Beyond (Basic) Cellular Networks: Multi-Hop/Meshed, Ad-Hoc, DTNs, White Space, Wireless Cloud …

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Beyond (Basic) Cellular Networks
Multi-Hop/Meshed, Ad-Hoc, DTNs, White Space,
Wireless Cloud

• Shivkumar Kalyanaraman
• shivkumar-k AT in DOT ibm DOT com
• http//www.shivkumar.org
• Google shivkumar ibm rpi

Based in part upon slides of Bhaskaran Raman,
Kameswari Chebrolu, Mihail L. Sichitiu, Hari
Balakrishnan
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Outline

• Multi-hop (meshed) networks
• Dynamic Ad-Hoc Networking Weak State Routing
• Delay/Disruption Tolerant Networks (DTNs)
• Opportunistic access / offload
• Software Radio Wireless Network Cloud
• Cooperative MIMO other cooperative techniques
• White Space Networking

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Recall Wireless A Short Technical summary
Wireless networks are designed to maximize
spectral efficiency, support mobility, coverage,
and Quality-of-Service under severe
spectrum/bandwidth constraints
Wireless IT convergence
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Spectrum Scarcity Solutions

• CDMA/OFDMA Scheduling
• Spread spectrum in time-domain (CDMA) or
  frequency domain (OFDMA)
• Statistical multiplexing of time-frequency
• Dynamic tradeoff of power/interference i/f
  limited
• More signal processing (3G/3.5G) voice/data

• Pico/Femto Cells (WiFi) Overlays/ Offload
• Smaller Cells, lower power
• Indoor access characteristics offload onto Wifi
  or Femto cells
• SON management
• Macro cell overlays

• Frequency Reuse
• Static spectrum sharing TDM within each cell
  (GSM)
• Adjacent cells use different frequency
• Widely used for voice communication (GSM)
• Coverage radius reduced and low spectral
  efficiency

• MIMO CoMP
• Multiple antennas Spatial degrees of freedom
• Collaborative MIMO (CoMP) to manage inter-cell
  interference
• Intensive signal processing, channel estimation,
  BS coordination
• Easier with BS Pools/Cloud

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Spectrum Scarcity Solutions (contd)
Spectrum Scarcity

• Multi-hop/Meshed Networks / 60 GHz/FSO
• Smaller hops O(sqrt(N)) capacity increase
• Multi-route diversity resilient transport
  protocols routing advances
• Backhaul or shared spectrum
• Spectrum at higher reaches (60GHz, FSO) less
  regulated dense spatial reuse

• Space-Time-Frequency Shifting of Workloads
• Mobile mini-base station fleet
• Opportunistic access to smaller cells
  (associations for 10s)
• Multi-homed mobile devices
• Time-shifting of content delivery sophisticated
  traffic shaping at peak times.
• Delay / disruption-tolerant/ad-hoc networks

• White space/Cognitive Radio/Opportunistic
  Spectrum Access
• Using white space of spectrum opportunistically
• Dynamic spectrum scheduling and management
• Need complex technologies for detecting the
  white spectrum space and management policies
• Fit into cellular model TBD

• UWB (Underlay)
• Using ultra wide band spectrum without
  disturbing the occupied users
• Need to control the transmitted power below
  noise level to avoid interference
• Generally for short range transmission

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Taxonomy
Wireless Networking
Multi-hop
Single Hop
Infrastructure-less (ad-hoc)
Infrastructure-based (Hybrid)
Infrastructure-less (MANET)
Infrastructure-based (hubspoke)
802.11
802.16
Bluetooth
802.11
Cellular Networks
Car-to-car Networks (VANETs)
Wireless Sensor Networks
Wireless Mesh Networks
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Meshed Networks
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Mesh vs. Ad-Hoc Networks
Wireless Mesh Networks
Ad-Hoc Networks

• Multihop
• Nodes are wireless, possibly mobile
• May rely on infrastructure
• Most traffic is user-to-user

• Multihop
• Nodes are wireless, some mobile, some fixed
• It relies on infrastructure
• Most traffic is user-to-gateway

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Mesh vs. Sensor Networks
Wireless Sensor Networks
Wireless Mesh Networks

• Bandwidth is generous (gt1Mbps)
• Some nodes mobile, some fixed
• Normally not energy limited
• Resources are not an issue
• Most traffic is user-to-gateway

• Bandwidth limited (tens of kbps)
• In most applications, fixed nodes
• Energy efficiency is an issue
• Resource constrained
• Most traffic is user-to-gateway

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Lots of long distance links, adapted from WiFi
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Goals variety of apps, QoS, scalable operation
(100-200 nodes)
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Meshed Networks Issues

• The link abstraction may or may not hold
  depending upon the type of links designed
  (directional vs omni)
• Using 802.11 MAC for multi-hop does not work
• Usually meshes are 3 hops diameter
• TDMA / OFDMA extensions to handle 802.11 issues
• Per-hop losses overcome via loss-tolerant TCP or
  multi-path LT-TCP
• Community meshes havent been too successful
• Niches in smart grids etc.

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Links in A Backhaul Mesh
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FRACTEL vs Roofnet
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MAC Multichannel

• Increases network capacity

B bandwidth of a channel
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MAC MultichannelStandard MAC Multiple Radios

• A node now can receive while transmitting
• Practical problems with antennas separation
  (carrier sense from nearby channel)
• Optimal assignment NP complete problem
• Solutions
• Centralized
• Distributed

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MAC MultichannelCustom MAC Multiple Radios

• Nodes can use a control channel to coordinate and
  the rest to exchange data.
• In some conditions can be very efficient.
• However the control channel can be
• an unacceptable overhead
• a bottleneck

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Routing for Meshes and MANETs

• Routing consists of two fundamental steps
• Data plane Forwarding packets to the next hop
  (from an input to an output interface in a
  traditional wired network)
• Control plane Determining how to forward packets
  (building a routing table or specifying a route)
• Forwarding packets is easy, but knowing where to
  forward packets (especially efficiently) is hard
• Reach the destination
• Minimize the number of hops (path length)
• Minimize delay
• Minimize packet loss
• Minimize cost

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Routing Table

• A routing table contains information to determine
  how to forward packets
• Source routing Routing table is used to
  determine route to the destination to be
  specified in the packet
• Hop-by-hop routing Routing table is used to
  determine the next hop for a given destination
• Virtual circuit routing Routing table used to
  determine path to configure through the network
• A distributed algorithm is required to build the
  routing table
• Distance vector algorithms
• Link state algorithms

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MANET vs. Traditional Routing

• Every node is potentially a router in a MANET,
  while most nodes in traditional wired networks do
  not route packets
• Topologies are dynamic in MANETs due to mobile
  nodes, but are relatively static in traditional
  networks
• Channel properties, including capacity and error
  rates, mostly static in traditional networks, but
  vary in MANETs
• Routing in MANETs could consider both Layer 3 and
  Layer 2 information L2 can indicate connectivity
  and interference
• Interference is an issue in MANETs, but not in
  traditional networks
• Channels can be asymmetric with some Layer 2
  technologies
• Traditional routing protocols for wired networks
  do not work well in most MANETs too dynamic

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Types of MANET Routing
MANET Routing Protocols
Proactive
Reactive
Hybrid
Example OLSR
Example AODV
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Common Features

• MANET routing protocols must
• Discover a path from source to destination
• Maintain that path (e.g., if an intermediate node
  moves and breaks the path)
• Define mechanisms to exchange routing information
• Reactive protocols
• Discover a path when a packet needs to be
  transmitted and no known path exists
• Attempt to alter the path when a routing failure
  occurs
• Proactive protocols
• Find paths, in advance, for all source-pair
  destinations
• Periodically exchange routing information to
  maintain paths

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Geographic Routing

• Geographic Routing
• Compared to topology-based routing schemes,
  geographic routing schemes
• forward packets by only using the position
  information of nodes in the vicinity
• and the destination node.
• Thus, topology change has less impact on the
  geographic routing than other
• routing protocols.
• Early geographic routing algorithms are a type of
  single-path greedy
• routing schemes in which packet forwarding
  decision is made based on
• the location information of current forwarding
  node, its neighbors,
• and the destination node.
• However, all greedy routing algorithms have a
  common problem, i.e., delivery is
• not guaranteed even if a path exists between
  source and destination.

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Challenges in Routing for Large-scale Dynamic
Networks

• Routing table entries state indirections
  from persistent names (ID) to locators
• Due to dynamism, such indirections break
• Problematic in two dimensions
• Dynamism/mobility gt frequent update of state
• Dynamism large scale gt very high overhead,
  hard to maintain structure
• Proposed solution
• Probabilistic and more stable state WEAK STATE
• Use of unstructured methods

Node Mobility
Number of Nodes
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Weak State A New Type of State

• Strong State
• Deterministic
• Requires control traffic to refresh
• Rapidly invalidated in dynamic environments

• Weak State
• Probabilistic hints
• Updated locally
• Exhibits persistence

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Hard, Soft and Weak State
s
r
A with probability ?
B
B
A
A
Time elapsed since state installed/refreshed
Confidence in state information (?)
Hard State
Soft State
Weak State
Weak State is natural generalization of Soft State
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An Instance of Weak State
SetofIDs
GeoRegion
a,b,c,d,e,f
Probabilistic in terms of scope
Probabilistic in terms of membership

• The uncertainty in the mappings is captured by
  locally weakening/decaying the state
• Other realizations are possible
• Prophet, EDBF etc

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Weak State Routing for MANETs
Random Directional Walk (RDW)

• RDW used to announce location information (put)
  and forward packets (get)

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Dissemination/Proactive Phase (put)

• When a node receives a location announcement, it
• creates a ID-to-location mapping
• aggregates this mapping with previously created
  mappings if possible

C
B
A
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Forwarding Packets (get)
A
S
B
WSR involves unstructured, flat, but scalable
routing no flooding !
C
E
D
Forwarding decision similar to
longest-prefix-match. strongest semantics
match to decide how to bias the random walk.
Details in ACM Mobicom 2007 paper
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Packet Delivery Ratio Fixed Density
WSR always achieve high delivery ratio
GLS works fine at low mobility but fails to
maintain structure at high dynamism
OLSR delivers only a small fraction even at low
dynamism
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Control Packet Overhead
OLSR overhead increases exponentially
GLS works fine at low mobility but requires
superlinearly increasing overhead to maintain
structure at high mobility
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Transport TCP Solutions

• Focus on eliminating the uncertainty between
  congestion loss and all other reasons
• Many approaches developed for single-hop wireless
  systems
• Snoop
• I-TCP
• M-TCP

• End to end
• SACK
• Explicit error notification
• Explicit congestion notification (e.g. RED)
• New solutions for multi-hop
• Loss-Tolerant TCP
• Multi-path LT-TCP (MPLOT)

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Loss-Tolerant TCP (LT-TCP) vs TCP-SACK
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Single path limited capacity, delay, loss
Time

• Network paths usually have
• low e2e capacity,
• high latencies and
• high/variable loss rates.

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Idea Aggregate Capacity, Use Route Diversity!
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Multi-path LT-TCP (ML-TCP) Structure
Socket Buffer
Map pkts?paths intelligently based upon Rank(pi,
RTTi, wi)
Per-path congestion control (like TCP)
Reliability _at_ aggregate, across paths (FEC block
weighted sum of windows, PFEC based upon
weighted average loss rate)
Note these ideas can be applied to other
link-level multi-homing, Network-level virtual
paths, non-TCP transport protocols (including
video-streaming)
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Delay/Disruption Tolerant Networks (DTNs)
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DTN Examples
Delay and Disruptions are first-class
issues End-to-end path may never exist at any
instant in time, but may emerge only over time
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Overview of Routing Issues for DTNs

• State vs stateless Routing
• Stateless completely depend upon mobility, local
  storage at nodes replication/coding
• Eg Spray-and-wait , Spray-and-focus
• Scaling challenges.
• Stateful How to maintain useful state info
  despite disconnections.
• Weak state can again help (eg WSR-D protocol),
  with osmosis of state across connectivity
  clusters.
• Simple situations such as data mule (getting
  e-mail from a village, or synchronizing photos)
  involve 1-hop DTN routing etc.
• Vehicular DTNs (eg for an entire city) to
  provide useful complementary communication
  services to cellular not yet fully solved.
• Interesting small-scale testbeds DieselNet
  (UMass)

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Opportunistic Offload via Small Cells
Opportunistic traffic offload
Eg Aruna Balasubramanian, Ratul Mahajan, Arun
Venkataramani Augmenting Mobile 3G Using WiFi
Measurement, Design, and Implementation In
Proceedings of ACM MobiSys, San Francisco, USA,
June 2010. Vladimir Bychkovsky, Bret Hull, Allen
K. Miu, Hari Balakrishnan, Samuel Madden, A
Measurement Study of Vehicular Internet Access
Using In Situ Wi-Fi Networks, Proceedings of ACM
Mobicom 2006, Los Angeles, 2006. (best paper)
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Wi-Fi Is Everywhere (in developed urban markets)
Images from WiGLE.net and CarTel
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Opportinistic Offload The Opportunity

• Today
• Broadband connections are often idle
• 65 of on-line households have Wi-Fi
• What if
• home users open up their APs
• and share/sell the spare bandwidth?
• Cellular complement for mobile users
• Messaging (multimedia, e-mail, text)
• Location-aware services
• Mobile sensor networks (e.g. MIT project CarTel )

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Wi-Fi For Mobile Messaging

• Wi-Fi cells are smaller than cellular cells
• Is density sufficient? Are connections too short?
• Organically grown, unplanned deployments
• Uneven densities, AP churn, unpredictable
• Back-of-the-envelope
• 55 km/hour 15 meters/s
• 150 meter AP coverage Akella05
• 10 sec connectivity
• What about connection overhead?
• scan, associate, get IP, etc.
• Current stacks too slow

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CarTel Expt Bytes Uploaded Per Connection
Non-trivial amount of data Median 200 KBytes
per connection Mean 600 KBytes
Fraction of connections
Consistency check 600 KBytes / 24 sec 25 KBps
Bytes received on server (KBytes)
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The Future of Software Radio Wireless Network
Cloud

• Parul Gupta, Smruti Sarangi, Shivkumar
  Kalyanaraman IBM Research India
• Zhen Bo Zhu, Lin Chen, Yong Hua Lin, Ling Shao
  IBM Research China

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PSTN
2G-3G wireless network architecture
4G Wireless Network over Wireless Network Cloud
Access Network
Core Network
Cloud of Wireless Access Network Core Network
Service Network
Mobile switch center
BS cluster
Radio network controller
Service support node
Radio network controller
Gateway
Internet
BS cluster
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PSTN
2G/3G/4G Wireless over Wireless Network Cloud
Cloud of Wireless Access Network Core Network
Service Network
BS cluster
Service support node
Internet
BS cluster
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Various Forms of Infrastructure Sharing in
Wireless Networks
Network Sharing
Base Station Sharing
BSC
Owner 1 Network
Owner 1 Retail
SDR
RRU
BSC
BTS
MSC
BSC
Base Band Unit
Owner 2 Network
Owner 2 Retail
BTS
Antenna Sharing
Tower Sharing
BSC
BTS
BSC
BTS
Owner 1 Network
Owner 1 Network
BSC
BTS
BSC
BTS
Owner 2 Network
Owner 2 Network
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Towards Active Sharing Unbundling Base Stations
RRU BBU

• Distributed base station
• RRU (Remote Radio Unit)
• BBU (Base Band Unit)
• Two key standards enable distributed base station
  development
• CPRI
• OBSAI
• Benefits of distributed base station
• Reduce cost of facilitate infrastructure
• Reduce power consumption
• Easy of installation
• Flexible deployment model

Traditional Integrated Macro BS
RRU
BBU
Distributed BS RRU BBU
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Multi-Technology Software Radio
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Unbundled SDR BS w/ Open Wireless Interfaces IT
Platforms
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Distributed base station 2 distributed RRU
centralized BBU pool

• Benefits
• Fit for super urban, urban with high density of
  traffic
• Highly scalable
• Improve utilization by resource sharing
• Reduce management cost
• Requirements Challenges to BBU
• High density
• Resource sharing with BBU pool
• Low power consumption
• Key barriers
• Fiber distance (lt10Km)
• Increasing IO data throughput gt10Gbs with LTE
• Fiber construction cost
• Synchronization in long distance network

• Case in China
• World largest TD-SCDMA BBU pool
• Max support 72 RRUs
• Power 400W

A city like Bangalore or Delhi could be served
from lt10 pooled sites.
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Wireless Network Cloud Convergence of IT
Platforms, SDR RRH, Cloud Computing Principles
Fiber-to-the-tower
IT Cloud Computing Techniques
Software Radio Technology/ Hybrid IT Systems
Remote Radio Header Technology

• End-to-End IP Infrastructure in 4G

Wireless Network Cloud
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Wireless Network Cloud Potential Distributed
Interference Management. Eg Collaborative MIMO
Multi-cell environment with frequency reuse
factor 1
Optical fiber
Optical fiber
interference
Optical fiber

• Multiple points collaborate to mitigate ICI
• or align interference for cancellation.

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Wireless Cloud/Base Station Pools Facilitate
Co-operative Techniques
Joint scheduling and Load Balancing
Base Station Co-operation
Efficient Handovers To ensure quality for
real-time flows like VoIP, Video on Demand
Interference Cancelation Through Collaborative
MIMO Processing (CoMP) techniques
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TV White Space Spectrum Fact Sheet

• Spectrum bands available 54-60 MHz (TV channel
  2), 76-88 MHz (TV channels 5 and 6), 174-216 MHz
  (TV channels 7-13), 470-608 MHz (TV channels
  14-36) and 614-698 MHz (TV channels 38-51).
  Channels below 21 restricted only to fixed
  devices.
• The amount of spectrum available varies from
  market to market. In rural areas where fewer
  broadcasters are operating, it can provide a
  substantial amount of capacity. But in dense
  urban areas, white spaces offer far less capacity
  because more broadcasters are using the spectrum.
  For this reason, white-space spectrum could be
  particularly valuable for providing broadband
  access in rural areas, where wired infrastructure
  doesnt exist.
• These bands was earlier used exclusively mainly
  by TV broadcast channels and wireless microphones
• The FCC order in Nov 2008 required sensing for TV
  channels every 60 seconds and wireless mics and
  auxiliary devices every 30 seconds. If detected,
  white space devices required to stop tx in 2
  seconds
• In the FCC Sep 2010 order, this need for spectrum
  sensing is removed for devices that lookup a
  geo-location dbase of usage schedules to
  determine channel availability.
• Sensing only devices may apply for
  certification which may be granted if they show
  high sensing accuracy
• 2 channels set aside for wireless mics. By
  default, mics will not be included in the
  geo-location dbase, but large scale events
  organizers can petition to FCC for special
  inclusion
• Power limits 1W for fixed devices, 100 mW for
  portable (subject to interference and adjacent
  channel separation conditions being met), 50mW
  for sensing only devices
• Fixed TV bands devices cant operate on locations
  where the ground level is more than 76 meters
  above the average terrain level in the area.

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Opinions on FCC White Space Ruling

• FCC ruling simplifies requirements on spectrum
  sensing for a class of devices (those which can
  query TV databases)
• We do not believe white-space spectrum will allow
  the emergence of a nation-wide competitor, though
  niche players may emerge in specific markets.
• Even complementary strategies like Satellite
  White Space, or WiMAX White space will be
  subject to capital investment constraints
• and unreliability challenges posed like meshed
  networks.
• Longer transmission range ad-hoc random access
  is a mixed blessing
• will lead to significant interference
  management problems for radio access/MAC design
  (more complex than WiFi)

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Some Implications Potentials

• White space spectrum and super-Wifi hotspots
  that it enables are complementary
• It could significantly expand the offload
  strategy beyond indoor-Wifi in homes,
  enterprises, airports, cafes etc.
• Sub-urban outdoor areas could also be served by
  Super-Wifi will allow the rapid adoption /
  proliferation of uplink video applications like
  Qik
• Delay-tolerant/opportunistic workloads could
  benefit if appropriate access designs are
  created.
• Technical trends likely to be catalyzed by White
  space spectrum
• Disruption tolerance opportunistic
  communication as a first class paradigm in
  protocol stack design (beyond PHY/MAC layers)
• Multi-homing / multiple access (eg 3G WiFi
  white space simultaneously access) and
  complementarities leveraged
• Distributed MAC algorithms balance needed
  between the two extremes of
• scheduled MAC (for scalability/QoS) and
• random access (for spontaneous / ad-hoc access)

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White-Fi a pre-FCC 2010 protocol

• Goal form network like Wi-Fi on top of white
  space.
• WhiteFi
• Wi-Fi like system constructed on top of UHF white
  spaces
• Adaptively configures itself to operate in the
  most efficient part of the available white spaces
  
• Techniques
• 1. Spectrum Assignment
• 2. AP discovery
• 3. Handling disconnection

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Summary

• Multi-hop (meshed) networks
• Dynamic Ad-Hoc Networking Weak State Routing
• Delay/Disruption Tolerant Networks (DTNs)
• Opportunistic access / offload
• Software Radio Wireless Network Cloud
• Cooperative MIMO other cooperative techniques
• White Space Networking
• Growth of video and data services over wireless
  networks will drive future innovations in these
  areas.