phase Scheme • Divide the network into clusters of size M nodes. • Focus first on a specific S-D pair. • source s wants to send M bits to destination d. Phase 1 : Setting up Tx cooperation: 1 bit to each node in Tx cluster Phase 2: Long-range MIMO between s and d clusters. Phase 3: Each node in Rx cluster quantizes signal into k bits and sends to destination d.

17. Parallelization across S-D Pairs Phase 1: Clusters work in parallel. Sources in each cluster take turn distributing their bits. Total time = M2 Phase 2: 1 MIMO trans. at a time. Total time = n Phase 3: Clusters work in parallel. Destinations in each cluster take turn collecting their bits. Total time = kM2

18. Back-of-the-Envelope Throughput Calculation total number of bits transferred = nM total time in all three phases = M2 + n + kM2 Throughput: bits/second Optimal cluster size Best throughput: 

19. Further Parallelization • In phase 1 and 3, M2 bits have to be exchanged within each cluster, 1 bit per node pair. • Previous scheme exchanges these bits one at a time (TDMA), takes time M2. • Can we increase the spatial reuse ? • Can break the problem into M sessions, each session involving M S-D pairs communicating 1 bit with each other: cooperation = communication • Any better scheme for the small network can build a better scheme for the original network.

20. Recursion Lemma: A scheme with thruput Mb for the smaller network yields for the original network a thruput:

21. MIMO + Hierarchical Cooperation-> Linear Scaling Long-range MIMO Setting up Tx cooperation Cooperate to decode . By having many levels of hierarchy, we can get as close to linear scaling as we wish.

22. Linear Scaling with Less Work? • Linear scaling means that the capacity of the network is not significantly limited by interference. • But the hierarchical scheme requires tracking of channel information as well as significant cooperation between nodes. • Can one get linear scaling with less work? • Yes, if nodes are mobile.

23. Mobility Can Help! (Grossglauser and T. 01) Suppose nodes move randomly and independently. A linear throughput can be achieved if one is willing to wait. Throughput is averaged over the time-scale of mobility.

24. Direct Communication Does Not Work The source and destination are nearest neighbors only O(1/n) of the time.

25. Detour: Multiuser Diversity in Cellular Systems By opportunistically scheduling transmissions to users with instantaneously strong channels, multiuser diversity gain is achieved.

26. Multiuser Diversity via Relaying Multiuser diversity created artificially using all other nodes as relays.

27. Phase I: Source to Relays • At each time slot, source relays a packet to nearest neighbor. • Different packets are distributed to different relay nodes.

28. Phase 2: Relays to Destination • Steady state: all nodes have packets destined for D. • Each relay node forwards packets to D only when it gets close.

29. Phase I and II Staggered • O(1) throughput from S to D • Communication is confined to nearest neighbors, but each packet goes through at most two hops • Load is distributed evenly between all relay nodes, enabling every S-D pair to follow the same strategy.

30. Linear Scaling without Cooperation? • The two approaches rely on some sort of cooperation to mitigate interference. • Is cooperation really necessary?

31. Spectrum Sharing Revisited • Working assumption: only one transmission on each time-frequency-space resource. • Implicit assumption: spectrum is a common “ether” shared by all. • But is this metaphor correct? Rx 1 Tx 1 Channel11 Channel21 Tx 2 Rx 2 : : Channel2n Tx n Channelnn Rx n

32. Interference Alignment Example (Cadambe-Jafar 07) • All direct channels delay transmission by 1 symbol time. • All cross channels delay by 2 symbol times. • Each user can transmit every other symbol time, yet no interference! • What matters is what happens at the receiver, and each receiver sees a different picture. • So all the interference can be aligned onto one symbol time and yet the signal is orthogonal to the interference. Rx 1 Tx 1 Channel11 Channel12 Tx 2 Rx 2 : : Channel2n Tx n Channelnn Rx n

33. Interference Alignment: Geometry Tx 1 H11 Rx 1 Rx 2 Tx 2 Tx 3 Rx 3

34. Recurring Theme Channel diversity is a key resource for breaking the interference barrier. The three approaches can be viewed as ways to exploit this diversity: • Hierarchical cooperation to exploit MIMO gain. • Mobility and relaying to exploit multiuser diversity gain. • Interference alignment to exploit diversity between direct and cross channels.

35. Conclusions • Breaking the interference barrier is the next step in the evolution of wireless systems. • We focus on speculative ideas in this talk. • Hopefully they provide some food for thought for system builders.