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Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks. Rahul Urgaonkar, Michael J. Neely University of Southern California http://www-rcf.usc.edu/~urgaonka/. *Sponsored in part by the DARPA IT-MANET Program, NSF OCE-0520324. Cognitive Radio Networks.

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Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks

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Opportunistic scheduling with reliability guarantees in cognitive radio networks l.jpg

Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks

Rahul Urgaonkar, Michael J. Neely

University of Southern California

http://www-rcf.usc.edu/~urgaonka/

*Sponsored in part by the DARPA IT-MANET Program, NSF OCE-0520324


Slide2 l.jpg

Cognitive Radio Networks

  • Radio spectrum: a precious commodity

    - recent FCC auction of 700MHz band ~$20 billion

  • Existing static allocation of spectrum considered inefficient

    - “white spaces” observed

  • Motivation: Improve spectrum usage by dynamic spectrum access

  • Key enabler: Cognitive Radio

    - here, cognitive radio ~ dynamic operating frequency


Design issues and challenges l.jpg

Design Issues and Challenges

  • Primary (licensed) and Secondary (unlicensed) users

  • Basic requirement: To ensure secondary users take advantage of the unused spectrum without adversely affecting primary users

  • Challenges:

    • potentially oblivious primary users

    • imperfect “channel state information” may cause collisions

    • network dynamics (mobility, traffic)

    • distributed solutions desirable


Our contributions l.jpg

Our Contributions

  • Develop a throughput optimal control algorithm for cognitive radio networks

    • general mobility and interference models

  • Notion of collision queues

    • inspired by the virtual power queue technique of [1]

    • worst case deterministic bound on maximum number of collisions

      • prior works give probabilistic guarantees

  • Consider full effects of queueing

    • yields bounds on average delay

  • Constant factor distributed approximation

    - in a special case

[1] M. J. Neely, Energy Optimal Control for Time Varying Wireless Networks, IEEE Transactions on Information Theory, July 2006


Network model l.jpg

Network Model

  • M primary, N secondary users

  • Primary users static, each has a unique channel

    • channels orthogonal in frequency or space

  • Secondary users mobile, no licensed channel

    • set of channels they can access time-varying

    • H(t) : 0/1 channel accessibility matrix

  • Mobility model

    • time-slotted

    • resulting channel accessibility matrix H(t) Markovian


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Example Network

hij(t) = 1 if SU i can access channel j in slot t

H(t) evolves according to a finite state ergodic Markov Chain, transition probabilities unknown


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Network Model (contd.)

  • Interference model

    • Sm(t) : actual state for channel m (busy, idle)

    • at most one transmission per channel per slot

    • additionally, interference sets Inm

    • conditions for successful SU transmission

I21 = {1, 2}

Important special case

Inm= {m} for all n,m


Network model contd8 l.jpg

Network Model (contd.)

  • Channel State Information model

    • probability Pm(t) = E{Sm(t)|S(t-1)}

    • known at slot t

    • obtained by sensing the channels or knowledge of PU traffic statistics or combination etc.

    • models imperfect channel state information

2 state Markov chain example. Assume know ε, δ

E{S(t)|S(t-1) = ON} = 1- ε

E{S(t)|S(t-1) = OFF} = δ


Queueing dynamics l.jpg

Queueing Dynamics

  • Secondary user queues Un(t)

  • Flow control decision Rn(t)

    • how many new packets to admit

  • Transmission decisions μnm(t)

    • subject to network model constraints


Setting up the problem l.jpg

Setting up the problem

Goal: Maximize secondary user throughput utility subject to maximum time average rate of collisions ρm with any primary user m

Rn(t) = admitted data for SU n in slot t

Cm(t) = collision variable for PU m in slot t

Let

can solve if know all parameters

challenge: unknowns

mobility, Λ, dynamics


Our approach l.jpg

Our Approach

  • Lyapunov Optimization technique [2]

    • generalization of backpressure technique

    • [2] also covers related work

  • Unifies stability and utility optimization

  • Main idea: Convert time average constraints into queueing stability problems

    • notion of virtual queues

  • Then, use Lyapunov Stability argument to design an optimal control algorithm

[2] Resource Allocation and Cross-Layer Control in Wireless Networks, Georgiadis, Neely, Tassiulas, NOW Foundations and Trends in Networking, 2006


Collision queue l.jpg

Collision queue

  • Define a collision queue Xm(t) for channel m

Observation: If this queue is stable, then the constraint on the maximum time average rate of collisions is met

This is exactly the collision constraint in our optimization problem


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Algorithm Design and Proof sketch

  • Define our state as Q(t) = (U(t), X(t))

  • Define Lyapunov function

  • Compute Lyapunov drift

  • Every slot, take control actions to minimize (V≥0)

  • Compare with a stationary, randomized policy

  • Delayed drift analysis for Markovian dynamics


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Cognitive Network Control Algorithm

  • “cross-layer” algorithm decoupled into 2 components. (V≥0)

  • Flow control: Each secondary user chooses the number of packets to admit as the solution to:

    • - simple threshold policy, implemented separately at each SU

  • Scheduling transmissions of secondary users: Choose a resource allocation that maximizes:

    • subject to network constraints

    • - a generalized Maximum Weight Match problem


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CNC Performance Theorem

  • Strong reliability bound: The worst case number of collisions suffered by any primary user m is no more than ρmT + Xmax over any finite interval T (where Xmax is a constant)

    • - deterministic performance guarantee

  • Bounded worst case queue backlog: The worst case queue backlog is upper bounded by a finite constant Umax for all secondary users

    • - Umaxlinear in V

  • Utility-Delay tradeoff: The average secondary user throughput achieved by CNC is within O(1/V) of the optimal value


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Distributed Implementation

  • Focus on the case with Imn = {m}

  • The resource allocation problem becomes the Maximum Weight Match problem on a Bipartite graph

    • NxM Bipartite graph, N secondary users, M channels

  • Constant factor (1/2) distributed approximation using Greedy Maximal Match Scheduling

  • Reliability guarantees stay the same


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Simulation example

  • Cell partitioned network with 9 static primary users, 8 mobile secondary users, moving according to a Random Walk

  • One channel per primary user

  • Here, greedy maximal match = MWM

7

2

4

1

3

8

5

6

Total average congestion vs.

input rate for different V

(also no flow control case)


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Simulation example

7

2

4

1

Throughput

vs. Input rate

for different V

3

8

5

6

  • All collision constraints met

  • The achieved throughput is very close to the input rate for small values of the input rate

  • The achieved throughput saturates at a value depending on V, being very close to the network capacity for large V


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