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PA_RL_permutatedFemtocells.m
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PA_RL_permutatedFemtocells.m
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Simulation of Power Allocation in femtocell network using
% Reinforcement Learning with random adding of femtocells to the network
%
%
function Q = PA_RL_permutatedFemtocells(fbsCount,femtocellPermutation, NumRealization,QTable, saveNum)
%% Initialization
% clear all;
clc;
format short
format compact
tic;
%% Parameters
Pmin = -20; %dBm
Pmax = 25; %dBm
Npower = 31;
dth = 25;
Kp = 100; % penalty constant for MUE capacity threshold
Gmue = 1.37; % bps/Hz
StepSize = 1.5; % dB
K = 1000;
PBS = 50 ; %dBm
sinr_th = 1.64;%10^(2/10); % I am not sure if it is 2 or 20!!!!!
gamma_th = log2(1+sinr_th);
%% Minimum Rate Requirements for N MUE users
N = 3;
q_mue = 1.00; q_fue=1.0;
%% Q-Learning variables
% Actions
actions = zeros(1,31);
for i=1:31
actions(i) = Pmin + (i-1) * 1.5; % dBm
end
% States
states = allcomb(0:3 , 0:3); % states = (dMUE , dBS)
% Q-Table
% Q = zeros(size(states,1) , size(actions , 2));
Q1 = ones(size(states,1) , size(actions , 2)) * inf;
alpha = 0.5; gamma = 0.9; epsilon = 0.1 ; Iterations = 50000;
%% Generate the UEs
mue(1) = UE(204, 207);
% mue(1) = UE(150, 150);
% mue(1) = UE(-200, 0);
% selectedMUE = mue(mueNumber);
MBS = BaseStation(0 , 0 , 50);
%%
%Generate fbsCount=16 FBSs
FBS_Max = cell(1,16);
for i=1:3
% if i<= fbsCount
FBS_Max{i} = FemtoStation(180+(i-1)*35,150, MBS, mue, 10);
% end
end
for i=1:3
% if i+3<= fbsCount
FBS_Max{i+3} = FemtoStation(165+(i-1)*30,180, MBS, mue, 10);
% end
end
for i=1:4
% if i+6<= fbsCount
FBS_Max{i+6} = FemtoStation(150+(i-1)*35,200, MBS, mue, 10);
% end
end
for i=1:3
% if i+10<= fbsCount
FBS_Max{i+10} = FemtoStation(160+(i-1)*35,240, MBS, mue, 10);
% end
end
for i=1:3
% if i+13<= fbsCount
FBS_Max{i+13} = FemtoStation(150+(i-1)*35,280, MBS, mue, 10);
% end
end
%%
%
FBS = cell(1,fbsCount);
for i=1:fbsCount
FBS{i} = FBS_Max{femtocellPermutation(i)};
end
%% Initialization and find MUE Capacity
% permutedPowers = npermutek(actions,3);
permutedPowers = randperm(size(actions,2),size(FBS,2));
% y=randperm(size(permutedPowers,1));
for j=1:size(FBS,2)
fbs = FBS{j};
fbs = fbs.setPower(actions(permutedPowers(j)));
fbs = fbs.getDistanceStatus;
FBS{j} = fbs;
end
% selectedMUE.SINR = SINR_MUE(FBS, BS, selectedMUE, -120, 1000);
% selectedMUE.C = log2(1+selectedMUE.SINR);
% if selectedMUE.C < gamma_th
% I = 1;
% else
% I = 0;
% end
%
% for j=1:size(FBS,2)
% fbs = FBS{j};
% fbs.state(1,1) = I;
% FBS{j} = fbs;
% end
%% Calc channel coefficients
fbsNum = size(FBS,2);
G = zeros(fbsNum+1, fbsNum+1); % Matrix Containing small scale fading coefficients
L = zeros(fbsNum+1, fbsNum+1); % Matrix Containing large scale fading coefficients
[G, L] = measure_channel(FBS,MBS,mue,NumRealization);
%% Main Loop
fprintf('Loop for %d number of FBS :\t', fbsCount);
textprogressbar(sprintf('calculating outputs:'));
count = 0;
MUE_C = zeros(1,Iterations);
xx = zeros(1,Iterations);
errorVector = zeros(1,Iterations);
% K1 is distance of selectedMUE from Agents
% k1 = zeros(1,size(FBS,2));
dth = 25; %meter
% Kp = 100;
% for i=1:size(FBS,2)
% k1(i) = (sqrt((FBS{i}.X-selectedMUE.X)^2+(FBS{i}.Y-selectedMUE.Y)^2))/dth;
% end
for episode = 1:Iterations
textprogressbar((episode/Iterations)*100);
permutedPowers = randperm(size(actions,2),size(FBS,2));
if (episode/Iterations)*100 < 80
% Action selection with epsilon=0.1
if rand<epsilon
for j=1:size(FBS,2)
fbs = FBS{j};
fbs = fbs.setPower(actions(permutedPowers(j)));
FBS{j} = fbs;
end
else
for j=1:size(FBS,2)
fbs = FBS{j};
for kk = 1:size(states,1)
if states(kk,:) == fbs.state
break;
end
end
[M, index] = max(QTable(kk,:));
fbs = fbs.setPower(actions(index));
FBS{j} = fbs;
end
end
else
for j=1:size(FBS,2)
fbs = FBS{j};
for kk = 1:size(states,1)
if states(kk,:) == fbs.state
break;
end
end
[M, index] = max(QTable(kk,:));
fbs = fbs.setPower(actions(index));
FBS{j} = fbs;
end
end
% calc FUEs and MUEs capacity
SINR_FUE_Vec = SINR_FUE_2(G, L, FBS, MBS, -120);
C_FUE_Vec = log2(1+SINR_FUE_Vec);
for i=1:size(mue,2)
MUE = mue(i);
MUE.SINR = SINR_MUE_4(G, L, FBS, MBS, MUE, -120);
MUE = MUE.setCapacity(log2(1+MUE.SINR));
mue(i)=MUE;
end
% MUE_C(1,episode) = selectedMUE.C;
xx(1,episode) = episode;
% R = K - (selectedMUE.SINR - sinr_th)^2;
% deviation_FUE=0.0;
% for i=1:size(FBS,2)
% deviation_FUE = deviation_FUE + (fbs.C_FUE-q_M)^2;
% end
dum1 = 1.0;
for i=1:size(mue,2)
dum1 = dum1 * (mue(i).C-q_mue)^2;
end
dum2 = 0.0;
minCFUE = inf;
for j=1:size(FBS,2)
fbs = FBS{j};
fbs = fbs.setCapacity(log2(1+SINR_FUE_Vec(j)));
if minCFUE>fbs.C_FUE
minCFUE = fbs.C_FUE;
end
dum2 = dum2 + (fbs.C_FUE-q_fue)^2;
FBS{j}=fbs;
end
% R = K - dum2*dum1;
for j=1:size(FBS,2)
fbs = FBS{j};
qMax=max(QTable,[],2);
for jjj = 1:31
if actions(1,jjj) == fbs.P
break;
end
end
for kk = 1:size(states,1)
if states(kk,:) == fbs.state
break;
end
end
% CALCULATING NEXT STATE AND REWARD
beta = fbs.dMUE/dth;
% if (minCFUE>q_fue) && (mue(1).C>q_mue)
% R = beta*fbs.C_FUE;
% R = beta*fbs.C_FUE;
% else
R = beta*fbs.C_FUE*(mue(1).C).^2 -(fbs.C_FUE-q_fue).^2 - (1/beta)*dum1;
% R = beta*fbs.C_FUE - (1/beta)*dum1*(minCFUE-q_fue).^2;
% R = beta*fbs.C_FUE - (1/beta)*dum1;
% en
% if R<0
% R=0;
% end
for nextState=1:size(states,1)
if states(nextState,:) == fbs.state
QTable(kk,jjj) = QTable(kk,jjj) + alpha*(R+gamma*qMax(nextState)-QTable(kk,jjj));
end
end
FBS{j}=fbs;
end
% break if convergence: small deviation on q for 1000 consecutive
errorVector(episode) = sum(sum(abs(Q1-QTable)));
if sum(sum(abs(Q1-QTable)))<0.001 && sum(sum(QTable >0))
if count>1000
episode % report last episode
break % for
else
count=count+1; % set counter if deviation of q is small
end
else
Q1=QTable;
count=0; % reset counter when deviation of q from previous q is large
end
% if selectedMUE.C < gamma_th
% I = 1;
% else
% I = 0;
% end
%
% for j=1:size(FBS,2)
% fbs = FBS{j};
% fbs.state(1,1) = I;
% FBS{j} = fbs;
% end
end
Q = QTable;
answer.mue = mue;
answer.Q = QTable;
answer.Error = errorVector;
answer.FBS = FBS;
min_CFUE = inf;
for j=1:size(FBS,2)
C = FBS{1,j}.C_profile;
c_fue(1,j) = sum(C(40000:size(C,2)))/(-40000+size(C,2));
if min_CFUE > c_fue(1,j)
min_CFUE = c_fue(1,j);
end
end
sum_CFUE = 0.0;
for i=1:size(FBS,2)
sum_CFUE = sum_CFUE + c_fue(1,i);
end
answer.C_FUE = c_fue;
answer.sum_CFUE = sum_CFUE;
answer.min_CFUE = min_CFUE;
answer.episode = episode;
answer.time = toc;
QFinal = answer;
save(sprintf('oct10/R_18_time/pro_%d_%d.mat',fbsCount, saveNum),'QFinal');
end