02-Matlab Subroutines Used in the Simulation

MATLAB SUBROUTINES USED IN THE SIMULATION

1. Channel allocation Based on Aggressive Algorithm Given the channel state, to allocate the

channel based on the aggressive Algorithm. Input (no,S,sto_state) & output cell array X with 4

columns(sto_state,ch_lock, norelock,chrelock)

function[X]=allo_agra7(no,S,sto_state);

ch_lock=0;

norelock=0;

chrelock=0;

nor_cost=call_cost7(no,S,sto_state);

if min(nor_cost)~=inf

chslock=find(nor_cost==min(nor_cost));

ch_lock=chslock(1,1);

sto_state(no,(ch_lock+2))=1;

sto_state(no,1)=(sto_state(no,1))+1;

else ceny=2*(sto_state(no,3:(S+2)));

nbd=unique([0,nbd_cells7(no)]);

len=length(nbd);

for k=1:(len-1),

y(k,:)=sto_state(nbd(1,(k+1)),3:(S+2));

end;

Y=(sum(y))+ceny;

singy=find(Y==1);

if (length(singy))~=0

for j=1:(length(singy)),

a=find(y(:,singy(1,j))==1)';

at_l=nbd(1,(a(1,1)+1));

agre_cost=call_cost7(at_l,S,sto_state);

if (min(agre_cost))~=inf

chsrelock=find(agre_cost==min(agre_cost));

chrelock=chsrelock(1,1);

sto_state(no,(singy(1,j)+2))=1;

sto_state(no,1)=sto_state(no,1)+1;

sto_state(at_l,(chrelock+2))=1;

sto_state(at_l,(singy(1,j)+2))=0;

ch_lock=singy(1,j);

norelock=at_l;

break;

elseif (j==length(singy))


end;

end;

else


end;

end;

X{1,1}=sto_state;

X{1,2}=ch_lock;

X{1,3}=norelock;

X{1,4}=chrelock;

2. To allocate/block a call based on the cost for that particular call in that particular cell returns cell

array with column 1 as sto_state & column 2 as ch_lock

function[A]=allo_dcam7(no,S,sto_state,fd);

X=call_cost17(no,S,sto_state,fd);

x=min(X);

ch_lock=0;

if x==inf


else


for i=1:S,

if X(1,i)==x

ch_lock=i;

sto_state(no,(ch_lock+2))=1;

break;

end;

end;

end;

A{1,1}=sto_state;

A{1,2}=ch_lock;

3. Routine to Identify a Cell in which call is under progress There is always a requirement

to find where is a given call in progress. Given any coordinate of the mobile handset, it will be able to

find the number of cell in which call is under progress.

function[no]=call_cell(x,y);

x_figure=x/((sqrt(3))*2/2);

a1=(ceil(x_figure))*(sqrt(3))*2/2;

a2=(floor(x_figure))*(sqrt(3))*2/2;

y_figure=y/(3*2/2);

b1=(ceil(y_figure))*3*2/2;

b2=(floor(y_figure))*3*2/2;

e1=coord_fn(a1,b1);

e2=coord_fn(a1,b2);

e3=coord_fn(a2,b1);

e4=coord_fn(a2,b2);

e=[e1,e2,e3,e4];

for j=1:4,

if isnan(e(j))~=1

e_c=no_coord(e(j));

e_x=e_c(1,1);

e_y=e_c(1,2);

d(j)=sqrt((e_x-x)^2+(e_y-y)^2);

else d(j)=inf;

end;

end;

for j=1:4,

if (min(d))>2

no=NaN;

elseif (min(d)<=2)&(d(j)==min(d))

no=e(j);

end;

end;

4. Co-channel Set When ever any cell is identified where call is under progress on any particular

channel, all the co-channel cells of the referred channel cane be located.

function[X]=coch_set(no);

%given a cell (no,s,N,M),to find the set of

%immediate cochannel cells

j=2;

c=no_coord(no);

a=c(1,1);

b=c(1,2);

d=(sqrt(7))*(sqrt(3))*2;

theta=(asin(sqrt(((j^2)*3/4)/(7))))*180/pi;

for q=1:6

alpha=theta+((q-1)*60);

ang=(alpha)*pi/180;

a1=a+d*cos(ang);

b1=b+d*sin(ang);

X(q)=coord_fn(a1,b1);

end;

X=[X(1),X(2),X(3),X(4),X(5),X(6)];

5. Identify central Cell Whenever approximate center coordinates are given, this will

return the cell number if it belongs to a cell.

function[c]=coord_fn(a,b);

x_element=round(a/((sqrt(3))*2/2));

y_element=round(b/(3*2/2));

if (((x_element-y_element)/2)<(7-0.5))&...

(((x_element-y_element)/2)>=0)&(y_element>=0)...

&(y_element<=(7-1))

c=floor((1+((x_element-y_element)/2)+...

7*(y_element)));

else c=NaN;

end;

6. Cost of Re-allocation After allocation of channel call is awaited completion. Once

completed, cost of re-allocation of channel is estimated. Minimum cost channel is released. Cost_re17

calculates cost as per the cost formula and cost_re27 calculates the cost as per the analytical model.

6.1 function[X]=cost_re17(no,S,sto_state,fd);

x=sto_state(no,3:(S+2));

tot_ch=sum(x);

eng_ch=find(x);


len=length(nbd);

for i=1:S,

for k=1:(len-1),

if ismember(i,eng_ch)

if ismember(i,fd{1,nbd(1,(k+1))})

q(k,i)=0;

else q(k,i)=1;

end;

if ismember(i,fd{1,no})==1

qx(1,i)=0;

else qx(1,i)=1;

end;

y(k,:)=avail_ch17(nbd(1,(k+1))...

,S,sto_state);

if y(k,i)==1

b(k,i)=0;

else b(k,i)=1;

end;

r(k,i)=b(k,i)+(2*q(k,i));

else qx(1,i)=-(inf);

r(k,i)=inf;

end;

end;

end;

R=sum(r);

for i=1:S,

X(1,i)=(1-qx(1,i))+R(1,i);

end;

6.2 Reallocation Cost Given the ch state,this function returns the cost of re_allocation based

on analytical model.Input as (no,S,sto_state)

function[X]=cost_re27(no,S,sto_state);

x=sto_state(no,3:(S+2));

eng_ch=find(x);

sto_state1=sto_state;

sto_state1(no,3:(S+2))=zeros(1,S);


len=length(nbd);

for j=1:(len-1),

y(j,:)=avail_ch7(nbd(1,(j+1)),S,sto_state1);

end;

Y=sum(y);

for i=1:S,

if ismember(i,eng_ch)==1

X(1,i)=Y(1,i);

else

X(1,i)=inf;

end;

end;

7. Aggressive DCA Algorithm Given average inter arrival time & average holding time, it will

estimate the probability of blocking in aggressive DCA using analytical model. Input (S,L,T,Y).

function[prob]=dca_agra7n(S,L,T,Y);

sto_state=zeros(49,(S+2));

seq=1:1:Y;

loc_com=(1+(floor([48*ones(1,Y)].*[rand(1,Y)])));

ex_durn=exprnd(T,1,Y);

ex_iarr=exprnd(L,1,Y);

ex_arri=zeros(1,Y);

for i=1:(Y-1),

ex_arri=(ex_arri)+((ex_iarr(1,i))*[zeros(1,i),ones(1,(Y-i))]);

end;

ex_inibal=(ex_iarr)-(ex_iarr(1,1))*(ones(1,Y));

ex_arr=[((ex_inibal)+(ex_arri));ones(1,Y);seq;...

loc_com;zeros(1,Y)];

ex_fini=[((ex_durn)+(ex_arr(1,:)));zeros(1,Y);seq;...

loc_com;zeros(1,Y)];

ex_comp=[ex_arr(:,1),ex_fini,ex_arr(:,(2:Y))];

tot_seq=sort([ex_arr(1,:),ex_fini(1,:)]);

temp_comp=zeros(5,1);

for i=1:(2*Y),

j=i:1:(2*Y);

a=find(ex_comp(1,j)==tot_seq(1,i));

k=a(1,1);

temp_comp=ex_comp(:,i);

ex_comp(:,i)=ex_comp(:,k+i-1);

ex_comp(:,k+i-1)=temp_comp;

end;

pre_arr=[0,ex_comp(1,1:(2*Y-1))];

ex_comp(1,:)=ex_comp(1,:)-pre_arr;

for i=1:2*Y,

tim_elap(ex_comp(1,i));

if (ex_comp(2,i)==1)

A=allo_agra7(ex_comp(4,i),S,sto_state);

sto_state=A{1,1};

if (A{1,3}~=0)

for r=1:i-1,

if ((ex_comp(4,r)==A{1,3})&...

(ex_comp(5,r)==A{1,2}))

ex_comp(5,r)=A{1,4};

break;

end;

end;

end;

ex_comp(5,i)=A{1,2};

else b=find(ex_comp(3,1:(i-1))==ex_comp(3,i));

if (ex_comp(5,b(1,1))~=0)

E=deallo_dca1a7n(ex_comp(4,i),S,...

sto_state,ex_comp,(i-1));

sto_state=E{1,1};

chpos=E{1,2};

ex_comp(5,chpos)=ex_comp(5,b(1,1));

ex_comp(5,b(1,1))=0;

% sto_state(ex_comp(4,b(1,1)),ex_comp(5,b(1,1))...

% +2)=0;

end;

end;

end;

bc=sum((sto_state(:,2))')

ac=sum((sto_state(:,1))')

prob=bc/(ac+bc);

8. DCA (P) Algorithm when handoff considered To estimate the probability of blocking in

DCA(P)with handoff. Speed of the mobile also added in the routine.

function[P]=dca_hand7(S,L,T,Y,V);

dd=0;

sto_state=zeros(49,(S+2));

A=pre_stofnn1(L,T,Y,V);

ex_comp=A{1,1};

len=A{1,2};

for i=1:len,




A=allo_dca7(ex_comp(4,i),S,sto_state);

sto_state=A{1,1};


else B=allo_dca7(ex_comp(4,i),S,sto_state);

sto_state=B{1,1};

ex_comp(5,i)=B{1,2};

if (B{1,2}==0)

dd=dd+1;

end;

end;


if (ex_comp(5,b(1,1))~=0)



sto_state=E{1,1};

chpos=E{1,2};


ex_comp(5,b(1,1))=0;

end;

end;

end;

dc=dd;

nc=sum((sto_state(:,2))');

bc=nc-dc;

hc=(len/2)-Y;

ac=sum((sto_state(:,1))');

pb=bc/(Y);

pd=dc/(hc);

P=[pb,pd];

9. DCA (A) Algorithm when handoff considered Given average inter arrival time & average

holding time to estimate the probability of blocking in aggressive DCA using mathematical model.

function[P]=dca_handcen(S,L,T,Y,V,H);

sto_stateh=zeros (49, (H+2));

sto_state=zeros (49, (S+2));

A=pre_stofnn1 (L,T,Y,V);

ex_comp=A{1,1};

len=A {1,2};

for i=1:len,

tim_elap(ex_comp (1,i));



A=allo_dca 7(ex_comp(4, i),S,sto_state);

sto_state=A {1,1};


else sh=allo_dca7(ex_comp(4,i),H,sto_stateh);

sto_stateh=sh{1,1};

ex_comp(5,i)=sh{1,2};

end;


if (ex_comp(5,b(1,1))~=0)




sto_state=E{1,1};

chpos=E{1,2};


else

E=deallo_dca1a7n(ex_comp(4,i),H,...

sto_stateh,ex_comp,(i-1));

sto_stateh=E{1,1};

chpos=E{1,2};


end;

end;

end;

end;

bc=sum((sto_state(:,2))');

ac=sum((sto_state(:,1))');

pb=bc/(ac+bc);

hc=sum((sto_stateh(:,1))');

dc=sum((sto_stateh(:,2))');

pd=dc/(ac);

P=[pb,pd];

10. DCA (A) Algorithm with mathematical model Given average inter arrival time & average

holding time to estimate the probability of blocking in aggressive DCA using mathematical model.

function[P]= dca_handque (S,L,T,Y,V);

bc=0;

dc=0;

hc=0;

sto_state=zeros (49,(S+2));

A=pre_ stofnn 2(L,T,Y,V);

ex_comp= A {1,1};

len= A {1,2};

for i=1:len,




B=allo_dca7 (ex_comp(4,i),S,sto_state);

sto_state=B{1,1};

ch_all=find(ex_comp(3,:)==ex_comp(3,i));

ex_comp(5,ch_all)=(B{1,2})*...

ones(1,length(ch_all));

if (B{1,2}==0)

bc=bc+1;

end;

elseif ((ex_comp(6,i)~=0)&(ex_comp(5,i)==0))

A=allo_dcahq7(ex_comp(4,i),S,sto_state,...

ex_comp(7,i),dc,hc);

sto_state=A{1,1};

dc=A{1,3};

hc=A{1,4};


ex_comp(5,ch_all)=(A{1,2})*ones(1,length(ch_all));

end;

elseif (ex_comp(5,i)~=0)

E=deallo_dcahq1a7n(ex_comp(4,i),S,...

sto_state,ex_comp);

sto_state=E{1,1};

chpos=E{1,2};

ex_comp(5,chpos)=(ex_comp(5,i))*...

ones(1,length(chpos));


ex_comp(5,ch_all)=zeros(1,length(ch_all));

end;

end;

hhh=hc

bbb=bc

ddd=dc

nc=sum((sto_state(:,2))')

ac=sum((sto_state(:,1))')

pb=bc/(ac+nc);

pd=dc/(Y);

P=[pb,pd];

02-Matlab Subroutines Used in the Simulation

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