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A trip into non-othogonal spectrum sharing. Francesco Guidolin* , Antonino Orsino† , Leonardo Badia*† and Michele Zorzi*† *Department of Information Engineering , University of Padova, Italy † Consorzio Ferrara Ricerche, Ferrara, Italy

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a trip into non othogonal spectrum sharing

A trip intonon-othogonalspectrumsharing

Francesco Guidolin*, Antonino Orsino†, Leonardo Badia*† and Michele Zorzi*†

*Departmentof Information Engineering, Universityof Padova, Italy

† Consorzio Ferrara Ricerche, Ferrara, Italy

Email :{fguidolin, orsinoan, badia, zorzi}@dei.unipd.it

slide2

Summary

  • Introduction
  • SpectrumSharingResume
  • Mimo Beamforming background
  • AnalyticalEvaluation
  • Hybridschedulers
  • SimulationResults
  • Conclusion

Signet Meeting , May. 31, 2012

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slide3

Introduction

  • Spectrum sharing has been recently proposed as a promising paradigm to improve the efficiency of resource usage in the next generation mobile networks.
  • Non orthogonal spectrum sharing (NOSS) allows the operators to reuse the available frequencies at the cost of higher interference at the receivers.
  • Our work focus on:
  • the characterizationof the degradationof the SINR in a NOSS scenario;
  • the definition and the statisticalanalysisofthreedifferent NOSS schedulers;
  • the definitionofthreeadditionalhybrid (OSS-NOSS) schedulers;
  • the estimationof the schedulers performance throught the NS3 simulator.

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slide4

SpectrumSharingResume

BS1

BS2

FixedSpectrumassignement

OrthogonalSpectrumSharing

Non-OrthogonalSpectrumSharing

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slide6

MIMO Beamforming background

The Multiple-InputMultiple-Output (MIMO) techniqueis the useof multiple antennas at both the transmitter and receivertoimprovecommunication performance.

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slide8

MIMO Beamforming background

  • SU-MIMO: multiple antennas are usedto serve a userover a time/frequencyresource
    • spatialdiversity: increase the reliabilitybytransmit and receive the information overdifferentspatialdimensions;
    • spatial-multiplexing: send multiple data layersoverdifferentdimensions
    • beamforming: maximize the SNR over a given link, byproperlycombining the channelover multiple dimensions.
  • MU-MIMO: simultaneouscommunicationsof multiple usersover the sametime/frequencyresource.

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slide9

MIMO Beamforming background

We consider a Multi-Input Single-Output system (MISO) and we assume a perfect knowledge at the BSs of the coefficientschannelcolumnvectorhij.

In the case ofOrtogonalSpectrumSharing (OSS) the Maximum-transmissionRatio (MRT) beamforming SU-MIMO schemeisadopted.

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slide10

MIMO Beamforming background

In the case of NOSS, the MRT techniqueisinefficient due the large amount of interferences create from the BSs to the users. Thus a Zero Forcing (ZF) MU-MIMO schemeisadopted.

NOSS

IS-NOSS

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slide11

Analyticalevaluation

To study the SINR perceived by the user in the NOSS case we define two parameters:

Thus, it is possible to obtain the statistical behavior of ISR from the probability distribution of ρ, that in turn depends on the choice of the scheduler.

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slide12

Analyticalevaluation

  • Wedefinethreedifferentschedulersfor the IS-NOSS and NOSS scenarios:
  • M-SNRscheduler: best SNR users in the overall pool;
  • M-ISR scheduler: best ISR users in the overall pool;
  • PSscheduler: priorityto the operatorthatowns the spectrumresourcethatselect the userin its pool with the best no-sharing SNR. Then the other
  • operator chooses in its pool the user achieving the best ρ.

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slide13

Analyticalevaluation: IS-NOSS

M-SNR

From literature: Assuming a unit-variance Rayleight fading, i.e., hij~CN(0, I), the CDF of ρis given by the regularized incomplete beta function:

M-ISR / PS

where n is equal to the number of possible pairs in the network, i.e.:

• for the M-ISR;

• Ni if the owner operator is i, or Nz if the owner operator is z for the PS

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slide15

Analyticalevaluation: NOSS

The objectiveofthe scheduler is not to maximize a single value of but rather the sum of the values perceived by the base stations when a given pair is selected.

M-SNR

From literature: the CDF of the sum of two beta variables has a distribution given by:

M-ISR / PS

where n is equal to the number of possible pairs in the network, i.e.:

• for the M-ISR;

• Ni if the owner operator is i, or Nz if the owner operator is z for the PS

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slide17

Hybridresourceallocationscheme

  • Motivation:
  • The schedulingisperformedby a central authority;
  • Non-orthogonalspectrumsharingisperformedevenifitresultsinefficient .
  • ProposedSolution:
  • More distributive approach: the operatorsperform a first schedulingover the total bandwith;
  • Possibilitytoperformorthogonal and non-orthogonalspectrumsharing in differentresource block.

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slide18

Hybridresourceallocationscheme: MR

The Maximum Rate scheduler (MR) permitstomaximize the total spectralefficiency in the network.

Fairness??

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slide19

Hybridresourceallocationscheme: BS

In order to give a higher level of fairness we proposed a new scheduling algorithm able to manage the use of the NOSS scheme over the spectrum resource in function of the operators utility. Wehavebasedthis algorithm on a Nash bargaining solution.

The operators negotiate for a specific ISR level (ISRthr), that permits to regulate the scheduling as:

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slide20

Hybridresourceallocationscheme: BS

The Nash bargaining solution is used to model situations in which two players can cooperate by negotiating an outcome or payoff from a set of feasible payoffs.

Disagreementvector

(Va, Vb)

Achievablepayoff

(Xa, Xb)

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slide23

Hybridresourceallocationscheme: Opt-BS

In order to exploit the multiuser diversity due to the usage of an extended spectrum, we proposed a further scheduling algorithm (Opt-BS).

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slide24

SimulationResults

  • To evaluate the performance of the scheduling algorithms proposed in a LTE system, we applied the traces obtained from the statistical analysis within the NS3 simulator.
  • Weevaluate the impact of the ISR parameter together with the SNR level perceived by the users on the downlink spectral efficiency
  • We compare the results also with:
    • the optimal OSS scheduler that chooses for every RB the user in the overall pool with the best SNR;
    • the optimal NOSS scheduler that selects the pair of users that achieve the best spectral efficiency for every RB

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slide32

Conclusions and Future Work

  • We investigated the NOSS techniques through a statistical analysis of the ISR and a simulation analysis of the spectral efficiency obtained with the use of several scheduling techniques in a LTE network.
  • NOSS appears to be a promising technique for the performance improvement in NGMN, and a joint user scheduling among the operators can give further improvements in terms of spectral efficiency.
  • As a possible extension of the present work, the same approach can be applied to other beamforming techniques, and also extended to scenarios with multiple cells.
  • Moreover, different kind of utilities can be considered in the schedulers based on BS.

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