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SPARTA: Stable and Efficient Spectrum Access in Next Generation Dynamic Spectrum Networks. Lili Cao and Haitao Zheng,UCSB IEEE Conference on Computer Communications (INFOCOM) 2008 Presenter: Han-Tien Chang. Outline. Introduction Background and Related Work Achieving Stable Spectrum Access

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Sparta stable and efficient spectrum access in next generation dynamic spectrum networks

SPARTA: Stable and Efficient Spectrum Access in Next Generation Dynamic Spectrum Networks

Lili Cao and Haitao Zheng,UCSB

IEEE Conference on Computer Communications (INFOCOM) 2008

Presenter: Han-Tien Chang


Outline
Outline Generation Dynamic Spectrum Networks

  • Introduction

  • Background and Related Work

  • Achieving Stable Spectrum Access

  • Interference-aware Statistical Admission Control

  • Stability-Driven Distributed Coordination

  • Experimental Results and Discussions

  • Practical Consideration

  • Conclusion


Introduction
Introduction Generation Dynamic Spectrum Networks

  • By multiplexing spectrum usage across time and space, dynamic spectrum access can significantly improve the spectrum utilization

  • A critical problem of unstable spectrum usage

    • Because of network and traffic dynamics, their achieved spectrum usages fluctuate and are inherently unpredictable and unstable

    • To meet critical service agreements, service providers are forced to significantly over-provision the network

    • Leading to unnecessary financial penalties and underutilization of spectrum


Introduction cont d
Introduction (cont’d) Generation Dynamic Spectrum Networks

  • SPARTA, a new DSA architecture

    • Provide efficient and stable spectrum usage by integrating proactive planning with reactive adaptation

    • Interference-aware statistical admission control

      • a novel statistical admission control algorithm to proactively prevent congestion of spectrum demand while addressing interference constraints

    • Stability-driven distributed spectrum allocation

      • an efficient allocation algorithm to quickly adapt admitted APs’ spectrum usage to match their time-varying demands


Background and related work
Background and Related Work Generation Dynamic Spectrum Networks

  • The Problem of Unstable Spectrum Access

    • nodes access spectrum opportunistically, making it difficult to obtain desired spectrum consistently

Network Topology

CDF of node spectrum

outage rate at different

network size

Spectrum demand and

assignment at a node


Background and related work cont d
Background and Related Work (cont’d) Generation Dynamic Spectrum Networks

  • The problem of spectrum allocation

    • is often modeled as resource scheduling problems [12]

    • [23] [24] propose algorithms to dynamically allocate spectrum to match demands

  • In this paper,

    • integrating admission control with dynamic spectrum allocation to achieve stable and efficient spectrum usage

    • focuses on the spectrum allocation problem with a set of combinatorial interference constraints

[12] JAIN, K., PADHYE, J., PADMANABHAN, V., AND QIU, L. Impact of interference on multi-hop wireless network performance. In Proc. of MobiCom (2003).

[23] YUAN, Y., BAHL, P., CHANDRA, R., CHOU, P. A., FERRELL, J. I., MOSCIBRODA, T., NARLANKA, S., AND WU, Y. Knows: Kognitiv networking over white spaces. In Proc. of IEEE DySPAN (2007).

[24] YUAN, Y., BAHL, P., CHANDRA, R., MOSCIBRODA, T., NARLANKA, S., AND WU, Y. Allocating dynamic time-spectrum blocks in cognitive radio networks. In Proc. of MobiHoc (2007).


Achieving stable spectrum access
Achieving Stable Spectrum Access Generation Dynamic Spectrum Networks

  • Problem Definition

    • Consider a large infrastructure network with N APs and M channels in a time horizon of [0,T]

    • Assumptions

      • Using OFDM

      • Nodes can utilize multiple non-continuous channels concurrently

      • Focus on channel assignment and assume fixed transmit power

      • The time is divided into small slots and nodes can only modify spectrum usage at the beginning of each slot.


Achieving stable spectrum access cont d
Achieving Stable Spectrum Access (cont’d) Generation Dynamic Spectrum Networks

  • Notations

    • Spectrum demand Dn(t)

      • The amount of spectrum node n requests at time t

    • Demand shaping Rn(.)

      • The shaping determined at the time of admission

      • The admitted demand at time t is Rn(Dn(t))

      • 0≦ Rn(Dn(t)) ≦ Dn(t)

    • Spectrum allocation An(t) =

      • The amount of spectrum n gets at time t ( an,m(t) ∈{0,1} )

      • an,m(t) = 1 means channel m is allocated to n.

    • Interference constraints cn,k,m (cn,k)

      • The conflict constraint between node n and k on channel m

      • cn,k,m= 1 means node n and k should not use channel m at the same time

      • an,m(t) * ak,m(t) = 0 if cn,k, = 1


Achieving stable spectrum access cont d1
Achieving Stable Spectrum Access (cont’d) Generation Dynamic Spectrum Networks

  • Optimization problem

Given

Find

Θn represents the maximum outage rate at node n.

U (.) represents the utility function used by the admission control module.

 Goal: Maximize the total admitted demand, where U(x) = x


Achieving stable spectrum access cont d2
Achieving Stable Spectrum Access (cont’d) Generation Dynamic Spectrum Networks

  • The above problem can be divided into two sub-problems

    • at time 0, defining spectrum shaping {Rn(.)}N

    • From time – to T, performing instantaneous spectrum allocation in each time slot to find {an,m(t) }NxM

    • are NP-Hard

  • Peak Demand based Static Access

  • The system treats each (bursty) demand as if it is a constant traffic at a

    rate of Dmaxnand assigns spectrum to match its peak demand.


    Achieving stable spectrum access cont d3
    Achieving Stable Spectrum Access (cont’d) Generation Dynamic Spectrum Networks

    • The SPARTA Architecture (Two components)

      • A central entity performs interference-aware statistical admission control to regulate spectrum demand and prevent outages

      • Admitted APs perform stability-driven distributed spectrum allocationto determine instantaneous spectrum usage to match their time-varying demands


    Interference aware statistical admission control
    Interference-Aware Statistical Admission Control Generation Dynamic Spectrum Networks

    • Motivated by prior work on statistical admission control using effective rate [15][16]

    • Assumes perfect knowledge of the statistical model of each AP’s spectrum demand and the global interference constraints

    • Background on Effective Rate

      • routers can treat the flow (with bursty traffic) as if it is a constant traffic at this effective rate during the active period of the flow.

    [15] KELLY, F. Notes on effective bandwidths. In Stochastic Networks: Theory and Applications, F. Kelly, S. Zachary, and I. Ziedins, Eds.,Royal Statistical Society Lecture Notes Series. Oxford University Press, 1996, pp. 141–168.

    [16] KELLY, F. P. Charging and accounting for bursty connections. In Internet economics. MIT Press, Cambridge, MA, 1997, pp. 253–278.


    Interference aware statistical admission control cont d
    Interference-Aware Statistical Admission Control (cont’d) Generation Dynamic Spectrum Networks

    • Let Xi (1≦i ≦N) represent a random variable

      • whose value is the amount of bandwidth requested by node i in a time slot

    • The router admits all N nodes if

    • Define the effective rate of node Xi as

    e-γ: outage rate

    C: total amount of bandwidth


    Interference aware statistical admission control cont d1
    Interference-Aware Statistical Admission Control (cont’d) Generation Dynamic Spectrum Networks

    • Effective rate based spectrum demand shaping

      • original effective-rate design relies on the linear constraints

      • The interference constraints are combinatorial and non-linear (need approximation)

      • Node-L Constraints

        • Let be the total amount of spectrum allocated to node n under this new constraint

        • where Inis the set of conflict peers of n in the original constraint who are left of n(geometric order)


    Interference aware statistical admission control cont d2
    Interference-Aware Statistical Admission Control (cont’d) Generation Dynamic Spectrum Networks

    • using Node-L constraints, each individual node’s outage rate is bounded by the outage rate of the constraint

  • Two admission policies

    • Binary shaping, Rn(x) = rnx and rn∈ {0,1}

      • A node is either fully admitted or declined

    • Continuous shaping, Rn(x) = rnx and rn∈ [0,1]

      • A node can be partially admitted


  • Interference aware statistical admission control cont d3
    Interference-Aware Statistical Admission Control (cont’d) Generation Dynamic Spectrum Networks

    • propose analytical results on deriving sub-optimal snvalues under different demand statistics.

      • several heuristics to choose the uniform s

    • After choosing sn, we develop efficient solutions to find sub-optimal rnvalues

  • Fast algorithm for continuous demand shaping

  • where hiis the peak rate, miis the mean rate, and riis the demand shaping factor


    Interference aware statistical admission control cont d4
    Interference-Aware Statistical Admission Control (cont’d) Generation Dynamic Spectrum Networks

    • Let xi = airi(s). We can calculate rifrom xi, ri= Fi(xi)

    • Using a low-complexity local search algorithm using the concept of “Feed Poverty” [4]

      • The algorithm runs iteratively


    Interference aware statistical admission control cont d5
    Interference-Aware Statistical Admission Control (cont’d) Generation Dynamic Spectrum Networks

    • In iteration k, the algorithm will

      • Choose node i, increase its effective rate: xki= xk-1i+Δx

      • Adjust the effective rate of other nodes

        • who share the same constraints with i until all the constraints are satisfied.

      • If the total system utility increases, apply this improvement, go to iteration k + 1.

        • Otherwise, discard the improvement, go to iteration k + 1.

    • The algorithm terminates if no more local modifications can improve the system utility


    Interference aware statistical admission control cont d6
    Interference-Aware Statistical Admission Control (cont’d) Generation Dynamic Spectrum Networks

    • Tow policies that how to adjust the effective rate of other nodes to satisfy all the constraints

      • Proportionally amortized decrease

        • For each constraint I which contains xi, if increasing xiviolates I, the algorithm must decrease the effective rate of other nodes in I

      • Tightest constraint first

        • need to optimize the ordering of constraint adjustments to avoid unnecessary ping-pong effect

  • Greedy Algorithm for Binary Demand Shaping

    • starting from an empty admission, iteratively admit nodes one by one


  • Stability driven distributed coordination
    Stability-Driven Distributed Coordination Generation Dynamic Spectrum Networks

    • A low-complexity distributed algorithm

      • admitted APs determine spectrum allocation using local coordination

      • each AP n applies the following procedure to choose spectrum channels

        • 1. Mark all the channels that are used by its neighbors who are in front of nin the ordering as “unavailable”; and who are behind nin the ordering as “busy”; mark the rest as “idle”

        • 2.1 Select channels from the set of “idle” channels. If the set is not sufficient to meet n’s demand, take channels from the set of “busy” channels.

        • 2.2 Neighboring nodes who are using these “busy” channels will exit from these channels and mark them as “unavailable”.

        • 3. Stop if n’s demand is satisfied or if the “idle” and “busy” sets become empty.


    Experimental results and discussions
    Experimental Results and Discussions Generation Dynamic Spectrum Networks

    • Evaluate SPARTA using network simulations

      • Randomly deploy APs in an 1x1 area

      • Two nodes conflict if their distance is within d = 0.2

      • Assume that nodes’ demand follows the On/Off model with mean m (0.15) and peak rate h (1).

      • Effective Rate vs Peak Rate Admission Control

        • PRA-B (Peak rate admission using binary shaping)

        • PRA-C (Peak rate admission using continuous shaping)

        • SPARTA-B (SPARTA with binary shaping)

        • SPARTA-C (SPARTA with continuous shaping)

        • No Adm – no admission control and APs contend for spectrum in each time slot


    Experimental results and discussions cont d
    Experimental Results and Discussions (cont’d) Generation Dynamic Spectrum Networks

    • guarantee the outage rate to below 2% while the system without admission control suffers from an average outage rate of 60% for a network size of 500 and 20% for 300

    • compared to PRA, SPARTA increases spectrum utilization by 80–100%


    Experimental results and discussions cont d1
    Experimental Results and Discussions (cont’d) Generation Dynamic Spectrum Networks

    Peak Rate 

    Mean Rate 

    For a given peak rate range, lowering the mean rate leads to higher gain in SPARTA, because of the increased degree of multiplexing


    Experimental results and discussions cont d2
    Experimental Results and Discussions (cont’d) Generation Dynamic Spectrum Networks

    • Impact of network topology

    In different cluster degree w.

    As w increases, the spectrum utilization decreases because the level of interference increases


    Practical consideration
    Practical consideration Generation Dynamic Spectrum Networks

    • Identifying Interference Constraints

      • The serverperforms network measurements to collect interference constraints

      • Individual nodes scan radio signals to find interfering nodes and report their findings to the server

      • Clients associated with nodes sense radio signals and provide feedback on findings of interfering nodes

    • Identifying Demand Statistics

      • proposed solution also requires information on demand statistics


    Conclusion
    Conclusion Generation Dynamic Spectrum Networks

    • Consider the problem of providing stable and efficient spectrum access in DSA networks

    • SPARTAcombines proactive planning with reactive adaptation to match spectrum allocation to time-varying demands

    • Using both analytical verification and experimental simulation

      • SPARTA can guarantee stable spectrum access while maximizing spectrum utilization


    Comments
    Comments Generation Dynamic Spectrum Networks

    • Combine the admission control with DSA problems

    • Well problem definition and modeling

    • Linear approximation to non-linear constraints


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