文件列表:

GitHub_TGCN (0, 2018-04-10)
GitHub_TGCN\ComputeULAvgErgodicSE (0, 2018-04-10)
GitHub_TGCN\ComputeULAvgErgodicSE\functionChannelEstimates.m (10345, 2018-04-10)
__MACOSX (0, 2018-04-10)
__MACOSX\GitHub_TGCN (0, 2018-04-10)
__MACOSX\GitHub_TGCN\ComputeULAvgErgodicSE (0, 2018-04-10)
__MACOSX\GitHub_TGCN\ComputeULAvgErgodicSE\._functionChannelEstimates.m (239, 2018-04-10)
GitHub_TGCN\ComputeULAvgErgodicSE\functionComputeSE_UL.m (6926, 2018-04-10)
__MACOSX\GitHub_TGCN\ComputeULAvgErgodicSE\._functionComputeSE_UL.m (239, 2018-04-10)
GitHub_TGCN\ComputeULAvgErgodicSE\functionExampleSetup.m (12363, 2018-04-10)
__MACOSX\GitHub_TGCN\ComputeULAvgErgodicSE\._functionExampleSetup.m (239, 2018-04-10)
GitHub_TGCN\ComputeULAvgErgodicSE\functionMultiSlopeChannelGain.m (2938, 2018-04-10)
__MACOSX\GitHub_TGCN\ComputeULAvgErgodicSE\._functionMultiSlopeChannelGain.m (239, 2018-04-10)
GitHub_TGCN\ComputeULAvgErgodicSE\functionRlocalscatteringApprox.m (2320, 2018-04-10)
__MACOSX\GitHub_TGCN\ComputeULAvgErgodicSE\._functionRlocalscatteringApprox.m (239, 2018-04-10)
GitHub_TGCN\ComputeULAvgErgodicSE\main.m (7186, 2018-04-10)
__MACOSX\GitHub_TGCN\ComputeULAvgErgodicSE\._main.m (239, 2018-04-10)
GitHub_TGCN\ComputeULAvgErgodicSE\SetPropagationParameters.m (3807, 2018-04-10)
__MACOSX\GitHub_TGCN\ComputeULAvgErgodicSE\._SetPropagationParameters.m (239, 2018-04-10)
__MACOSX\GitHub_TGCN\._ComputeULAvgErgodicSE (239, 2018-04-10)
GitHub_TGCN\GenerateSimulationFigures (0, 2018-04-10)
GitHub_TGCN\GenerateSimulationFigures\figure2 (0, 2018-04-10)
GitHub_TGCN\GenerateSimulationFigures\figure2\.DS_Store (6148, 2017-11-16)
GitHub_TGCN\GenerateSimulationFigures\figure2\functionCPcomputationvsLvsP.m (6676, 2018-04-10)
__MACOSX\GitHub_TGCN\GenerateSimulationFigures (0, 2018-04-10)
__MACOSX\GitHub_TGCN\GenerateSimulationFigures\figure2 (0, 2018-04-10)
__MACOSX\GitHub_TGCN\GenerateSimulationFigures\figure2\._functionCPcomputationvsLvsP.m (239, 2018-04-10)
GitHub_TGCN\GenerateSimulationFigures\figure2\functionCPmodel.m (1739, 2018-04-10)
__MACOSX\GitHub_TGCN\GenerateSimulationFigures\figure2\._functionCPmodel.m (239, 2018-04-10)
GitHub_TGCN\GenerateSimulationFigures\figure2\plot_EEvsLvsP_3d.m (15333, 2018-04-10)
__MACOSX\GitHub_TGCN\GenerateSimulationFigures\figure2\._plot_EEvsLvsP_3d.m (239, 2018-04-10)
__MACOSX\GitHub_TGCN\GenerateSimulationFigures\._figure2 (239, 2018-04-10)
GitHub_TGCN\GenerateSimulationFigures\figure3a (0, 2018-04-10)
GitHub_TGCN\GenerateSimulationFigures\figure3a\.DS_Store (6148, 2017-11-16)
GitHub_TGCN\GenerateSimulationFigures\figure3a\functionCPcomputationvsLvsP.m (6669, 2018-04-10)
__MACOSX\GitHub_TGCN\GenerateSimulationFigures\figure3a (0, 2018-04-10)
__MACOSX\GitHub_TGCN\GenerateSimulationFigures\figure3a\._functionCPcomputationvsLvsP.m (239, 2018-04-10)
GitHub_TGCN\GenerateSimulationFigures\figure3a\functionCPmodel.m (1732, 2018-04-10)
__MACOSX\GitHub_TGCN\GenerateSimulationFigures\figure3a\._functionCPmodel.m (239, 2018-04-10)
GitHub_TGCN\GenerateSimulationFigures\figure3a\plotEEvsL.m (8942, 2018-04-10)
... ...

%% Network Deployment for Maximal Energy Efficiency in Uplink with Multislope Path Loss % This is a code package is related to the follow scientific article: % % Andrea Pizzo, Daniel Verenzuela, Luca Sanguinetti and Emil Bjornson, "Network Deployment for Maximal Energy Efficiency % in Uplink with Multislope Path Loss," IEEE Transactions on Green Communications and Networking, Submitted to. % The package contains a simulation environment, based on Matlab, that reproduces all the % numerical results and figures in the article. We encourage you to also perform reproducible research! % % % Abstract of Article % This work aims to design the uplink (UL) of a cellular network for maximal energy efficiency (EE). % Each base station (BS) is randomly deployed within a given area and is equipped with M antennas to % serve K user equipments (UEs). A multislope (distance-dependent) path loss model is considered and linear % processing is used, under the assumption that channel state information is acquired by using pilot sequences % (reused across the network). Within this setting, a lower bound on the UL spectral efficiency and a realistic % circuit power consumption model are used to evaluate the network EE. Numerical results are first used to % compute the optimal BS density and pilot reuse factor for a Massive MIMO network with three different % detection schemes, namely, maximum ratio combining, zero-forcing (ZF) and multicell minimum meansquared % error. The numerical analysis shows that the EE is a unimodal function of BS density and achieves % its maximum for a relatively small BS densification, irrespective of the employed detection scheme. This % is in contrast to the single-slope (distance-independent) path loss model, for which the EE is a monotonic % non-decreasing function of BS densification. Then, we concentrate on ZF and use stochastic geometry to % compute a new lower bound on the spectral efficiency, which is then used to optimize, for a given BS % density, the pilot reuse factor, number of BS antennas and UEs. Closed-form expressions are computed % from which valuable insights into the interplay between the optimization variables, hardware characteristics, % and propagation environment can be obtained. % % % Content of Code Package % The package contains 3 folders which can be used to generate the 5 simulation figures as they appear in the article. % The folder "ComputeULAvgErgodicSE" compute the uplink average ergodic % spectral efficiency as seen in Theorem 2 in the article. The results % obtained by running the main script in this folder are then saved in another folder % called "SimulationResults". Finally, the scripts included in the folder "GenerateSimulationFigures" % generate the figures in the article by using the simulation results computed beforehand. % % See each script and function for further documentation. % % % License and Referencing % This code package is licensed under the GPLv2 license. % If you in any way use this code for research that results in publications, please cite our original article listed above.

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