【无人机】基于Matlab实现高效局部地图搜索算法附论文

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智能优化算法  神经网络预测 雷达通信  无线传感器

信号处理 图像处理 路径规划 元胞自动机 无人机

⛄ 内容介绍

This paper studies the optimal unmanned aerial vehicle (UAV) placement problem for wireless networking. The UAV operates as a flying wireless relay to provide coverage extension for a base station (BS) and deliver capacity boost to a user shadowed by obstacles. While existing methods rely on statistical models for potential blockage of a direct propagation link, we propose an approach capable of leveraging local terrain information to offer performance guarantees. The proposed method allows to strike the best trade-off between minimizing propagation distances to ground terminals and discovering good propagation conditions. The algorithm only requires several propagation parameters, but it is capable to avoid deep propagation shadowing and is proven to find the globally optimal UAV position. Only a local exploration over the target area is required, and the maximum length of search trajectory is linear to the geographical scale. Hence, it lends itself to online search. Significant throughput gains are found when compared to other positioning approaches based on statistical propagation models.

具体模型见https://xueshu.baidu.com/usercenter/paper/show?paperid=14170ca0ug5a0ju0nt6q0ek0vx244295&site=xueshu_se​

⛄ 部分代码

% Massive simulation

close all

clear

addpath(genpath('lib')),

Nue = 10000;    % <- reduce this number to shorter simulation time (coarser results)

DATA = load('citymap/urbanMapSingleUserK2.mat');

U = DATA.U; PosBS = DATA.PosBS; 

DATA = load('citymap/losStatistics.mat');

losStat.Plos = DATA.Plos;

losStat.ElvAngles = DATA.ElvAngles;

clear DATA

load('citymap/topologyK2.mat');

U.K = 2;

if U.K == 2

    U.Alpha = [-21.4, -30.3];

    U.Beta =[-36.92, -38.42];

elseif U.K == 3

    U.Alpha = [-22, -28, -36];

    U.Beta =[-28, -24, -22];

else

    error('K should be 2 or 3.');

end

U.A0 = -20.8; U.B0 = -38.5;

U.A1 = U.Alpha(1); U.B1 = U.Beta(1); 

U.A2 = U.Alpha(2); U.B2 = U.Beta(2);

Noise_dBm = -80;

Power_BS_dBm = 33;

Power_UAV_dBm = 33;

U.Noise = 10^(Noise_dBm/10) / 1000; % Watt in linear scale

U.Pb = 10^(Power_BS_dBm/10) / 1000; 

U.Pd = 10^(Power_UAV_dBm/10) / 1000; 

U.Hbs = 45;     % meter, BS height

U.Hmin = 45;    % meter, minimum UAV operation height

U.Hdrone = 50;  % meter, UAV search height

stepSizeMeter = 5;  % UAV search step size

fun = @(x,y) max(-log2(1 + U.Pd * real(x)), -log2(1 + U.Pb * real(y)));

fun0 = @(x) -log2(1 + U.Pb * x);

% Ergodic capacity

SNRs_dB = -10:2:20; Ks_dB = [9, -Inf];

Rerg = capacity_ergodic(Ks_dB, SNRs_dB);

fun1 = @(x,y) max(- max(0, ppval(spline(SNRs_dB, Rerg(1, :)), 10 * log10(U.Pd * real(x)))), ... 

                  - log2(1 + U.Pb * real(y))); % UAV-UE_LOS(K-factor = 9dB, 

              

fun2 = @(x,y) max(- max(0, ppval(spline(SNRs_dB, Rerg(2, :)), 10 * log10(U.Pd * real(x)))), ... 

                  - log2(1 + U.Pb * real(y))); % UAV-UE_NLOS, Rayleigh fading 

%%

N_scheme = 6;

tic

Nue = min(size(Topology, 1), Nue);

Rates0 = zeros(Nue, N_scheme);

strongUserIds = zeros(Nue, 1);

failIds = zeros(Nue, 1);

parfor i = 1:Nue

    

    PosUE = Topology{i}.PosUE; 

    Blds = Topology{i}.Blds;

    BldTypes = Topology{i}.BldTypes;

    BldLines = Topology{i}.BldLines; 

    BldHeight = Topology{i}.BldHeight; 

    Nbld = size(Blds, 1);

    

    los = IsLosK(PosUE, [PosBS, U.Hbs], BldLines, BldHeight, U.Hdrone, BldTypes);

    if los == 1

        strongUserIds(i) = 1;

        % continue    % We are only interested in the case where the direct BS-user link is blocked

    end

    

    urbanMap = struct();

    urbanMap.BldLines = BldLines;

    urbanMap.BldHeight = BldHeight;

    urbanMap.BldTypes = BldTypes;

    

    % Direct BS-user link

    k = round((1 - los) * (U.K - 1) + 1);   % propagation segment index

    d = norm([PosBS, U.Hbs] - [PosUE, 0], 2);

    snr = 10 ^ ((U.Alpha(k) * log10(d) + U.Beta(k)) / 10) / U.Noise;

    F0 = fun0(snr);

    

    try

        % [Fmin3, Xhat3] = finduavpos3d(PosUE, PosBS, U, fun, stepSizeMeter, urbanMap);

        % Fmin3 = min(Fmin3, F0);

        Fmin3 = 0;

        

        [~, Xhat2] = finduavpos(PosUE, PosBS, U, fun, stepSizeMeter, urbanMap);

        los = IsLosK(PosUE, [Xhat2, U.Hdrone], BldLines, BldHeight, U.Hdrone, BldTypes);

        Fmin2 = getcostf2DK_ergodic([Xhat2, U.Hdrone], [PosUE, 0], [PosBS, U.Hbs], los, U, fun1, fun2);

        % Fmin2 = min(Fmin2, F0);

        

        [~, Xhat1] = finduavpos1d(PosUE, PosBS, U, fun, stepSizeMeter, urbanMap);

        los = IsLosK(PosUE, [Xhat1, U.Hdrone], BldLines, BldHeight, U.Hdrone, BldTypes);

        Fmin1 = getcostf2DK_ergodic([Xhat1, U.Hdrone], [PosUE, 0], [PosBS, U.Hbs], los, U, fun1, fun2);

        % Fmin1 = min(Fmin1, F0);

        

        % [Fmin_exhst, Xhat_exhst] = finduavpos2d_exhst(PosUE, PosBS, U, fun, stepSizeMeter, urbanMap);

        Fmin_exhst = Fmin3;

        

        [~, XhatStat] = finduavposStat(PosUE, PosBS, U, fun, stepSizeMeter, urbanMap, losStat);

        los = IsLosK(PosUE, [XhatStat, U.Hdrone], BldLines, BldHeight, U.Hdrone, BldTypes);

        FminStat = getcostf2DK_ergodic([XhatStat, U.Hdrone], [PosUE, 0], [PosBS, U.Hbs], los, U, fun1, fun2);

        % FminStat = min(FminStat, F0);

    catch

        Fmin1 = 0;

        Fmin2 = 0;

        Fmin3 = 0;

        Fmin_exhst = 0;

        FminStat = 0;

        failIds(i) = 1;

    end

   

    Rates0(i, :) = - [F0, FminStat, Fmin1, Fmin2, Fmin3, Fmin_exhst];

end

toc

%% Plot results

my_line_styles = {'-', '--', '-.', ':'}.';

Alg_scheme_name = {

    'Direct BS-User linkxx'

    'Probabilistic Alg'

    'Simple Search'

    'Proposed'

    'Proposed (3D)'

    'Exhaustive'

};

schemes_to_show = [1 2 3 4 6];

N_scheme_to_show = length(schemes_to_show);

validUserId = failIds < 1;

Rates = Rates0(validUserId, :);

Nue = size(Rates, 1);

maxdata = max(Rates(:));

Npt = 40;

XI = sort([0.1 0.17 0.3 0.5 (0:1/(Npt - 1 - 4):1) * maxdata], 'ascend');

X_data = zeros(Npt, N_scheme_to_show);

F_data = zeros(Npt, N_scheme_to_show);

for i = 1:N_scheme_to_show

    n = schemes_to_show(i);

    

    r_vec = Rates(:, n);

    [F1,X1] = ksdensity(r_vec, XI, 'function', 'cdf');

    

    X_data(:, i) = X1(:);

    F_data(:, i) = F1;

    

end

figure(1),

h = plot(X_data, F_data,'linewidth', 2);

set(gca, 'FontSize', 14);

legend(Alg_scheme_name{schemes_to_show}, 'location', 'southeast');

xlim([0 ceil(max(Rates(:)))]);

set(gca, 'YTick', 0:0.2:1);

xlabel('bps/Hz');

ylabel('CDF');

tune_figure,

set(h(1), 'linewidth', 2);

set(h(1), 'Marker', '*', 'Markersize', 6);

set(h(1), 'LineStyle', ':');

set(h(2), 'LineStyle', '-.');

set(h(3), 'LineStyle', ':');

set(h(4), 'LineStyle', '-');

set(h(4), 'LineWidth', 3);

set(h(5), 'linestyle', '--');

set(h(5), 'LineWidth', 3);

% ----

schemes_to_show = [1 2 3 4];

figure(2),

rateNoUav = Rates(:, 1);

[~, sortedIndex] = sort(rateNoUav, 'ascend');

low20percentileIndex = sortedIndex(1:round(Nue * 0.2));

high20percentileIndex = sortedIndex(round(Nue * 0.8): end);

RateLow = mean(Rates(low20percentileIndex, schemes_to_show), 1);

RateMean = mean(Rates(:, schemes_to_show), 1);

RateHigh = mean(Rates(high20percentileIndex, schemes_to_show), 1);

h = bar([RateLow

         RateMean

         RateHigh]);

set(gca, 'FontSize', 14);

set(h, 'linewidth', 2);

ylim([0, 10]);

legend(Alg_scheme_name{schemes_to_show}, 'location', 'northwest');

set(gca, 'XTickLabel', {'20th percentile', 'Mean', 'Top 20th percentile'});

set(gca, 'YTick', 0:2:10);

ylabel('Average end-to-end throughput [bps/Hz]');

% label the bars

Xdata = [RateLow

         RateMean

         RateHigh];

bartext = [];

for i = 1:size(Xdata, 1)

    for j = 1:size(Xdata, 2)

        bartext(i, j) = text(i + (j - 2.5) * 0.18, Xdata(i, j) + 0.05, ...

            sprintf('%1.2f', Xdata(i, j)), 'fontsize', 12);

    end

end

% Use the handles TH to modify some properties

set(bartext,'Horizontalalignment','center',...

'verticalalignment','bottom') ;

tune_figure,

[im_hatch,colorlist] = applyhatch_pluscolor(gcf,'\-x./+',0,0,[],150,2,2);

⛄ 运行结果

⛄ 参考文献

[1] Chen J ,  Gesbert D . Efficient Local Map Search Algorithms for the Placement of Flying Relays[J].  2018.

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