an be easily added and tested. This paper is organized as follows: in Section II we describe the overall structure of the LTE system-level simulator. In Section III we show how the physical layer has been abstracted in the link measurement model. Afterwards, we present the link performance model in Section IV, and Section V presents the main uses of the simulator as well as some conclusions.
II. SIMULATOR OVERVIEW 模拟器概述
While link-level simulations are suitable for developing receiver structures [8], coding schemes or feedback strategies [9], it is not possible to reflect the effects of issues such as cell planning, scheduling, or interference using this type of simulations. Simulating the totality of the radio links between the User Equipments (UEs) and eNodeBs is an impractical way of performing system level simulations due to the vast amount of computational power that would be required [10]. Thus, in system-level simulations the physical layer is abstracted by simplified models that capture its essential characteristics with high accuracy and simultaneously low complexity. Figure 1 depicts a schematic block diagram of the LTE system-level simulator. Similarly to other system-level simulators, the core part consists of: (i) a link measurement model [11] and (ii) a link performance model [12].
mobility managementlink-performancemodellink-measurementmodelbase-station deploymentantenna gain patterntilt/azimuthmicro-scale fadinginterferencestructuremacro-scale fadingantenna gainshadow fadingthroughputerror rateserror distributiontraffic modellink adaptation strategyresource schedulingstrategyprecodingnetwork layoutpower allocation strategyFig. 1. Schematic block diagram of the LTE system level simulator The link measurement model abstracts the measured link quality used for link adaptation and resource allocation. On the other hand the link performance model determines the link Block Error Ratio (BLER) at reduced complexity. As figures of merit, the simulator outputs traces containing throughput and error rates, from which their distributions can be computed. Implementation-wise, the simulator flow follows the pseudo-code below. The simulation is performed by defining a Region Of Interest (ROI) in which the eNodeBs and UEs are positioned and a simulation length in Transmission Time Intervals (TTIs). It is only in this area where UE movement and transmission of the Downlink Shared Channel (DLSCH) are simulated. for each simulated TTI do move UEs if UE outisde ROI then reallocate UE randomly in ROI for each eNodeB do receive UE feedback after a given feedback delay schedule users for each UE do 1-channel state → link quality model → SINR 2-SINR, MCS → link perf. model → BLER 3-send UE feedback Where, ”!” represents the data flow in and out of the simulator’s link abstraction model. In the MATLAB implementation, the separated structure in the pseudo-code is maintained, allowing for easy adding of new functionalities and algorithms.
III. LINK MEASUREMENT MODEL LINK测度模型
In order to abstract the measured link quality, and as shown in the pseudocode, the Signal to Interference and Noise Ratio x pos (m)y pos (m) .1000.50005001000.1000.800.600.400.20002004006008001000708090100110120130140 .1000.50005001000.40.30.20.10010203040x pos (m)macroscopic pathloss [dB]shadow fading [dB]Fig. 2. Left: Macrosopic pathloss LM;b11;uj , 70 dB MCL, 3dB = 65
/15 dBi antenna, 128:1 + 37:6 log10(R [Km]) pathloss. Right: space-correlated shadow fading LS;b11;uj (SINR) has been utilized as metric [13]. Specifically, a persubcarri
