Spectrum Sharing in Cognitive Radio
Networks
Neil Tang
3/23/2009
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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
C
a
b
B
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 N*C.

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 p=[p1,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 pt*L 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) = (dπ(v)cv)/(X[v] + 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.
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