Spectrum Sharing in Cognitive Radio Networks Neil Tang 3232009

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Spectrum Sharing in Cognitive Radio
NetworksNeil Tang3/23/2009
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Outline

• References
• A Cognitive Radio Network
• System Model
• Problem Definition
• Proposed Algorithms
• Simulation Results
• Conclusions

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References

• J. Tang, S. Misra and G. Xue, Joint spectrum
  allocation and scheduling for fair spectrum
  sharing in cognitive radio wireless
  networks, Computer Networks, Vol. 52, No.
  11, 2008, pp. 2148-2158.

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A Cognitive Radio Network
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Assumptions

• A user refers to a transmitter-receiver pair.
• The channels available to each user are known in
  advance.
• A user can dynamically access a channel to
  deliver its packets, but can only work on one of
  the available channels at one time.
• Half-duplex, unicast communications and no
  collisions.
• A scheduling-based MAC layer.
• A spectrum server controlling the spectrum
  allocation and scheduling.

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Interference Model

• Primary Interference

A
B
C
A
B
C
A
B
C
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Interference Model

• Protocol Model C(a) C(b) and (d(A,D) ? RI or
  d(C,B) ? RI)

a
A
B
b
C
D
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Interference Model

• Physical Model

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Problem Definition

• A user-channel pair (i, j) ? A iff channel j is
  available to user i. The total number of
  user-channel pairs is bounded by NC.
• A traffic demand vector d d1, d2, , dN,
  specifying the traffic demand of each user.
• A transmission mode is composed of a subset of
  user-channel pairs which can be active
  concurrently. Whether concurrent transmissions
  are allowed or not can be determined based on the
  interference models.

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Problem Definition

• A transmission mode can be used in one timeslot.
  We wish to find a transmission schedule vector
  pp1,p2, , pT, where pt is the fraction of
  time that transmission mode t is activated.
• Suppose that all possible transmission modes are
  given. The scheduling problem is to determine the
  frame length L and the number of active time
  slots ptL of each transmission mode in one
  frame.
• A rate allocation vector r r1, r2, , rN and
  a corresponding DSF vector ? ?1, ?2, , ?N
  r1/d1, r2/d2, , rN/dN.

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Problem Definition

• All problems seeks a feasible rate allocation
  vector r, all transmission modes along with a
  feasible transmission schedule vector
• The objective of the MAximum throughput Spectrum
  allocation and Scheduling (MASS) problem is
  maximizing the network throughput
• The objective of the Max-min MAximum throughput
  Spectrum allocation and Scheduling (MMASS)
  problem is maximizing the network throughput
  under the condition min DSF is maximum among all
  feasible rate allocation vectors.
• The objective of the Proportional fAir Spectrum
  allocation and Scheduling (PASS) problem is
  maximizing the utility function ?log(?i)

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Multi-Channel Contention Graph (MCCG)
A transmission mode based on protocol
interference model corresponds to a Maximal
Independent Set (MIS) in MCCG.
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Proposed Algorithms

• Find all transmission modes (optimal) based on
  MCCG or a good subset of transmission modes
  (heuristic).
• Formulate LPs or CP to solve the defined
  problems.

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Compute Transmission Modes for Protocol Model

• Compute all MISs in MCCG existing algorithms
• Compute a subset of MISs
• - Start from a node, keep adding other
  nodes until no more can be
• added. Then we obtain one MIS.
• - Go through every node.
• - Repeat such procedure q times.
• - Adding criteria in each step w(v)
  (dp(v)cv)/(Xv 1))

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LP for MASS
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LPs for MMASS
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CP for PASS
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Compute Transmission Modes for Physical Model
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Simulation Results Protocol Model
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Simulation Results Physical Model
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Simulation Results
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Conclusions

• Our numerical results have shown that the
  performance given by our heuristic algorithms is
  very close to that of the optimal solutions.
• A good tradeoff between throughput and fairness
  can be achieved by our PASS algorithms.