【覆盖率分析】蜂窝异构网络（多径散热场）覆盖率分析【含Matlab源码 3318期】.zip

clear all; close all; clc; ParameterSet = 'pico'; EvaluationMode = 'simulation'; % {simulation, analysis} Thinning_Assumption = 'on'; fprintf('Parameter Set: %s\nEvaluation Mode: %s\nThinning Assumption: %s\n', ParameterSet, EvaluationMode, Thinning_Assumption); if strcmp(ParameterSet,'macro') % Parameter-Set: MACRO PM = 40; % Macro BS transmit power alphaL = 2.42; % LOS exponent alphaN = 4.28; % NLOS exponent cL = 10^((103.4 - 24.2*3)/10); % Free space path loss intercept自由空间路径损耗截断指数（视距、非视距） cN = 10^((131.1 - 42.8*3)/10); RCM = 250; % Average cell size else % Parameter Set: PICO PM = 0.25; alphaL = 2; % LOS exponent alphaN = 4; % NLOS exponent cL = 10^((103.8 - 20.9*3)/10); % Free space path loss intercept cN = 10^((145.4 - 37.5*3)/10); RCM = 40; % Average cell size end % dependent parameter lambdaM = 1/(2*sqrt(3)*RCM^2);%宏基站分布密度 % Buildings LB = 10^(-1);%穿墙损耗？ Ri = 15; % Indoor area radius [m]建筑覆盖面积 % Indoor PS = 0.1; % Small cell BS transmit power % Small scale fading; parameters for Nakagami fading. Omega = 1;%多径散射场的平均功率 NL = 1; % 3 NN = 1; % 2 if strcmp(EvaluationMode, 'simulation') % Noise Bandwidth = 10*10^6;%10MB NoisePowerDensity = 10^(-174/10)*10^-3; NoiseFigure = 10^(9/10); N0 = NoisePowerDensity*Bandwidth*NoiseFigure; % Noise Power else N0 = 0; end % Calibration coefficients 校准系数 % alphaB = 1; %0.85; % alphaSIR = 1; %0.5; % Numerical evaluation 数值计算 Tmin = 0.1; Tmax = 2^6 - 1; aCvector = 0.2:0.1:0.8; etaVector = [0.2, 0.8]; BSVector = [1, 6]; LWVector = [1,10^(-2)];%穿墙损耗 % PcEvalVec = -10:2:20; % SimAreaRadius = 1000;%仿真半径 SimArea = (SimAreaRadius^2)*pi;%仿真面积 NrOfRealizations = 10^3;%仿真数number of realizations %% PREALLOCATE MEMORY % Distances and misc NumberOfVisibleSmallCells = zeros(NrOfRealizations, length(aCvector), length(etaVector), length(BSVector), length(LWVector));%创建多维矩阵用于存储计算结果 AssociationToSmallCell = zeros(NrOfRealizations, length(aCvector), length(etaVector), length(BSVector), length(LWVector)); MacroDistance = zeros(NrOfRealizations, length(aCvector), length(etaVector), length(BSVector), length(LWVector)); SmallCellDistance = zeros(NrOfRealizations, length(aCvector), length(etaVector), length(BSVector), length(LWVector)); % SIR Results MacroSIRSim = zeros(NrOfRealizations, length(aCvector), length(etaVector), length(BSVector), length(LWVector)); SmallCellSIRSim = zeros(NrOfRealizations, length(aCvector), length(etaVector), length(BSVector), length(LWVector)); % Rate Results MacroRateSim = zeros(NrOfRealizations, length(aCvector), length(etaVector), length(BSVector), length(LWVector)); SmallcellRateSim = zeros(NrOfRealizations, length(aCvector), length(etaVector), length(BSVector), length(LWVector)); %% ENTER MAIN SIMULATION LOOP % Indoor area coverage ratio for ii=1:length(aCvector) fprintf('pI = %g:', aCvector(ii)); % Occupation probability for jj = 1:length(etaVector) for kk = 1:length(BSVector) eta = etaVector(jj); BS = BSVector(kk); lambdaB = -log(1-aCvector(ii))/(Ri^2*pi); % Indoor area density lambdaS = eta*lambdaB; betaB = 2*lambdaB*Ri; pB = lambdaB*Ri^2*pi; fprintf(' %g ', eta); for ll = 1:NrOfRealizations % Realizations if ~mod(ll,NrOfRealizations/10) fprintf('*'); end %% Macro BSs NumberOfMacroBSs = poissrnd(SimArea*lambdaM); MacroRiAll = SimAreaRadius * sqrt(rand(NumberOfMacroBSs,1)); % Additional macro BS to check approximation if strcmp(Thinning_Assumption,'off') NumberOfAddMacroBSs = poissrnd(1/3*Ri^2*pi*lambdaM); MacroRiAdd = Ri * sqrt(rand(NumberOfAddMacroBSs,1)); MacroRiAll = [MacroRiAll; MacroRiAdd]; end % Calculate path losses from macro BSs MacroPathLosses = 1/cN* MacroRiAll.^(-alphaN) .* exp(-(betaB*MacroRiAll-pB)*(1-LB)); %% Small Cell BSs NumberOfSmallcells = poissrnd(SimArea * lambdaS); SmallcellRiAll = SimAreaRadius * sqrt(rand(NumberOfSmallcells,1)); % Exclude smallcells inside Ri IncludedSmallcells = (SmallcellRiAll - Ri) >= 0; SmallcellRiGuard = SmallcellRiAll(IncludedSmallcells); BernoulliParamsSC = exp(-betaB*(SmallcellRiGuard)); VisibleSCs = logical(binornd(1,BernoulliParamsSC)); InvisibleSCs = logical(1-VisibleSCs); % Path Losses % Visible Small Cells SmallcellRiLOS = SmallcellRiGuard(VisibleSCs); SmallcellPathLossesLOS = 1/cL * SmallcellRiLOS.^(-alphaL); % Invisible Small Cells SmallcellRiNLOS = SmallcellRiGuard(InvisibleSCs); SmallcellPathLossesNLOS = 1/cN * SmallcellRiNLOS.^(-alphaN).*exp(-(betaB*SmallcellRiNLOS-pB)*(1-LB)); %% Loop over wall penetration losses for mm = 1:length(LWVector) LW = LWVector(mm); % Received powers SmallCellReceivePowers = sort(PS * SmallcellPathLossesLOS * LW, 'descend'); SmallCellReceivePowersInt = PS * SmallcellPathLossesNLOS * LW; MacroReceivePowers = sort(PM * MacroPathLosses , 'descend'); AssociationToSmallCell_ = -1; % Identify faulty simulations by -1. if ~isempty(MacroReceivePowers) if ~isempty(SmallCellReceivePowers) AssociationToSmallCell_ = BS * SmallCellReceivePowers(1) > MacroReceivePowers(1); else AssociationToSmallCell_ = 0; end end % Generate small scale fading % Macro gM = gamrnd(NN, Omega/NN, size(MacroReceivePowers)); % Small cells gSLOS = gamrnd(NL, Omega/NL, size(SmallCellReceivePowers)); % Small cell interferers gSNLOS = gamrnd(NN, Omega/NN, size(SmallCellReceivePowersInt)); SIRbounded = 0; Rate = 0; % Calculate SIR and rate % If associated with small cell if AssociationToSmallCell_==1 DesiredRxPower = SmallCellReceivePowers(1); SmallCellReceivePowers(1) = []; g0 = gSLOS(1); gSLOS(1) = []; SIRraw = DesiredRxPower*g0 / (nansum(gM.*MacroReceivePowers)+nansum(gSLOS.*SmallCellReceivePowers)+nansum(gSNLOS.*SmallCellReceivePowersInt)); SIRbounded = max(min(SIRraw,Tmax),Tmin); Rate = log2(1 + SIRbounded); % if associated with outdoor BS elseif Association