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Hierarchical Cooperation for Linear Scaling in Wireless Networks

Explore how hierarchical cooperation achieves linear scaling in wireless networks through intelligent schemes and distributed MIMO. Learn about channel models, interference limitations, and optimal cluster sizes for maximizing throughput.

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Hierarchical Cooperation for Linear Scaling in Wireless Networks

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  1. Hierarchical Cooperation Achieves Linear Scaling in Ad Hoc Wireless Networks David Tse Wireless Foundations U.C. Berkeley AISP Workshop May 2, 2007 Joint work with Ayfer Ozgur and Olivier Leveque at EPFL TexPoint fonts used in EMF: AAAAAAAAAA

  2. Scaling of Ad Hoc Wireless Networks • 2n 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?

  3. How much can cooperation help? ? Gupta-Kumar 00

  4. Main Result Linear capacity scaling is achievable with intelligent cooperation. More precisely: For every  > 0, we construct a cooperative scheme that can achieve a total throughput T(n) = n1-.

  5. Channel Model • Baseband channel gain between node k and l: where rkl is the distance apart and kl is the random phase (iid across nodes). • is the path loss exponent (in power)

  6. Gupta-Kumar Capacity is Interference-Limited • Long-range transmission causes too much interference. • Nearest-neighbor transmission means each packet is transmitted times (multi-hop). • To get linear scaling, must be able to do many simultaneous long-range transmissions. • How to deal with interference? • A natural idea: distributed MIMO (Aeron & Saligrama 06).

  7. MIMO:Multiple Transmit Multiple Receive Antennas • The random MxM channel matrix allows transmission of M parallel streams of data. • Originally conceived for antennas co-located at the same device.

  8. Distributed MIMO • MIMO effect can be simulated if nodes within each cluster can cooperate. • But cooperation overhead limits performance. • What kind of architecture minimizes overhead?

  9. 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.

  10. 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

  11. 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:

  12. 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.

  13. Recursion Lemma: A scheme with thruput Mb for the smaller network yields for the original network a thruput: with optimal cluster size:

  14. MIMO + Hierarchical Cooperation-> Linear Scaling . Long-range MIMO Setting up Tx cooperation Cooperate to decode At the highest level hierarchy, cluster size is of the order n1- => near network-wide MIMO cooperation.

  15. Upper Bound • A simple upper bound: each source node has the benefit of all other nodes in the network cooperating to receive without interference from other nodes. • Each source gets a rate of at most order log n. • Yields an upper bound on network throughput • The hierarchical scheme is nearly information theoretically optimal.

  16. From Dense to Extended Networks • So far we have looked at dense networks, where the total area is fixed. • Another natural scaling is to keep the density of nodes fixed and the network covers an increasing area. • Here power limitation comes into play in addition to interference limitation.

  17. Main Results for Extended Networks • For path loss exponent  between 2 and 3: a modification of the hierarchical scheme can achieve: T(n) = n2-/2 We also showed this is the best that one can do. • For  > 3: Multihop is optimal:

  18. Conclusion • Hierachical cooperation allows network-wide MIMO without significant cooperation overhead. • Network wide MIMO achieves a linear number of degrees of freedom. • This yields a linear scaling law for dense networks. • Also yields optimal scaling law for extended networks.

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