文件列表:

Codes_MandarKulkarni_DynamicTDDSelfbackhaul (0, 2018-05-23)
Codes_MandarKulkarni_DynamicTDDSelfbackhaul\.DS_Store (6148, 2016-12-16)
__MACOSX (0, 2018-05-23)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul (0, 2018-05-23)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\._.DS_Store (212, 2016-12-16)
Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations (0, 2018-05-23)
Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\createnetwork.m (1956, 2016-12-16)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations (0, 2018-05-23)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\._createnetwork.m (212, 2016-12-16)
Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\ScheduleUsers.m (15778, 2016-12-16)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\._ScheduleUsers.m (212, 2016-12-16)
Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\AssociateUEandSBS.m (981, 2016-12-16)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\._AssociateUEandSBS.m (268, 2016-12-16)
Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\computemetric.m (9673, 2016-10-21)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\._computemetric.m (212, 2016-10-21)
Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\computeperformance.m (2782, 2018-05-23)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\._computeperformance.m (212, 2018-05-23)
Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\plotAssociations.m (1036, 2016-04-11)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\._plotAssociations.m (176, 2016-04-11)
Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\createchannel.m (3728, 2016-10-27)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\._createchannel.m (268, 2016-10-27)
Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\main.m (4026, 2016-12-16)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\._main.m (268, 2016-12-16)
Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\config.m (3634, 2018-05-23)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\MonteCarloSimulations\._config.m (212, 2018-05-23)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\._MonteCarloSimulations (212, 2018-05-23)
Codes_MandarKulkarni_DynamicTDDSelfbackhaul\AnalysisCodes (0, 2018-05-23)
Codes_MandarKulkarni_DynamicTDDSelfbackhaul\AnalysisCodes\Lambda.m (433, 2016-09-27)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\AnalysisCodes (0, 2018-05-23)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\AnalysisCodes\._Lambda.m (212, 2016-09-27)
Codes_MandarKulkarni_DynamicTDDSelfbackhaul\AnalysisCodes\RateULs.m (5187, 2016-11-29)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\AnalysisCodes\._RateULs.m (212, 2016-11-29)
Codes_MandarKulkarni_DynamicTDDSelfbackhaul\AnalysisCodes\Lambdadash.m (425, 2016-09-27)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\AnalysisCodes\._Lambdadash.m (212, 2016-09-27)
Codes_MandarKulkarni_DynamicTDDSelfbackhaul\AnalysisCodes\kappa.m (312, 2016-09-27)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\AnalysisCodes\._kappa.m (212, 2016-09-27)
Codes_MandarKulkarni_DynamicTDDSelfbackhaul\AnalysisCodes\RateDLs.m (5144, 2016-11-29)
__MACOSX\Codes_MandarKulkarni_DynamicTDDSelfbackhaul\AnalysisCodes\._RateDLs.m (212, 2016-11-29)
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Author: Mandar N. Kulkarni (mandar.kulkarni@utexas.edu) In order to use these codes, please cite the following paper: M. N. Kulkarni, J. G. Andrews, and A. Ghosh,"Performance of Dynamic and Static TDD in Self-backhauled mmWave Cellular Networks", submitted to IEEE Trans. Wireless Commun., 2017. The analysis codes implement the formulas corresponding to SINR distribution and mean rate theorems in the above paper. Instructions: 1. Helper files: a) Load distribution - Upsilon.m computes the joint UL/DL load distribution in Assumption 2, kappa computes the marginal load distribution in Assumption 1. b) Service distance distributions: F.m, ffunc.m compute the functions in Theorem 1 for computing service link distance distribution. c) Assocprob.m finds association probability d) Fad_typical.m evaluates distribution of DL access subframe length from Lemma 1. e) Lamda.m and Lambdadash.m compute the propagation process intensity and its derivative as given in Appendix A. f) Lul_access, Lul_backhaul, Ldl_access, Ldl_backhaul are Laplace transform of interference 2. SINR distribution computation: ULSINR.m and DLSINR.m compute the SINR CCDF for UL and DL access links as a function of time slot index and TDD scheme. The helper 3. Mean rate computation: RateUL.m and RateDL.m are the main files to run. For UL, RateULs, RateULm, RateULb are are mean rates for access SBS connection, access MBS connection and backhaul link respectively. Similarly for DL, RateDLs, RateDLm, RateDLb compute the respective intermediate mean rate computations. 4. VaryDLfrac_backhaulsplit.m is a runfile that gives an example for how to call RateUL.m and RateDL.m and vary the DL fraction or access-backhaul time splits.

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