RF Toolbox 2.6
Designing Broadband Matching Networks (Part 1: Antenna)
In any system that uses RF circuits, a matching network is necessary to transfer the maximum amount of power between a source and a load. In most systems, such as wireless devices, there is a bandwidth of operation specified. As a result the purpose of the matching network is to provide maximum power transfer over a range of frequencies. While the L section matching approach (conjugate match), guarantees maximum power transfer, it does so only at a single frequency. This demo uses an approach based on optimization with direct search methods to design a broadband matching network between a resistive source and inductive load. Such a situation might occur while matching an antenna's input impedance to a source over a specific bandwidth.
Figure 1: Impedance matching of an antenna to a source
Contents
- Specify Frequency and Impedance
- Understand Load Behavior using Reflection Coefficient and Power Gain
- Design the Matching Network
- Optimize the Designed Matching Network
- Update the Matching Network Elements with Optimal Values
- Analyze and Display Optimization Results
- Display Optimized Element Values
- References
Specify Frequency and Impedance
You are building a matching network with a bandpass response, so specify the center frequency and the bandwidth of match.
fc = 350e6; % Center Frequency of matching network (Hz) BW = 110e6; % Bandwidth of matching network (Hz)
Here you specify the source impedance, the reference impedance and the load impedance. In this demo the load Zl is modeled as a series R-L circuit. You could instead measure the impedance of the load and use that directly.
Zs = 50; % Source impedance (ohm) Z0 = 50; % Reference impedance (ohm) Rl = 40; % Load resistance (ohm) L = 12e-8; % Load inductance (Henry)
Define the number of frequency points to use for analysis and set up the frequency vector.
Npts = 256; % No. of analysis frequency points fLower = fc - (BW/2); % Lower band edge fUpper = fc + (BW/2); % Upper band edge freq = linspace(fLower,fUpper,Npts); % Frequency array for analysis w = 2*pi*freq; % Frequency (radians/sec)
Understand Load Behavior using Reflection Coefficient and Power Gain
You then use two simple expressions for calculating the load reflection coefficient and the power gain. This corresponds to directly connecting the source to the antenna input terminals i.e. in Figure 1 there is no matching network.
Xl = w*L; % Reactance (ohm) Zl = Rl + 1i*Xl; % Load impedance (ohm) GammaL = (Zl - Z0)./(Zl + Z0); % Load reflection coefficient Gt = 10*log10(1 - abs(GammaL).^2); % Power delivered to load
Use the SMITHCHART to create a Smith chart that shows plot the variation in the load reflection coefficient with frequency. Input reflection coefficient closer to center of the Smith chart means a better matching performance. This plot shows that the load reflection coefficient is far away from this point and so, there is an impedance mismatch.
fig = figure; l = smithchart(GammaL); set(l,'LineWidth',1.0,'Color','r'); legend('\Gamma_L');
You can confirm this mismatch by plotting the transducer gain as a function of frequency.
plot(freq.*1e-6,Gt,'r'); grid on; title('Power delivered to load - No matching network') xlabel('Frequency (MHz)') ylabel('Magnitude (decibels)') legend('G_t','Location','Best')
As the plot shows, there is approximately 10 dB power loss around the desired region of operation (295 - 405 MHz). As a result, the antenna needs a matching network that operates over a 110 MHz bandwidth that is centered at 350 MHz.
Design the Matching Network
The matching network must operate between 295 MHz and 405 MHz, so you choose a bandpass topology for the matching network which is shown here.
Type - I: Series LC first element followed by shunt LC
Figure 2: Matching network topology
The approach you take here is to design an odd order, 0.5 dB Chebyshev low pass (LP) prototype and then apply a lowpass to bandpass transformation to obtain the initial design for the matching network [1]. Figure 2 shows the resulting matching network topology. You now need to enter the order desired and the associated coefficients. This is a single match problem [3], i.e. the source is purely resistive while load is a combination of R and L, so you can begin by choosing a five element prototype network.
N = 5; % Order of matching network LCprototype = [1.7058 1.2296 2.5408 1.2296 1.7058]; % LP prototype element values(Normalized) wU = 2*pi*fUpper; % Upper band edge wL = 2*pi*fLower; % Lower band edge w0 = sqrt(wL*wU); % Geometric mean
Use the constructor RFCKT.LCBANDPASSTEE to build the matching network. The impedance and frequency transformations are included in this for denormalization purposes. Please note that the topology demands an LP prototype that begins with a series inductor. If the topology chosen is an LC bandpass pi then you would begin with shunt C for the LP prototype.
Lvalues = zeros(N,1); % Preallaocate to store inductors Cvalues = zeros(N,1); % Preallocate to store capacitors Lvalues(1:2:end) = LCprototype(1:2:end).*Zs./(wU-wL); % Series L 's (H) Cvalues(1:2:end) = (wU-wL)./(Zs.*(w0^2).*LCprototype(1:2:end)); % Series C 's (F) Lvalues(2:2:end) = ((wU-wL)*Zs)./((w0^2).*LCprototype(2:2:end)); % Shunt L' s Cvalues(2:2:end) = LCprototype(2:2:end)./((wU-wL).*Zs); % Shunt C' s MatchingNW = rfckt.lcbandpasstee('C',Cvalues,'L',Lvalues); % Create t he matching network L_Initial = Lvalues; % Copy initial values for comparison C_Initial = Cvalues;
Optimize the Designed Matching Network
There are several points to consider prior to the optimization
- Objective function - The objective function can be built in different ways depending on the problem at hand. For this demo, the objective function is shown in the file below.
- Choice of cost function - The cost function is the function we would like to minimize (maximize) to achieve near optimal performance. There could be several ways to choose the cost function. One obvious choice is the input reflection coefficient, gammaIn. In this demo we have chosen to minimize the average reflection coefficient in the passband.
- Optimization variables - In this case it is a vector of values, for the specific elements to optimize in the matching network.
- Optimization method - A direct search based technique, the MATLAB® function FMINSEARCH, is used in this demo to perform the optimization.
- Number of iterations/function evaluations - Set the maximum no. of iterations and function evaluations to perform, so as to tradeoff between speed and quality of match.
The objective function used during the optimization process by FMINSEARCH is shown here.
type('broadband_match_antenna_objective_function.m');
function output = broadband_match_antenna_objective_function(MatchingNW,Lval
ues,freq,Zl,Zs,Z0)
%BROADBAND_MATCH_ANTENNA_OBJECTIVE_FUNCTION Is the objective function.
% OUTPUT = BROADBAND_MATCH_ANTENNA_OBJECTIVE_FUNCTION(MATCHINGNW,LVALUES,FR
EQ,ZL,ZS,Z0)
% returns the current value of the objective function stored in OUTPUT
% evaluated after updating the inductor values in the object, MATCHINGNW.
% The inductor values are stored in the variable LVALUES.
%
% BROADBAND_MATCH_ANTENNA_OBJECTIVE_FUNCTION is an objective function of RF
Toolbox demo:
% Designing Broadband Matching Networks (Part I: Antenna)
% Copyright 2008 The MathWorks, Inc.
% $Revision: 1.1.6.1 $ $Date: 2008/09/13 07:10:27 $
% Ensure positive element values
if any(Lvalues<=0)
output = inf;
return;
end
% Update the element values in the matching network
MatchingNW.L(1) = Lvalues(1);
MatchingNW.L(end) = Lvalues(end);
% Perform analysis on tuned matching network
Npts = length(freq);
analyze(MatchingNW,freq,Zl,Zs,Z0);
% Calculate input reflection coefficient 'gammaIn'
[GammaGt] = calculate(MatchingNW,'gammain','Gt','none');
gammaIn = zeros(Npts,1);
gammaIn(1:Npts,1) = GammaGt{1}(1:Npts,1);
% Cost function
output = mean(abs(gammaIn));
% Other possible choices for objective function could be : -
% output = max(abs(gammaIn));
% output = -(mean(Gt_pass));
% Animate
l = smith(MatchingNW,'gammaIn');
set(l,'DisplayName','Optimizing \Gamma_i_n');
drawnow;
There are several ways to choose the cost function and some options are shown within the objective function above (in comments). The optimization variables are the first and last inductors, L1 and L5 respectively. The element values are stored in the variable L_Optimized.
nIter = 125; % Max No of Iterations options = optimset('Display','iter','MaxIter',nIter); % Set options str ucture L_Optimized = [Lvalues(1) Lvalues(end)]; L_Optimized = fminsearch(@(L_Optimized) broadband_match_antenna_objective_fu nction(MatchingNW,... L_Optimized,freq,Zl,Zs,Z0),L_Optimized,options);
Iteration Func-count min f(x) Procedure
0 1 0.933981
1 3 0.933981 initial simplex
2 5 0.920321 expand
3 7 0.911351 expand
4 9 0.853251 expand
5 11 0.730432 expand
6 13 0.526433 reflect
7 15 0.526433 contract inside
8 17 0.421086 reflect
9 19 0.421086 contract inside
10 20 0.421086 reflect
11 22 0.421086 contract inside
12 24 0.421086 contract inside
13 26 0.339941 expand
14 27 0.339941 reflect
15 29 0.285288 reflect
16 31 0.285288 contract inside
17 32 0.285288 reflect
18 34 0.283533 reflect
19 36 0.283533 contract inside
20 38 0.278945 contract inside
21 40 0.278134 reflect
22 41 0.278134 reflect
23 43 0.276368 contract inside
24 45 0.275793 contract inside
25 47 0.275646 contract inside
26 49 0.275509 reflect
27 51 0.275292 contract inside
28 52 0.275292 reflect
29 54 0.275292 contract inside
30 56 0.275292 contract inside
Optimization terminated:
the current x satisfies the termination criteria using OPTIONS.TolX of 1.00
0000e-004
and F(X) satisfies the convergence criteria using OPTIONS.TolFun of 1.00000
0e-004
Update the Matching Network Elements with Optimal Values
When the optimization routine stops, the optimized element values are stored in L_Optimized. The following code updates the input and output matching network with these values.
MatchingNW.L(1) = L_Optimized(1); % Update the matching network inducto r L1 MatchingNW.L(end)= L_Optimized(end); % Update the matching network inducto r L5
Analyze and Display Optimization Results
Use the ANALYZE method to perform frequency domain analysis on the circuit under two scenarios:
- With the optimized matching network
- Without a matching network
analyze(MatchingNW,freq,Zl,Zs,Z0); hold all; hline = smithchart(GammaL); set(hline,'Color','r'); [leg,h_line]= legend('\Gamma_i_n','\Gamma_L'); h1 = findall(h_line,'Tag','\Gamma_i_n'); set(h1,'Color','b','LineStyle','-'); h1 = findall(h_line,'Tag','\Gamma_L'); set(h1,'Color','r'); hold off;
The optimized matching network improves the performance of the circuit. In the passband (295 MHz to 405 MHz), the input reflection coefficient is closer to the center of the Smith chart.
Plot the power delivered to load, with the matching network, using the PLOT, method of RFCKT object.
plot(MatchingNW,'Gt'); hold all; plot(freq*1e-6,Gt,'r'); grid on; title('Power delivered to load') legend('Optimized network','No matching network','Location','Best');
The power delivered to the load is approximately 1 dB down for the optimized matching network.
Display Optimized Element Values
The following code shows the initial and optimized values for inductors L1 and L5.
L1_Initial = L_Initial(1) L1_Optimized = L_Optimized(1)
L1_Initial = 1.2340e-007 L1_Optimized = 1.2111e-007
L5_Initial = L_Initial(end) L5_Optimized = L_Optimized(end)
L5_Initial = 1.2340e-007 L5_Optimized = 1.7557e-009
There are a few things to consider when setting up an optimization:
- Choosing a different objective function would change the result.
- You can use advanced direct search optimization functions such as PATTERNSEARCH AND SIMULANNEALBND in your optimization, but you must have the Genetic Algorithm and Direct Search Toolbox™ installed to access them.
A Low noise amplifier design example is covered in the second demo Designing Broadband Matching Networks (Part 2: Amplifier).
close(fig);
References
[1] RF Circuit Design, Theory and Applications, Reinhold Ludwig and P. Bretchko, pp 229-239,Prentice Hall, 2000.
[2] Microwave Engineering, David M. Pozar, 2nd ed., John Wiley and Sons, 1999.
[3] Broadband Direct-Coupled and Matching RF networks, Thomas R. Cuthbert, pp 31-33, TRCPEP, 1999.
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