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Resource Allocation Admission Control in Distributed MAC for

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         Ad-Hoc Wireless
            Networks




   Main Characteristics
       Each node generates independent data
       Any node can communicate with any other.
       No centralized controller (self-configuring)
       Data transmitted in (short) packets
       Links typically symmetric.
       Nodes may be mobile and/or power constrained.
       Typically a large number of nodes
     What has changed
       since 1985?
    Better, cheaper, low power DSPs
    Advanced communication techniques
       Powerful channel codes and decoders.
       Equalization/SS/Multicarrier
       High level modulation
       Diversity/MUD/smart antennas

    Advances in routing

    Signal strength measuring techniques
     available in radios.

    Adaptive radios.

How would we leverage these developments
    to make better ad-hoc networks?
    Sensor Networks




   Sensor Networks
       Energy is the driving constraint.
       Data highly correlated in time and space.
       Node location information critical
       Low homogeneous rates.
       Links typically asymmetric.
       Data flows to centralized location.
       1000-100,000 Nodes
       Have a common mission.
       Very different from typical ad-hoc networks
    Link Layer Design
   Fundamental limits
      “Shannon capacity” versus energy

   Processing to reduce transmit power
      Diversity (multinode combining)
      Coding
      Adaptive modulation (probing)
      Adaptive framing
      Beamforming
      Processing vs. transmitting bits

   Data Processing
     Compression via local decisions
     Data prioritization
     Data distribution (“need-to-know”)

   Variable node alertness
      Sleep modes
      Hierarchical power conservation modes
Network Layer Design

   Network Capacity

   Routing
       Delay/throughput/energy tradeoffs
       Distributed control

   Topology
       Dense deployment
       How many hops per connection?
       Effect of adaptive link techniques
       Supernodes vs. homogeneous nodes

   Adaptive Techniques
       Multiple access.
       Link adaptation to maximize
        throughput
       Network optimization versus link
        optimization.
    Application Design
   Design Optimization
       What is the “mission” of the network.
       Tradeoff between longevity and
        capability.
       Longevity driven by application

   Data Prioritization

   Collective Data Processing
       Can compensate for node limitations
       Compression and clustering
       Requires additional communication
        between nodes
       Multiuser game theoretic approaches

   Energy optimization:
       minimize total energy (between
        processing and communication)
        required for mission success
    Network Capacity
   Capacity limits of ad-hoc 2D
    networks.
       Measured as throughput of each node
        to another randomly selected node

   Assumptions
       n users uniformly distributed over the interior
        of a unit cube.
       Each user communicates with another user
        randomly chosen among all users.
       Nodes communicate at fixed rate W or when a
        minimum threshold SIR is met.
       Interference from nodes outside a disk around
        receiving node negligible
       Alternate SIR model (iuterference as AWGN)
       No channel division or diversity
        Capacity Bounds
   Capacity Definition:
       Average rate (bps) transmitted by any
        user to another randomly selected user.

   Lower Bound
                           W       
                          
                ( n)            
                                    
                           n log n 
       Based on deterministic routing scheme and
        partition of network area.

   Upper Bound
                        W 
                (n)    
                         n
       Uses convexity and aggregate rate

   In 3D, bounds proportional to 1/n1/3
     Capacity goes to zero as n increases
       Summary and
       Open Problems
   Multiple Access Techniques
   Capacity
   Random Access
   Cellular System Design
   Cellular Capacity and ASE
   Power Control
   Dynamic Resource Allocation
   Ad-Hoc Networks
   Sensor/Energy Efficient Networks
   Wireless impact on higher level
    protocols
       Multiple access
        techniques
   TD, FD, and orthogonal CD
    support same number of users

   DS spread spectrum typically
    supports fewer users
      capacity flexible (soft capacity)
      Improved by MUD, activity, etc.

   FH not typically used alone as a
    MAC technique
      Averages out-of-cell interference

   Open Problems
     Tradeoffs   in wideband channels.
     Tradeoffs   without perfect CSI
        Capacity (1 cell)
   User capacity
       Computed for DSSS systems
       Inherent assumptions needed
            BER, channel, voice activity, etc.
   Shannon capacity
       Obtained for fading broadcast and
        MAC channels
       Optimizes resource allocation
       TD and FD equal, CD best or same as
        TD depending on MUD
   Outage capacity
       Keeps rate constant over all fading
       Optimizes resource allocation
       Useful for delay-constrained data
   Open Problems
       Wideband channels/Imperfect CSI
       Combined Shannon/outage capacity
       Capacity with multiple antennas
        Random Access

   ALOHA inefficient
   Channel sensing ineffective

   Busy tones work well in some
    topologies, but not ad-hoc nets

   Reservation protocols inefficient
    for short messaging

   Different media types require
    different access techniques
   Open Problems:
       Multimedia techniques
       Satisfying QOS/delay constraints
          Cellular System
              Design
   Minimize reuse distance and cell size
   Optimal access technique is in the
    eyes of the beholder (stockholder).
       User capacity calculations skewed
       Tradeoffs in complex systems hard to
        assess - implementation considerations
   Interference reduction is good!!!
       Sectorized/Smart antennas
       Power control
       Dynamic resource allocation
       Multiuser detection

   Open Problems
       Optimizing/implementing interference
        reduction techniques
       Impact of multiple antennas
       Impact of packet data and multimedia
     Cellular Capacity and
              ASE

   Preliminary Shannon capacity results
       Simple channel model
       TD scheme
       Base station coordination (uplink)

   ASE general formula (bps/Hz/Km2)
       Based on MAC or broadcast channel
        capacity region
       Interference treated as noise
       No base station coordination

   Open Problems (Lots!!!)
       Expand exisiting capacity/ASE results
            Channels, multiple antennas, coordination, ...
       Propose new capacity/ASE definitions
       Develop outage capacity results
          Power Control
   Extremely powerful tool
       Increases battery life
       Maintains link SIR
       Reduces interference
       Component of resource allocation
       Aids in smooth handoff
       Reduces delays
       Increases capacity/throughput
   Distributed vs. Centralized
   Active link protection
       Combine with channel access

   Open Problems
       Impact of estimation errors
       Throughput/delay/power optimization
       Impact of noncooperative users
       Group vs. individual optimization
     Dynamic Resource
        Allocation
   Optimally assigns available resources
    based on traffic, user conditions, etc.
       Channels (time, codes, BW)
       Power (for transmission or processing)
       Rate
       Antennas
   Optimal dynamic channel allocation
    (MP) is NP hard
       Heuristics often used (work well)
       Little exact analysis - some bounds
       FCA optimal at high loads
       Optimal resource allocation NP harder.
   Open Problems (Lots!!!)
       Combinations (e.g. power/channels)
       Antenna allocation
       Processing power allocation
    Ad-Hoc Networks

   Wide open design issues at all layers
    of the protocol stack
       Access
       Channel allocation/freq. reuse
       Power adaptation
       Connectivity and Routing
       QOS
   Synergies across layers should be
    exploited

   Performance measures and capacity

   Open Problems (Everything!!!)
    Sensor and Energy
    Efficient Networks

   Network optimization for energy-
    constrained nodes
     Power tradeoffs for processing vs.
      transmitting bits
     Longevity vs. network function
     Energy-conserving modes

   Collective data processing
   Diffuse routing

   Open Problems (Everything!!)
    Wireless impact on
      higher layers
   Routing must take user mobility
    into account
       Mobile IP not designed for rapid
        movement
       Base stations may not be available for
        handoffs

   Network protocols react to errors
    using congestion control
       Does not correct for link failures due to
        fading
       Significantly slows down network when
        links fade

   Open Problems
       Design of protocols that take wireless
        channel into account without breaking
        the great features of current protocols

				
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