Hi everyone.....
could anyone help......
I need to find th optimal value of x0 and x1 to maximize R, where x1=d-x0..........the function MATLAB code:
close all; clear all; clc; % System parameters F = 10000; % Number of files S = 100; % SBS cache capacity (set to 100) M = 50; % Cash capacity fraction alpha = 2; % Path loss exponent for LOS link c = 3e8; % Light speed fr = 1e12; % Operating frequency B = 10e6; % System bandwidth epsilon = 0.8; % Skewness factor K = 0.0016; % Molecular absorption coefficient P_M = 10^(64/10); % Transmit power MBS P_S = 10^(30/10); % Transmit power SBS sigma = 10^(-90/10); % Noise power N_L = 512; N_M = 16; eta = 1; x2 =30; % RIS-MBS distance d_range = 1:1:40; R = zeros(size(d_range)); for i = 1:length(d_range) d = d_range(i); sum1 = 0; for f = 1:F sum1 = sum1 + f^(-epsilon); end sum2 = 0; for f = 1:M sum2 = sum2 + f^(-epsilon)/sum1; end sum3 = 0; for f = (M + 1):(M + (S - M) / eta) sum3 = sum3 + (f^(-epsilon)) / sum1; end sum3 = sum3 * eta; beta = (c/(4*pi*fr))^2; % Spreading loss index Rs = B * log2(1 + (P_S * beta * (d-x1)^(-alpha) * exp(-K * (d-x1))) / sigma); Rm = B * log2(1 + (P_M * (N_L^2) * N_M * (beta * (d-x0)^(-alpha) * exp(-K * (d-x0))) * (beta * x2^(-alpha) * exp(-K * x2))) / sigma); Rt = Rs * (sum2 + sum3) + Rm * (1 - (sum2 + sum3)); R(i) = Rt; end figure plot(d_range, R, 'b^-') xlabel('SBS-RIS Distance, d') ylabel('Achievable Rate')
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seems to me that R curves goes up as x0 goes closer to zero (but not rqual to zero otherwise you get Inf values)
also all the small for loops to create the sum1, sum2,sum3 variables can be replaced with sum function directly
close all; clear all; clc; x0_range = [0.01 0.25 0.5 1]; % System parameters F = 10000; % Number of files S = 100; % SBS cache capacity (set to 100) M = 50; % Cash capacity fraction alpha = 2; % Path loss exponent for LOS link c = 3e8; % Light speed fr = 1e12; % Operating frequency B = 10e6; % System bandwidth epsilon = 0.8; % Skewness factor K = 0.0016; % Molecular absorption coefficient P_M = 10^(64/10); % Transmit power MBS P_S = 10^(30/10); % Transmit power SBS sigma = 10^(-90/10); % Noise power N_L = 512; N_M = 16; eta = 1; x2 =30; % RIS-MBS distance d_range = 1:1:40; R = zeros(numel(d_range),numel(x0_range)); for k = 1:numel(x0_range) x0 = x0_range(k); for i = 1:length(d_range) d = d_range(i); x1 = d-x0; % added line % sum1 = 0; % for f = 1:F % sum1 = sum1 + f^(-epsilon); % end f = 1:F; sum1 = sum(f.^(-ep
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