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

Transcription

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) circuitswitching 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 n, femtocells Still some work (and monetary investment), but this is the trend This trend will further encourage userbased 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 m, LTE Advanced, IEEE 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 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 FemtoHaul 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) Normal Scenario: All data comes from the base station backhaul

9 FemtoHaul 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) Normal Scenario: All data comes from the base station backhaul

10 FemtoHaul 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) Normal Scenario: All data comes from the base station backhaul FemtoHaul: Obtain the data from an access point (WiFi, Femtocell) through another device Femtocell

11 FemtoHaul 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) Normal Scenario: All data comes from the base station backhaul FemtoHaul: Obtain the data from an access point (WiFi, Femtocell) through another device Femtocell

12 FemtoHaul 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) Normal Scenario: All data comes from the base station backhaul FemtoHaul: Obtain the data from an access point (WiFi, Femtocell) through another device Femtocell

13 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?

14 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

15 Using cooperative R-DSM on the second hop

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

17 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 for more details