【无人机】基于博弈论的无人机授权应急网络中用户设备关联模拟器附matlab代码

该工作评估了在灾难场景中使用游戏理论来确定无人机支持的蜂窝网络的用户关联策略，以最大化服务质量。通过Matlab仿真，研究了从5G网络、智能城市到无人机通信的灾后通信机会，对比了信号基础的用户关联和基于游戏理论的用户关联策略对灾后服务质量和效率的影响。

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⛄ 内容介绍

The availability of communication channels able to offer a good level of quality of service is pivotal after theoccurrence of a disaster in order to share critical info or to coordinate search and rescue operations.

With the developments in both Smart City and 5G paradigms new communication opportunities arise thanks to ad-hoc networks among smartphones and surviving IoT devices, cellular networks as well as wireless networks based on vehicles.

With all these opportunities, the choice of the communication medium to use to access the Internet by the user equipment must consider the quality of service achievable through each of them, while reducing the amount of energy consumed for communicating, given that it might not be possible to have access to energy sources.

This work evaluates the adoption of Game Theory to find association strategies that maximize the quality of service perceived in post-disaster scenarios in which UAVs have been deployed to support the cellular network.

⛄ 部分代码

% Add functions to path

addpath('association', 'constants', 'data', 'evaluation', 'disruption', 'performance', 'placement', 'utils');

% Set random number generator seed for reproducibility

% rng(6);

% Definition of the area of interest

vertex_1 = [40.77405, 14.79425];

vertex_2 = [40.76728, 14.80319];

altitude = 280;

origin = [min(vertex_1(1), vertex_2(1)) + (max(vertex_1(1), vertex_2(1)) - min(vertex_1(1), vertex_2(1))) / 2, ...

min(vertex_1(2), vertex_2(2)) + (max(vertex_1(2), vertex_2(2)) - min(vertex_1(2), vertex_2(2))) / 2, altitude];

% Selection of network operator and technology

radio = ["LTE", "UMTS"];

mcc = 222;

mnc = [10, 6];

fprintf('Getting towers in the area ...');

towers_table = tower_placement(vertex_1, vertex_2, origin, altitude, radio, mcc, mnc);

fprintf('done!\n');

% User placement

number_of_users = 500;

fprintf('Placing users in the area ...');

users_table = user_placement(vertex_1, vertex_2, altitude, number_of_users);

fprintf('done!\n');

% Signal-based user association before disaster

fprintf('Performing signal-based user association before disaster ...');

[base_stations_info_table_sbbd, user_association_table_sbbd] = signal_based_association(users_table, towers_table, empty_uavs_table, true);

user_association_plot(users_table, towers_table, empty_uavs_table, user_association_table_sbbd, 'Signal-based pre-disaster');

fprintf('done!\n');

% Natural disaster simulation

center = [0, 0];

radius = 250;

destruction_probability = 0.4;

damage_probability = 0.8;

fprintf('Simulating disaster ...');

towers_table_ad = natural_disaster(center, radius, destruction_probability, damage_probability, towers_table);

fprintf('done!\n');

% Signal-based user association after disaster without UAVs

fprintf('Performing signal-based user association before disaster ...');

[base_stations_info_table_sbad, user_association_table_sbad] = signal_based_association(users_table, towers_table_ad, empty_uavs_table, true);

user_association_plot(users_table, towers_table_ad, empty_uavs_table, user_association_table_sbad, 'Signal-based post-disaster');

fprintf('done!\n');

% UAV placement

cell_size = 20;

max_uavs = 48;

fprintf('Placing UAVs in the area (this may take a while) ...\n');

uavs_table = uav_placement(vertex_1, vertex_2, origin, altitude, cell_size, max_uavs, users_table, towers_table_ad);

% Signal-based user association after disaster with UAVs deployed

fprintf('Performing signal-based user association after UAV deployment ...');

[base_stations_info_table_sbadwu, user_association_table_sbadwu] = signal_based_association(users_table, towers_table_ad, uavs_table, true);

user_association_plot(users_table, towers_table_ad, uavs_table, user_association_table_sbadwu, 'Signal-based post-disaster with UAVs deployed');

fprintf('done!\n');

% Game-theoretic user association after disaster with UAVs deployed

game_learning_rate = 0.05;

fprintf('Performing game-theoretic user association after UAV deployment ...');

[base_stations_info_table_gtadwu, user_association_table_gtadwu, changes_history] = game_theoretic_association(users_table, towers_table_ad, uavs_table, true, game_learning_rate);

user_association_plot(users_table, towers_table_ad, uavs_table, user_association_table_gtadwu, 'Game-theoretic post-disaster with UAVs deployed');

fprintf('done!\n');

% Plot user throughput distributions

throughput_distributions = [user_association_table_gtadwu.throughput user_association_table_sbadwu.throughput user_association_table_sbad.throughput user_association_table_sbbd.throughput];

labels = {'Ottimizzato', 'Post-disastro con UAV', 'Post-disastro', 'Pre-disastro'};

throughput_distribution_plot(throughput_distributions, labels);

% Plot the number of strategy changes for each round of the game

game_changes_history_plot(changes_history);