% Define time range (in hours)
time_hours = 0:23;

% Define the network data traffic profile (just an example, replace with actual data)
traffic_profile = [10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 70, ...
                   80, 85, 90, 80, 75, 70, 65, 60, 55, 45, 40, 35];

% Define different SIR values
SIR_values = [5, 10, 15, 20];

% Preallocate arrays to store activity rates for different SIR values
activity_rates = zeros(length(SIR_values), length(time_hours));

% Calculate small cell BS activity rate for each SIR value
for i = 1:length(SIR_values)
    SIR = SIR_values(i);
    % Example calculation for activity rate, replace with actual calculations
    activity_rates(i, :) = 3 * traffic_profile / max(traffic_profile); % Example: Same activity rate for all SIR values
    activity_rates(i, :) = 2 * traffic_profile / min(traffic_profile);
end

% Plotting the activity rates for different SIR values
figure;
hold on;
for i = 1:length(SIR_values)
    plot(time_hours, activity_rates(i, :), 'LineWidth', 2, 'DisplayName', ['SIR = ', num2str(SIR_values(i))]);
end

for j = 2:length(SIR_values-1)
    plot(time_hours, activity_rates(j, :), 'LineWidth', 4, 'DisplayName', ['SIR = ', num2str(SIR_values(j))]);
end
xlabel('Time (hours)');
ylabel('Small Cell BS Activity Rate');
title('Small Cell BS Activity Rate vs. Time for Different SIR Values');
legend('Location', 'northwest');
grid on;

% Monte Carlo Simulation for Random BS Activation

% Define a finer time range (in hours) for the Monte Carlo simulation
time_hours = 0:0.1:24;

% Define the probability of turning on a BS at each time step
% Here, we use the Monte Carlo simulation to generate random probabilities
% This randomization represents the uncertain nature of BS activation in real networks
probability_turn_on = rand(size(time_hours)); % Generate random probabilities between 0 and 1

% Set a threshold for turning on a BS (adjust as needed)
% If the randomly generated probability exceeds the threshold, the BS turns on
threshold = 0.5; % Example threshold for BS activation

% Determine which BS are turned on based on the generated random probabilities
BS_turned_on = probability_turn_on > threshold;

% Plot the results of the Monte Carlo simulation
figure;
plot(time_hours, BS_turned_on, 'b-', 'LineWidth', 2);
xlabel('Time (hours)');
ylabel('Base Station Turned On');
title('Random Turning On of Base Stations (Monte Carlo Simulation)');
grid on;

% Clarification: The use of the Monte Carlo method is implemented here by generating random
% probabilities at each time step and comparing them to a threshold to determine BS activation.
% This approach introduces randomness and simulates uncertainty, which is the core concept
% of Monte Carlo simulations.