opportunistic scheduling with reliability guarantees in cognitive radio networks
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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

Opportunistic Scheduling with Reliability Guarantees in Cognitive Radio Networks

Rahul Urgaonkar, Michael J. Neely

University of Southern California


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


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
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
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
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

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

network model contd
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
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
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
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


can solve if know all parameters

challenge: unknowns

mobility, Λ, dynamics

our approach
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
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

algorithm design and proof sketch
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

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

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

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

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









Total average congestion vs.

input rate for different V

(also no flow control case)


Simulation example






vs. Input rate

for different V





  • 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