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Challenges and Impact of User-provided Networking Technology

Challenges and Impact of User-provided Networking Technology. Shivendra S. Panwar Polytechnic Institute of NYU Brooklyn, NY. Some historical trends. Computing: From (centralized) mainframes to (distributed) personal computing, but… browsers and cloud computing

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Challenges and Impact of User-provided Networking Technology

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  1. Challenges and Impact of User-provided Networking Technology Shivendra S. Panwar Polytechnic Institute of NYU Brooklyn, NY

  2. Some historical trends Computing: From (centralized) mainframes to (distributed) personal computing, but… browsers and cloud computing Network protocols: From (centralized) circuit-switching and SNA, to (end-to-end principle based) TCP/IP, but …. consider the complexity of Cisco’s IOS What about using user nodes or so-called “end systems” as Layer X forwarding nodes?

  3. The last mile will soon not be a problem! Indeed, we may have an “all but the last mile problem” The spread of FTTX, 4G networks, IEEE 802.11n, femtocells Still some work (and monetary investment), but this is the trend This trend will further encourage user-based networking

  4. Good and bad user-provided networking ISP’s will encourage user-provided networking as long as they do not lose control P2P is generally bad, except when controlled by the ISP, then it is good WiFi is good if part of wireless carrier’s service; otherwise viewed as a competitor to 3G/4G Femtocells are by definition good!

  5. Some user-provided networking research projects at NYU-Poly • VoD service on fiber to the home, using ISP controlled P2P technology; relieves traffic on backbone network • FemtoHaul, a technique to use femtocells to relieve pressure on the cellular backhaul • Cellular networks have poor coverage and data rates at their edges; cooperative two-hop relaying with distributed space time coding can double or triple cell capacity • Using other devices’ battery still an issue; security is not an issue. - Being considered in IEEE 802.16m, LTE Advanced, IEEE 802.11 standards

  6. P2P for VoD on FTTX FTTX networks can provide VoD services to customers. The use of P2P technology on Set Top Boxes substantially reduces the traffic on the core network ring.

  7. FemtoHaul • Normal Scenario: • All data comes from the base station backhaul • FemtoHaul: • Obtain the data from an access point (WiFi, Femtocell) through another device Femtocell Backhaul Femtocell Base Station Backhaul • Current 3G/4G base station capacity: ~ 100Mbps • Four T1 lines typical for base station backhaul: ~ 6Mbps • Increasing number of data intensive applications • Adding backhaul infrastructure is very expensive (>15% of OPEX)

  8. Spatial multiplexing gain for cooperative MIMO The number of antennas integrated on portable devices is limited However, there might be several antennas at the base station Randomized processing provides a robust scheme for distributed cooperation Instead of diversity gain (Randomized Distributed Space Time Coding), can we achieve spatial multiplexing gain?

  9. Randomized Distributed Spatial Multiplexing • Randomized Distributed Spatial Multiplexing (R-DSM) is based on the Bell Lab Layered Space Time (BLAST) scheme • Assuming each mobile station equipped with only one antenna and base station has L antennas • The channel capacity between the relays and the destinations scales linearly with min(N,L), where N is the number of relays • How does R-DSM work in PHY? • Two-hop network: SISO transmission from source to relays first, followed by relays transmitting together to the destination using R-DSM. • Each relay independently generates a random coefficient and then transmits a weighted sum of the signals for each antennas in BLAST scheme

  10. Using cooperative R-DSM on the second hop

  11. Performance • Our results demonstrate that R-DSM scheme mimics a real MIMO system • Provides additional capacity when and where you need it

  12. Our Team • Multiple Poly faculty and students working on different aspects • Erkip (PHY, MAC, Video, Security, Implementation) • Knox (PHY, Implementation) • Korakis (MAC, Video, Implementation) • Memon (Security) • Liu (MAC, Video, Implementation) • Panwar (PHY, MAC, Video, Security, Implementation) • Wang (PHY, MAC, Video, Implementation) • Collaboration with industry • Funded by NSF, WICAT, CATT, Poly Angel Funds • See http://coop.poly.edu for more details

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