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A trip into non-othogonal spectrum sharing

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

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  1. 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

  2. Summary • Introduction • SpectrumSharingResume • Mimo Beamforming background • AnalyticalEvaluation • Hybridschedulers • SimulationResults • Conclusion Signet Meeting , May. 31, 2012 fguidolin@dei.unipd.it

  3. 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. fguidolin@dei.unipd.it

  4. SpectrumSharingResume BS1 BS2 FixedSpectrumassignement OrthogonalSpectrumSharing Non-OrthogonalSpectrumSharing fguidolin@dei.unipd.it

  5. SpectrumSharingResume fguidolin@dei.unipd.it

  6. MIMO Beamforming background The Multiple-InputMultiple-Output (MIMO) techniqueis the useof multiple antennas at both the transmitter and receivertoimprovecommunication performance. fguidolin@dei.unipd.it

  7. MIMO Beamforming background fguidolin@dei.unipd.it

  8. 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. fguidolin@dei.unipd.it

  9. 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. fguidolin@dei.unipd.it

  10. 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 fguidolin@dei.unipd.it

  11. 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. fguidolin@dei.unipd.it

  12. 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. fguidolin@dei.unipd.it

  13. 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 fguidolin@dei.unipd.it

  14. Analyticalevaluation: IS-NOSS IEEE CAMAD, Sept. 18, 2012 fguidolin@dei.unipd.it

  15. 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 fguidolin@dei.unipd.it

  16. Analyticalevaluation: IS-NOSS fguidolin@dei.unipd.it

  17. 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. fguidolin@dei.unipd.it

  18. Hybridresourceallocationscheme: MR The Maximum Rate scheduler (MR) permitstomaximize the total spectralefficiency in the network. Fairness?? fguidolin@dei.unipd.it

  19. 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: fguidolin@dei.unipd.it

  20. 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) fguidolin@dei.unipd.it

  21. Hybridresourceallocationscheme: BS Disagreementvector Achievablepayoff fguidolin@dei.unipd.it

  22. Hybridresourceallocationscheme: BS So the optimizationproblemis: fguidolin@dei.unipd.it

  23. 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). 2 6 1 8 9 3 1 2 9 9 CQI 4 5 2 6 1 7 7 4 4 5 fguidolin@dei.unipd.it

  24. 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 fguidolin@dei.unipd.it

  25. SimulationResults: system parameters fguidolin@dei.unipd.it

  26. SimulationResults: IS-NOSS fguidolin@dei.unipd.it

  27. SimulationResults: NOSS fguidolin@dei.unipd.it

  28. SimulationResults: Hybridscheme IS-NOSS fguidolin@dei.unipd.it

  29. SimulationResults: Hybridscheme IS-NOSS fguidolin@dei.unipd.it

  30. SimulationResults: Hybridscheme NOSS fguidolin@dei.unipd.it

  31. SimulationResults: Hybridscheme NOSS fguidolin@dei.unipd.it

  32. 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. fguidolin@dei.unipd.it

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