ebdss max research report - final

1. 1 IMPLEMENTATION OF A WIDEBAND SPECTRUM SENSING ALGORITHM USING A SOFTWARE-DEFINED RADIO (SDR) Max Robertson and Mario Bkassiny ABSTRACT This report covers the steps of algorithm creation for wideband spectrum sensing, using raw data taken from a local software-defined radio. This includes the fundamental idea of Energy Detection Based Spectrum Sensing and a brief example illustrating this method through simulations obtained via MATLAB. It will include MATLAB code and functions that are proven to be accurate in reaching this goal. The goal of this research report is to create an optimized algorithm that successfully senses the local frequency spectrum, and determines the usage by detecting the un-used portions of the spectrum and relaying this information back to the user. The aim of this technique is to assess these un-used frequency bands to help make a more efficient communication system. The simulation of the algorithm was tested first with known variables, and then tested in the field via a chosen Universal Software Radio Peripheral (USRP) model: National Instruments (NI) NI USRP 2920, 50 MHz 2200 MHz. The Introduction will be covered in section I, the Simulations of Known Signals are covered in section II, Modulation Consideration is covered in section III, Threshold Calculation and Implementation is covered in section IV, Center Frequency Detection Algorithm is covered in section V, USRP Collected Data Analysis is covered in section VI, Selected USRP Results are covered in section VII, the Conclusion is covered in section VIII, and Future Work is covered in section IX. Universal Software Radio Peripheral (USRP) model: National Instruments (NI) NI USRP 2920, 50 MHz 2200 MHz.

2. 2 I. INTRODUCTION The idea of Cognitive Radios (CR) is relatively new, much like the age of multimedia communication, and the manipulation of these CRs could potentially change the way we communicate forever. In this report, Energy Detection Based Spectrum Sensing (EDBSS) is the main focus, and as shown in other studies it proves to be the simplest form of investigation into this problem. Many other approaches have been proposed, including Waveform-Based Sensing (WBS), Cyclostaionary-Based Sensing (CBS), Radio Identification Based Sensing (RIBS),Matched Filtering (MF), and many more; not to mention the cooperative approach[1], which simply combines some of the previously stated methods. The EDBSS approach is the least complex and thus is the least accurate of the methods stated, but in understanding at this level it can be transferred to the higher levels of complexity. Once the concept of signal generation has been grasped, digitization and modulation are a key aspect of communication through CRs. The MATLAB code used in this report suffices for accurate replication of this experiment. The parameters of this approach will be stated and justification as to their particular chosen values will be thorough as well. The results are subject to change if replicated as spectrum usage changes all the time, the time of day is as variant as the time of year, location too is a large aspect. There are many angles to cover, and prior knowledge of signal detection would be of great advantage too. The techniques and code produced in this report is of a university undergraduate level, and therefore has a very basic format. The first few sections cover an introduction to signal generation and understanding the fundamentals behind what we are trying to achieve; moving on to more technical code but again explained enough to replicate with ease. The end goal of this challenge is to see if this idea of spectrum sensing using an autonomous algorithm will work, first with known data and variables, and then again in the realworld where the signals are unknown; at least, unknown for now. The first step of this experiment is creating a base line algorithm, and being able to familiarize oneself with MATLAB and basic signal generation. II. DETECTION OF SINUSOIDAL SIGNALS USING ENERGY DETECTORS In this section the report will describe the approach, computation methods and plots obtained from this EDBSS simulation using MATLAB software. Note that,in this section, the signal generation has certain parameters; without loss of generality let us assume that the signal is sinusoidal and has a power of 1W. Let us also assume that the carrier frequency is 10MHz. Taking into consideration the Nyquist property that the sampling rate must be at least twice that of the carrier frequency. To fulfil this requirement set the sampling rate of the signal to be 100MHz, which is ten times as large as the carrier frequency. In doing so, the plots produced will be more accurate and have smoother curves, if any. We also assume that the sensing duration is very small, such that one period ( sT ) is set to 1s. Remember that for this simulation we are making the assumption that the signal is sinusoidal and has finite power. Thus we have: )cos()( 0tAtx , where 20 t tf0

3. 3 Note that )(tx is considered as a finite-power signal whose power xP can be expressed in function of the amplitude A. A very simple proof of this relationship is shown below: PROOF: dttx T P To To x 2 2 2 0 )(* 1 dttA T To To 2 2 0 22 0 )(cos* 1 dtt T A To To 2 2 0 0 2 )]2cos( 2 1 2 1[* )]2sin( 2 1 [* 2 0 00 2 2 2 tt T A To To )sin( 2 1 2 )sin( 2 1 22 00 0 0 00 0 0 0 2 T T T T T A 2 0 2 2 0 0 2 A T T A So now we can compute the desired value of A when we have a signal power of 1W. Using the formula above A= 2 . MATLAB: power=1; %signal power (Watts) fc=10*10^6; %frequency (MHz) fs=10*fc; %sampling rate > 2*fc (MHz) T=0:1/fs:1*10^-6; %sensing (micro-sec) A=sqrt(2); %would solve for A from Power=1W x=A*sin(2*pi*fc*T); %sinusoidal setup

4. 4 II. A. Spectrum Estimation in the Absence of Noise The setup is relatively simple, and the parameters can change depending on the user preference or environment specifications. Now we have a signal with the correct parameters,we need to compute the Fast-Fourier Transform (FFT) of the function, which ultimately converts the signal from time-domain to frequency-domain. But the axis in frequency domain are different and require some adjusting when plotting the signals. We calculate and plot the signal itself in time-domain, then its magnitude spectrum, then we finish by plotting the associating phase angle spectrum. This is standard procedure for signal analysis. Note: we must normalize the axis for the FFT plots. MATLAB: N=length(x); n=sqrt(0.1)*randn(1,N); f0=[-N/2:N/2-1]; f1=f0/N; %This normalizes the frequency from -1/2 to 1/2-1/N f=f1*fs; %fs is the sampling rate. f is now a vector from -fs/2 to fs/2-fs/N~fs/2 Now we can compute the Fast Fourier Transform of the function x: MATLAB: X=fftshift(fft(x)); %fast-fourier transform X0=abs(X);%Magnitude X1=angle(X); %angle

5. 5 )(tx : %subplot(311) %stem(T,x); plot(T,x,'b'); xlabel('t(sec.)'); ylabel('x(t)'); title('The Signal x(t) in the Absence of Noise'); grid; figure 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 x 10 -6 -1.5 -1 -0.5 0 0.5 1 1.5 t(sec.) x(t) The Signal x(t) in the Absence of Noise

6. 6 Magnitude Spectrum of )(tx : %subplot(312) %stem(f,X0); plot(f, X0,'r'); ylabel('|X(f)|'); xlabel('f(Hz)'); title('Magnitude Spectrum |X(f)| in the Absence of Noise'); grid; figure -5 -4 -3 -2 -1 0 1 2 3 4 5 x 10 7 0 10 20 30 40 50 60 70 |X(f)| f(Hz) Magnitude Spectrum |X(f)| in the Absence of Noise

7. 7 Phase Spectrum of )(tx : %subplot(313) %stem(f,X1); plot(f,X1,'g'); ylabel('= eta4_1)=[1]; %active fc1 e1 t_n1_2(t_n1_2 ~= 1)=[0]; %noise fc1 e1 t_n2_2(t_n2_2 >= eta4_1)=[1]; %active fc2 e1 t_n2_2(t_n2_2 ~= 1)=[0]; %noise fc2 e1 %alpha=0.1 F_c=20KHz SWL=1 subplots1 plot(q,t_n1_2,'c'); ylabel('Active Frequency'); xlabel('f(Hz)'); title('alpha=0.1 - t(n) - (F_c=20KHz) - (BSPK signal) - SNR=6.9897 - 10,000 samples - SWL=1 '); grid; figure plot(q,t_n1_3,'c'); ylabel('Active Frequency'); xlabel('f(Hz)'); title('alpha=0.01 - t(n) - (F_c=20KHz) - (BSPK signal) - SNR=6.9897 - 10,000 samples - SWL=1 '); grid; figure plot(q,t_n1_4,'c'); ylabel('Active Frequency'); xlabel('f(Hz)'); title('alpha=0.001 - t(n) - (F_c=20KHz) - (BSPK signal) - SNR=6.9897 - 10,000 samples - SWL=1 '); grid; figure

51. 51 %alpha=0.1 F_c=25KHz SWL=1 subplots1 plot(q,t_n2_2,'r'); ylabel('Active Frequency'); xlabel('f(Hz)'); title('alpha=0.1 - t(n) - (F_c=25KHz) - (BSPK signal) - SNR=6.9897 - 10,000 samples - SWL=1 '); grid; figure plot(q,t_n2_3,'r'); ylabel('Active Frequency'); xlabel('f(Hz)'); title('alpha=0.01 - t(n) - (F_c=25KHz) - (BSPK signal) - SNR=6.9897 - 10,000 samples - SWL=1 '); grid; figure plot(q,t_n2_4,'r'); ylabel('Active Frequency'); xlabel('f(Hz)'); title('alpha=0.001 - t(n) - (F_c=25KHz) - (BSPK signal) - SNR=6.9897 - 10,000 samples - SWL=1 '); grid; figure

52. 52 -5 -4 -3 -2 -1 0 1 2 3 4 5 x 10 4 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 ActiveFrequency f(Hz) alpha=0.1 - t(n) - (Fc =20KHz) - (BSPK signal) - SNR=6.9897 - 10,000 samples - SWL=1 -5 -4 -3 -2 -1 0 1 2 3 4 5 x 10 4 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 ActiveFrequency f(Hz) alpha=0.1 - t(n) - (Fc =25KHz) - (BSPK signal) - SNR=6.9897 - 10,000 samples - SWL=1

55. 55 As you can see,there is a correlation between the Magnitude spectrum plots and the previous plots; they show the existence/detection of a signal. But as you also see there isnt just one straight line at the carrier frequency, there are many lines concentrated, this emphasizes the Sinc-shaped nature of this signal. The previous plots clearly show a decrease in width as the alpha threshold value decreases, let us see what happens if you increase the SWL value. The following setup will proceed as only the carrier frequency 20KHz because the graphs are essentially the same but shifted for 25KHz. Note that the first three plots will be for alpha=0.1, the second: 0.01 and the third: 0.001. And the plots are ordered, from top to bottom, SWL=1, 11, 21. The MATLAB code is exactly the same,it is a case of copy/paste and simple subscript change as to not confuse variables. The only thing to note is that for each SWL there is a function created after the for loop, this is what would be multiplied here: t_n1_2=(2/(P1*P_new1)).*T_n1; MATLAB: t_n4_1=(2/(P1*P_new1)).*T3_1; t_n4_2=(2/(P1*P_new1)).*T3_1; t_n4_3=(2/(P1*P_new1)).*T3_1; t_n4_1(t_n4_1 >= eta3_1)=[1]; %active fc1 e1 t_n4_1(t_n4_1 ~= 1)=[0]; %noise fc1 e1 t_n4_2(t_n4_2 >= eta3_2)=[1]; %active fc1 e2 t_n4_2(t_n4_2 ~= 1)=[0]; %noise fc1 e2 t_n4_3(t_n4_3 >= eta3_3)=[1]; %active fc1 e3 t_n4_3(t_n4_3 ~= 1)=[0]; %noise fc1 e3 plot(q,t_n4_1,'b'); ylabel('Active Frequency'); xlabel('f(Hz)'); title('alpha=0.1 - t(n) - (F_c=20KHz) - (BSPK signal) - SNR=6.9897 - 10,000 samples - SWL=21 '); grid; figure plot(q,t_n4_2,'b'); ylabel('Active Frequency'); xlabel('f(Hz)'); title('alpha=0.01 - t(n) - (F_c=20KHz) - (BSPK signal) - SNR=6.9897 - 10,000 samples - SWL=21 '); grid; figure plot(q,t_n4_3,'b'); ylabel('Active Frequency'); xlabel('f(Hz)'); title('alpha=0.001 - t(n) - (F_c=20KHz) - (BSPK signal) - SNR=6.9897 - 10,000 samples - SWL=21 '); grid;

56. 56 -5 -4 -3 -2 -1 0 1 2 3 4 5 x 10 4 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 ActiveFrequency f(Hz) alpha=0.1 - t(n) - (Fc =20KHz) - (BSPK signal) - SNR=6.9897 - 10,000 samples - SWL=1 -5 -4 -3 -2 -1 0 1 2 3 4 5 x 10 4 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 ActiveFrequency f(Hz) alpha=0.1 - t(n) - (Fc =20KHz) - (BSPK signal) - SNR=6.9897 - 10,000 samples - SWL=11 -5 -4 -3 -2 -1 0 1 2 3 4 5 x 10 4 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 ActiveFrequency f(Hz) alpha=0.1 - t(n) - (Fc =20KHz) - (BSPK signal) - SNR=6.9897 - 10,000 samples - SWL=21

59. 59 V. CENTER FREQUENCY DETECTION ALGORITHM The concept behind this section is to have known inputs, essentially a known input signal, where we have some knowledge of what the output of this experimental algorithm will be. The reason behind this is to test the existing algorithm before using raw collected data for real world analysis via the USRP (Universal Software Radio Peripheral). The conclusion of this section is to be able to receive a signal, process it using the algorithm, then output the center frequencies detected and their corresponding bandwidths. To test the ability to detect more than one active frequency within one window scan,we need to create a new input signal. For the sake of time, let us simply combine the 20KHz signal and the 25KHz signal. This must be done in the y(t) format, and then all the FFT work. Once completed, we need the shifted periodogram, smoothed, and a frequency axis correctly scaled. MATLAB: T_prime=[t_n1_111 + t_n2_111]; %obtained by y(20KHz) + y(25KHz), FFT, shift, smoothed, etc. t_n3_111=(2/(P3*P_new3)).*T3_3; i00=[-P3/2:P3/2-1]; %new frequency axis scaled as there are twice as many points than previous i11=i00/P3; it=(i11/T_s); And then to plot this new function: semilogy(it/1000,t_n3_111,'b'); ylabel('Threshold value'); xlabel('f(KHz)'); title('Semilog plot of t(n) - F_c1=20KHz/F_c2=25KHz - (BSPK signal) - SNR=6.9897 - 10,000 samples - SWL=101'); grid; hold on xh1 = [-50 50]; yh1 = [eta3_1 eta3_1]; semilogy(xh1,yh1,'r') xh2 = [-50 50]; yh2 = [eta3_2 eta3_2]; semilogy(xh2,yh2,'m') xh3 = [-50 50]; yh3 = [eta3_3 eta3_3]; semilogy(xh3,yh3,'c') hold off figure

60. 60 It produces the two peaks at the frequencies, and notice they do not interfere, this is due to backtracking to the y(t) state versus adding the two received signals, notice that What we need to do now is take the data that is above the threshold lines and extract its peak location and how wide the peak is, Center Frequency and Bandwidth respectively. MATLAB: H=t_n_new; %this is the t(n) used previous with the combined signals G=eta3; %the desired threshold we are using K=H-G; %graphically this should shift it down to the x-axis (zero) -50 -40 -30 -20 -10 0 10 20 30 40 50 10 1 10 2 10 3 10 4 Thresholdvalue f(KHz) Semilog plot of t(n) - Fc 1=20KHz/Fc 2=25KHz - (BSPK signal) - SNR=10 - 20,000 samples - SWL=101 -50 0 50 10 010 210 4 Thresholdvalue f(KHz) Semilog plot of t(n) - Fc 1=20KHz/Fc 2=25KHz - (BSPK signal) - St(n) data alpha=0.1 alpha=0.01 alpha=0.001

61. 61 To plot this shift by the threshold value we obtain a plot like this: i00=[-P3/2:P3/2-1]; %the frequency axis with correct length i11=i00/P3; it=(i11/T_s); plot(it/1000,K,'r'); ylabel('Threshold value'); xlabel('f(KHz)'); title('Semilog plot of t(n) - F_c1=20KHz/F_c2=25KHz - (BSPK signal) - SNR=10 - 20,000 samples - SWL=101'); grid; figure hold on xh1 = [-50 50]; yh1 = [0 0]; plot(xh1,yh1,'black') hold off figure Now we have obtained a plot that we can now manipulate to we can observe on the positive part and record the x-axis intercepts too. -50 -40 -30 -20 -10 0 10 20 30 40 50 -500 0 500 1000 1500 2000 2500 3000 3500 Thresholdvalue f(KHz) Semilog plot of t(n) - Fc 1=20KHz/Fc 2=25KHz - (BSPK signal) - SNR=10 - 20,000 samples - SWL=101

62. 62 We have to generate this new shifted t(n) data in a binary way so that we can access the relevant data from the plot, using a sign plot will make the plot shift from [-1,1] where the square wave peaks indicate where the active frequency is sensed. Once we have completed this we need to take the magnitude (absolute value) of the derivative as this will shift the graph so instead of accessing [-1,1] we can access [0,2]; where a y-value of 2 indicates an active frequency. This differentiation process also converts the square sign curve into an impulse plot, the reason for doing this makes the values easier to manipulate in the vector form and accessing them too. The code is as follows: MATLAB: W=sign(K); plot(it/1000,W,'r'); ylabel('Threshold value'); xlabel('f(KHz)'); title('Sign plot of t(n) - F_c1=20KHz/F_c2=25KHz - SNR=10 - 20,000 samples - SWL=101'); grid; figure (Square wave) -50 -40 -30 -20 -10 0 10 20 30 40 50 -1 -0.8 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 0.8 1 Thresholdvalue f(KHz) Sign plot of t(n) - Fc 1=20KHz/Fc 2=25KHz - SNR=10 - 20,000 samples - SWL=101

63. 63 Z=abs(diff(W)); %N-1 points after diff fcn applied, discrete differentiation it(10000)=[]; %remove the 10,000th point as the diff function is length N-1 plot(it/1000,Z,'b'); ylabel('Threshold value'); xlabel('f(KHz)'); title('Impulse plot of t(n) - F_c1=20KHz/F_c2=25KHz - SNR=10 - 20,000 samples - SWL=101'); grid; (Impulse plot) So, now we have a vector where, for the sake of this example, there exists 8 points where the function has a value of 2. There are many different ways to which one could have reached this part, but choosing this way seemed easier and there have been speculations about what if there was a partial signal detected, giving you say 9 points, and odd number? The solution lies in the smoothing and threshold value, it will not occur because these values limit this from occurring. If you look at the plot now, you will notice that each pair of lines is the bandwidth of the active frequency, and the active frequency is the median of each pair. This was the reason for this approach, so as to manipulate the output data and extract not positive from negative, but odd from even elements. If you create a vector for the odd ordered elements, and the even ordered elements, by doing a simple subtraction you can calculate the bandwidth and center frequency of each pair, ONLY if there is an even number of elements. The lengths of the two vectors must be equal and even. For example, take this section: If you take the second line value and minus the first line value you will obtain the width (Bandwidth) of that section, also if you take first line + second line, and divide by 2 you get the middle value (Center Frequency) too. So we need to access the odd and even elements of this vector. -50 -40 -30 -20 -10 0 10 20 30 40 50 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 Thresholdvalue f(KHz) Impulse plot of t(n) - Fc 1=20KHz/Fc 2=25KHz - SNR=10 - 20,000 samples - SWL=101

64. 64 MATLAB: XVAL = (it(Z ~= 0))./1000; %frequencies where they are detected at the threshold via intersection Even = XVAL(2:2:length(XVAL)); %the critical points AFTER the CF peaks Odd = XVAL(1:2:length(XVAL)); %the critical points BEFORE the CF peaks Bandwidth = Even - Odd; %the difference in the intersections for peaks Center_Frequency=(Even + Odd)/2; %the average of the intersections (approx.) This code allows you to access the odd and even elements and store them in two separate vectors,the arithmetic manipulations output the carrier frequencies detected and their corresponding bandwidths. For this example, (MATLAB Command Window Outputs): > Center_Frequency = -25.0900 -19.9200 19.9100 25.0800 > Bandwidth= 2.1800 2.1800 2.1800 2.1800 As you can see there is some slight error but there is always going to be error in energy detection, its an approximation, we know this because for the center frequencies they are KHz20 and KHz25 ; now compare these values to the ones above. What does this mean? We can conclude that the algorithm thus far is relatively accurate and we can now apply it to realworld data observed through the USRP. Adjustments will have to be made but the theoretical aspect of this autonomous algorithm is accurate and now ready for the next stage: USRP collected data. Also, we can create a function that takes a received signal and does these processes stated in the report thus far through the use of one function. We will use this function for the next stage as the only input that is going to change is the input signal. For the readers convenience,attached is a MATLAB function that will essentially take and received signal and calculate the active frequencies and their bandwidths automatically, the only thing the user needs to do is specify the parameters.

65. 65 MATLAB Function written by Max Robertson (06/2015,SUNYOswego) for the purpose of spectral sensing, details ofthis function can be found within the commented sections or previously in this report. NOTE: Ifyou want to produce the plots at this stage, uncomment the sections labelled with a %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% Final_Function Start %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function[Output, t_n, f_vec, eta, D] = Final_Function( r, t, L, alpha, CF ) %Inputs %r which represents the received signal in time domain %t is the time axis, typically setup as T_s:T_s:N*T_o, where T_s is the %sampling period, T_o is the bit duration, and N is the number of bits %L is the spectral window length, this is simply a positive integer %value, optimal values are >11 %alpha is the false alarm probability, the smaller the better %CF is the center frequency at which the USRP is sensing at a determined window length %Outputs %Outputs consist of Center Frequencies, then corresponding bandwidths below %t_n is the smoothed data %f_vec is the frequency axis %eta is the determined threshold value %D is the square plot of the data as determined by eta %f_vec1 is essentially f_vec with one less data point, for diff plot Ts=t(2)-t(1); %Sampling period fs=1/Ts; %Sampling frequency R=fftshift(fft(r)); %Fast Fourier Transform of the received signal Mag_R=abs(R); %Magnitude Spectrum of r M=length(r); %this is the scaling requirement for time->freq. n0=[-M/2:M/2-1]; % n1=n0/M; % f_vec=(n1*fs); % Q=(Mag_R).^2; %Typical Periodogram setup eta=2*gammaincinv((1-alpha),L,'lower'); %threshold value eta in MHz k1=-(L-1)/2; %lower sum k2=(L-1)/2; %upper sum P_t=mean(abs(r).^2); T=zeros(1,M);

66. 66 for j=1:M a=max(j+k1,1); %truncation at lower end at 1 b=min(j+k2,M); %truncation at upper end at M T(j)=sum(Q(a:b)); end t_n=(2/(M.*P_t)).*T; % plot(CF+f_vec,t_n,'b',CF+f_vec,eta,'r'); % ylabel('f(MHz) - |R(f)|.^2'); % xlabel(' f-vec'); % title('Smoothed Periodogram of r at specified L value'); % grid; % figure % semilogy(CF+f_vec,t_n,'b'); % ylabel('Threshold value'); % xlabel('f(MHz)'); % title('Semilog plot of t(n) - with desired threshold value and SWL value'); % grid; % K1=min(CF+f_vec); % K2=max(CF+f_vec); % hold on % xh1 = [K1 K2]; %determined by window size and what frequency band being observed % yh1 = [eta eta]; % semilogy(xh1,yh1,'r') % hold off % figure A=t_n; B=eta; C=A-B; %shift t_n down to x-axis creating critical points D=sign(C); %square plot E=abs(diff(D)); %impulse plot G=length(f_vec); f_vec1=f_vec; f_vec1(G)=[];

67. 67 % plot(CF+f_vec,E,'r'); % ylabel('Threshold value'); % xlabel('f(MHz)'); % title('Impulse plot of t(n) - with desired threshold value and SWL value'); % grid; XVAL = (f_vec1(E ~= 0))+CF; %frequencies where they are detected at the threshold via intersection Even = XVAL(2:2:length(XVAL)); %the critical points after the carrier frequency peaks Odd = XVAL(1:2:length(XVAL)-1); %the critical points before the carrier frequency peaks Center_Frequencies = ((Even + Odd)/2)./1000000; %the average value of the two intersections, not exact value (approximation) Bandwidth = (Even - Odd)./1000000; %simply the difference in the intersections for one peak Indices =(Bandwidth > 0.01); %creating a minimum bandwidth threshold Bandwidth = Bandwidth(indices); %removing values that dont pass threshold Center_Frequencies = Center_Frequencies(indices); Output = [Center_Frequencies Bandwidth]; %Outputs of this function set up so that CF has corresponding Bandwidth underneath end %% Final_Function End %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

68. 68 VI. USRP COLLECTED DATA ANALYSIS VI. A. Initial USRP parameters In this section, we use the previously created algorithm and test it on raw real world data, to see if we can detect real active signals in the local SUNY Oswego area; but not only detect, be able to identify what the active frequency is and its corresponding bandwidth depending on the parameters set in place. The above diagram is the original Simulink setup file, where we use the USRP denoted as Software Defined Radio (SDRu ) Receiver and tested the receiving capability with our algorithm. For this one instance, of 89MHz, we set the parameters as: On this GUI display we can see the parameters that need to be satisfied for this Simulink SDRu to work. The Center frequency is a constant input as above, and we are keeping the offset as 0 for now it may have to change later; the gain too will be a constant at 200dB. The decimation factor will change, but for this one test it will be 10, at it is essentially the window width being sensed and displayed on the Spectrum Analyzer. The sample time is the Decimation Factor divided by 100MHz, and the Frame Length or samples is 2000.

69. 69 In other words, the actualsampling rate fs of the USRP is equal to: DF MHz DF f f s s 100max, , where MHzfs 100max, is the maximum sampling rate of the USRP and DF is the decimation factor. The setup above in Simulink can be explained as follows: The constant input will be the known Center Frequency at which we are sensing, we will shift this to be able to move and sense the active frequencies throughout the spectrum The SDRu is the Simulink Software Defined Radio setup that acts as the USRP we are using, this receives the data The Terminator simply terminates this output as we are not using it The spectrum Analyzer is simply is visual display of what is being sensed at 89MHz, where there are peaks, that indicates an active frequency The frame conversion allows us to convert the data into sample points which are then stored as a vector/matrix y which we can call in MATLAB It is possible to do the algorithm in Simulink but you would need to do a lot of blocks for the threshold and all the calculations, eventually it would just get confusing, but there is a way that you can limit this to MATLAB using the comm.SDRuReceiver() command in MATLAB, we can call the SDRu block from the Simulink file and then do the manipulations. Also using this approach it allows us to be able to create some loop to iterate the input frequencies to change, thus autonomously scan the spectrum. We will now be able to use our algorithm with the received data as the input. MATLAB: rx_SDRu = comm.SDRuReceiver('192.168.10.2', ... 'CenterFrequency', fc, ... 'Gain', 200, ... 'DecimationFactor', decimation_factor, ... 'LocalOscillatorOffset', 0, ... 'SampleRate', Ts, ... 'FrameLength', N, ... 'OutputDataType', 'single') As you can see,there are many variables that have to be accounted for, this is the MATLAB equivalent of the GUI previously stated. You can see the USRP IP Address, Center Frequency, Gain, etcWe can call this variable as the input r for Final Function previously mentioned. So now a new function will be needed such that it takes the input y from the SDRu and uses the Final Function to compute the required outputs. Also note that there is a delay at the beginning of the signal detection so the first hundred or so data points are recorded as having a value of 0, this is why the for loop in the detection function accounts for this delay until non-zero data is received. NOTE:These will all change when put into practice,fine-tuning the parameters for optimal resolution.

70. 70 MATLAB Function written by Max Robertson (07/2015,SUNYOswego) for the purpose of spectral sensing via USRP, details ofthis function can be found within the commented sections or previously in this report. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% detection Start %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% function [ Output, t_n, f_vec, eta, D] = detection( fc, fs, N, L, alpha ) %Inputs %fc which represents the center frequency for the SDRu %fs which is the sampling frequency, bandwidth can be calculated from this %N is the number of samples for the SDRu %L is the spectral window length, this is simply a positive integer %value, optimal values are >11 %alpha is the false alarm probability, the smaller the better %Outputs %Outputs consist of Center Frequencies, then corresponding bandwidths below %t_n is the smoothed data %f_vec is the frequency axis %eta is the determined threshold value %D is the square plot of the data as determined by eta Ts=1/fs; %Period decimation_factor=100e6./fs; %needed for SDRu t=0:Ts:(N-1)*Ts; %calculated from Ts for Final Function input %this takes the parameters needed to obtain the USRP data rx_SDRu = comm.SDRuReceiver('192.168.10.2', ... 'CenterFrequency', fc, ... 'Gain', 200, ... 'DecimationFactor', decimation_factor, ... 'LocalOscillatorOffset', 0, ... 'SampleRate', Ts, ... 'FrameLength', N, ... 'OutputDataType', 'single') r=step(rx_SDRu); %need the step of the data

71. 71 while (norm(r) == 0) r=step(rx_SDRu); %loop for non-zero elements r=step(rx_SDRu); end % only takes 2 iterations for USRP natural delay r=r-mean(r); %removing the DC component [Output, t_n, f_vec, eta, D]=Final_Function(r, t, L, alpha, fc); %Outputs of detection are the same as Final function, detection is simply giving Final Function real world inputs end %% detection End %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% Note: This algorithm can be made completely autonomous with the addition of another for/while loop that iterates over the sub-bands and essentially restarts the process without stopping, like a continuous live stream ofthe assigned sub-bands being scanned. Ifyou were able to implement this process on many CRs then you could collaborate the data and be able to constantly scan the spectrum completely autonomously. Now we have a function that can plot the data we receive from the USRP,but now we want to be able to plot as much of the spectrum as we can autonomously! That is, create another new function that calls the previous detection and repeats it every specified bandwidth from starting frequency to ending frequency. At this point we need the function to be able to have a starting Center Frequency (CF) and an ending CF, and be able to iterate in steps of the determined bandwidth sensing window, from one to the other. We also need to plot the data from the USRP and the determined threshold value, alongside with a simple binary plot to indicate if an active frequency is detected (above the threshold marker). Once we are able to achieve this step, we need to address the false-positive issue with the USRP receiver. These occur and look much like that of active signals but have a much larger amplitude and much smaller bandwidth, almost zero. These are not signals but internal harmonics in the hardware, unfortunately its unavoidable. For now, we will continue to work with them but know that they are false-positive harmonics. Here is the sequential function followed by the plots.

72. 72 MATLAB Script written by Max Robertson (07/2015,SUNYOswego) for the purpose of iterative spectral sensing via USRP using previously created functions: Final Function and detection; details ofthis Script can be found within the commented sections or previously in this report. This is trial run example with the random parameters in bold purple. %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %% sequential Start %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% close all clear all %function [ x ] = sequential( f_start, f_end, BW, N, L, alpha ) f_start=88e6; %starting frequency f_end=91e6; %ending frequency BW=500e3; %sampling rate, window size N=2000; %sample points L=151; %Spectral Window Length alpha=0.001; %threshold value as determined by false alarm probability x=[]; count=1; for i=f_start:BW:f_end %for loop that iterates across pre-specified spectrum %calls detection and iterates through i center frequency in for loop [Outputs, t_n, f_vec, eta, D]=detection( i, BW, N, L, alpha ); % subplot(311) % plot(i+f_vec,t_n,'b',i+f_vec,eta,'r'); % ylabel('f(MHz) - |R(f)|.^2'); % xlabel(' f-vec'); % title('Smoothed Periodogram of Received Signal, SWL=151, %Threshold=0.001'); % grid; % hold on % pause(1) D_norm=(D*0.5)+0.5; % Shift the D data from [-1 1] to [0 1] Percent(count)=[(sum(D_norm)/N)*100]; %mean of new D graph count=count+1; Percentage=mean(Percent); %Overall percentage used

73. 73 subplot(211) % subplot(312) semilogy(i+f_vec,t_n,'b',i+f_vec,eta,'r'); ylabel('f(MHz) - |R(f)|.^2'); xlabel(' f-vec'); title(['Smoothed Periodogram of Received Signal, SWL=', num2str(L),... ', Threshold=',num2str(alpha),', Utilization=',... num2str(Percentage),'%']); grid; %plots the t_n data from USRP hold on pause(1) %natural delay, helps with seeing the plots before adding them subplot(212) % subplot(313) plot(i+f_vec,D_norm,'r'); ylabel('Threshold value'); xlabel('f(MHz)'); title(['Impulse plot of Scaled/Shifted Received Signal, SWL=',... num2str(L), ', Threshold=',num2str(alpha),', Utilization=', ... num2str(Percentage),'%']); ylim([-1 2]); grid; %plots the square data from USRP above threshold marker hold on pause(1) x=[x, Outputs]; %adds the AF and BW after each iteration end hold off %This will now plot where all the peaks above the threshold are and their %respective bandwidths, it is a visual representation, ^ that are closer to %the x axis can be assumed to be weak signals or that of a false alarm %detection figure plot(x(1,:),x(2,:),'^','LineWidth', 2); ylabel('Bandwidth (MHz)'); xlabel('Center Frequency (MHz)'); title('Detected Signals remaining after threshold test'); grid; % after the plots, it will display the vector showing the CF, respective BW and the utilization of that sub-band x Percentage %% sequential End %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

74. 74 VI. B. Final USRP parameters At this point we now have a fully functional algorithm which can achieve the goals we set out to reach at the start of this report, some fine tuning was in need for the input parameters. The functions and algorithms are the same as previously stated but for the sequential function, the bold purple parameters are much different, this needed to be the case as to achieve an optimal output. The following diagram illustrates the overall system setup. Again the starting and ending frequencies are subject to change: MATLAB: f_start=88e6; %starting frequency f_end=90e6; %ending frequency BW=500e3; %sampling rate, window size N=20000; %Sample points L=901; %Spectral Window Length alpha=0.000001; %threshold value as determined by false alarm probability It is worth mentioning that as the SWL increases, the detection probability increases as well. Note that, the SNR cannot be directly computed in the USRP measurements since the received signal power is the combination of the noise and signal powers. The noise level can still be estimated from the noise floor in the generated spectralestimation plots. %this takes the parameters needed to obtain the USRP data rx_SDRu = comm.SDRuReceiver('192.168.10.2', ... 'CenterFrequency', fc, ... 'Gain', 200, ... 'DecimationFactor', decimation_factor, ... 'LocalOscillatorOffset', 100e6, ... 'SampleRate', Ts, ... 'FrameLength', N, ... 'OutputDataType', 'single') We also had to add a local oscillator (LO) offset to eliminate the harmonic signals generated by the USRP hardware. This was a hardware issue that cannot be avoided, but we tried to account for it and minimize it as much as possible by adding this offset. This is what was originally produced:

75. 75 You can see the issue with the internal harmonics in the plot above, these look like active signals but are periodic and have much larger magnitudes. This is why an offset was introduced to account for these. Also note that there are downward spikes every 0.5MHz, this is the bandwidth sensing window. It has a concave down shape because the USRP uses an internal Band Pass Filter (BPF).There is a slight loss of information at these points because these are graphed on a logarithmic scale so the spikes seem a lot larger than they actually are,if you used a plot function instead, it wouldnt seem significant. where the BPF concavity is the most extreme at the end of the sensing period of the BW. 4.48 4.5 4.52 4.54 4.56 4.58 4.6 4.62 x 10 8 10 -2 10 0 10 2 10 4 f(MHz)-|R(f)|.2 f-vec Smoothed Periodogram of Recieved Signal, SWL=151, Threshold=0.001 4.48 4.5 4.52 4.54 4.56 4.58 4.6 4.62 x 10 8 -2 -1 0 1 2 Thresholdvalue f(MHz) Impulse plot of Scaled/Shifted Recieved Signal, SWL=151, Threshold=0.001 9.3 9.4 9.5 9.6 9.7 10 1 10 2 10 3 10 4 f(MHz)-|R(f)|.2 f-vec Smoothed Periodogram of Recieved Signal, SWL=151, Threshold= 1 1.5 2 e Impulse plot of Scaled/Shifted Recieved Signal, SWL=151, Threshold

76. 76 Now that we have addressed the issues with the USRP,we can scan effectively the spectrum for active frequencies in the local area. We tried to cover a large section of the spectrum but found that it takes a long time to process,and that the MATLAB files were very large. MATLAB crashed a few times just trying to process the information. So only a handful of different sections of the spectrum were sensed just to show the potential capability of this work. As for the time problem, perhaps multiple CRs could scan assigned portions of the spectrum as to not overwork one USRP,and then collaborate the information for the whole spectrum. Using the FCC allocation document [4]: We were able to detect a large variety of different signals. The following format will consist of the sub- band detected center frequencies and their corresponding bandwidths underneath, and the sub-band utilization percentage,the FCC allocation, followed by the sensing plot and the CF/BW plot. Here are the results:

77. 77 8.758.88.858.98.9599.05 x10 7 10 2 10 3 10 4 f(MHz)-|R(f)|.2 f-vec SmoothedPeriodogramofRecievedSignal,SWL=901,Threshold=1e-06,Utilization=22.618% 8.758.88.858.98.9599.05 x10 7 -1 -0.5 0 0.5 1 1.5 2 Thresholdvalue f(MHz) ImpulseplotofScaled/ShiftedRecievedSignal,SWL=901,Threshold=1e-06,Utilization=22.618% 88MHz90MHz:

78. 78 The MATLAB results produced were: x = CF: 87.9959 88.4461 88.9046 89.4353 89.8991 90.0045 BW: 0.1177 0.1003 0.1196 0.1041 0.0231 0.0654 Percentage = 22.6180 % 87.5 88 88.5 89 89.5 90 90.5 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 0.11 0.12 Bandwidth(MHz) Center Frequency (MHz) Detected Signals remaining after threshold test 88MHz 90MHz:

79. 79 1.5451.551.5551.561.5651.571.5751.581.585 x10 8 10 2 10 3 10 4 10 5 f(MHz)-|R(f)|.2 f-vec SmoothedPeriodogramofRecievedSignal,SWL=901,Threshold=1e-06,Utilization=4.7179% 1.5451.551.5551.561.5651.571.5751.581.585 x10 8 -1 -0.5 0 0.5 1 1.5 2 Thresholdvalue f(MHz) ImpulseplotofScaled/ShiftedRecievedSignal,SWL=901,Threshold=1e-06,Utilization=4.7179% 155MHz158MHz:

80. 80 The MATLAB results produced were: x = CF: 155.0000 155.9777 156.5164 157.1447 157.5144 157.6045 BW: 0.0230 0.0225 0.0166 0.0187 0.0225 0.0123 Percentage = 4.7179 % 154.5 155 155.5 156 156.5 157 157.5 158 0.012 0.014 0.016 0.018 0.02 0.022 0.024 Bandwidth(MHz) Center Frequency (MHz) Detected Signals remaining after threshold test 155MHz 158MHz:

81. 81 4.0254.034.0354.044.0454.054.0554.064.0654.074.075 x10 8 10 2 10 3 10 4 f(MHz)-|R(f)|.2 f-vec SmoothedPeriodogramofRecievedSignal,SWL=901,Threshold=1e-06,Utilization=5.7111% 4.0254.034.0354.044.0454.054.0554.064.0654.074.075 x10 8 -1 -0.5 0 0.5 1 1.5 2 Thresholdvalue f(MHz) ImpulseplotofScaled/ShiftedRecievedSignal,SWL=901,Threshold=1e-06,Utilization=5.7111% 403MHz407MHz:

82. 82 The MATLAB results produced were: x = CF: 402.8725 403.4275 404.0279 404.4872 404.9999 405.5425 406.9462 407.0199 BW: 0.0147 0.0336 0.0131 0.0225 0.0244 0.0303 0.0233 0.0110 Percentage = 5.7111 % 402.5 403 403.5 404 404.5 405 405.5 406 406.5 407 407.5 0.01 0.015 0.02 0.025 0.03 0.035 Bandwidth(MHz) Center Frequency (MHz) Detected Signals remaining after threshold test 403MHz 407MHz:

83. 83 6.9977.017.027.037.047.057.06 x10 8 10 2 10 3 10 4 10 5 f(MHz)-|R(f)|.2 f-vec SmoothedPeriodogramofRecievedSignal,SWL=901,Threshold=1e-06,Utilization=7.4545% 6.9977.017.027.037.047.057.06 x10 8 -1 -0.5 0 0.5 1 1.5 2 Thresholdvalue f(MHz) ImpulseplotofScaled/ShiftedRecievedSignal,SWL=901,Threshold=1e-06,Utilization=7.4545% 700MHz705MHz(LTE):

84. 84 The MATLAB results produced were: x = CF: 699.9798 700.0319 700.5018 701.5436 702.0330 703.8591 705.0008 BW: 0.0432 0.0171 0.1144 0.0156 0.0133 0.0144 0.0265 Percentage = 7.4545 % 699 700 701 702 703 704 705 706 0 0.02 0.04 0.06 0.08 0.1 0.12 Bandwidth(MHz) Center Frequency (MHz) Detected Signals remaining after threshold test 700MHz 705MHz (LTE):

85. 85 8.4858.498.4958.58.5058.518.5158.528.525 x10 8 10 2 10 3 10 4 f(MHz)-|R(f)|.2 f-vec SmoothedPeriodogramofRecievedSignal,SWL=901,Threshold=1e-06,Utilization=2.7943% 8.4858.498.4958.58.5058.518.5158.528.525 x10 8 -1 -0.5 0 0.5 1 1.5 2 Thresholdvalue f(MHz) ImpulseplotofScaled/ShiftedRecievedSignal,SWL=901,Threshold=1e-06,Utilization=2.7943% 849MHz852MHz(GSM):

86. 86 The MATLAB results produced were: x = CF: 848.9865 849.9900 851.0165 852.0024 BW: 0.0226 0.0185 0.0225 0.0226 Percentage = 2.7943 % 848.5 849 849.5 850 850.5 851 851.5 852 852.5 0.0185 0.019 0.0195 0.02 0.0205 0.021 0.0215 0.022 0.0225 0.023 Bandwidth(MHz) Center Frequency (MHz) Detected Signals remaining after threshold test 849MHz 852MHz (GSM):

87. 87 0.99850.9990.999511.00051.0011.00151.0021.0025 x10 9 10 0 10 1 10 2 10 3 10 4 10 5 f(MHz)-|R(f)|.2 f-vec SmoothedPeriodogramofRecievedSignal,SWL=901,Threshold=1e-06,Utilization=7.7136% 0.99850.9990.999511.00051.0011.00151.0021.0025 x10 9 -1 -0.5 0 0.5 1 1.5 2 Thresholdvalue f(MHz) ImpulseplotofScaled/ShiftedRecievedSignal,SWL=901,Threshold=1e-06,Utilization=7.7136% 999MHz1.002GHz

88. 88 The MATLAB results produced were: x = 1.0e+03 * CF: 0.9990 0.9995 1.0000 1.0000 1.0006 1.0010 BW: 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Percentage = 7.7136 % 998.5 999 999.5 1000 1000.5 1001 1001.5 0.02 0.025 0.03 0.035 0.04 0.045 0.05 Bandwidth(MHz) Center Frequency (MHz) Detected Signals remaining after threshold test 999MHz 1.002GHz

89. 89 2.0242.0252.0262.0272.0282.0292.032.031 x10 9 10 2 10 3 10 4 f(MHz)-|R(f)|.2 f-vec SmoothedPeriodogramofRecievedSignal,SWL=901,Threshold=1e-06,Utilization=2.6709% 2.0242.0252.0262.0272.0282.0292.032.031 x10 9 -1 -0.5 0 0.5 1 1.5 2 Thresholdvalue f(MHz) ImpulseplotofScaled/ShiftedRecievedSignal,SWL=901,Threshold=1e-06,Utilization=2.6709% 2.025GHz2.03GHz

90. 90 The MATLAB results produced were: x = 1.0e+03 * CF: 2.0250 2.0280 2.0300 BW: 0.0001 0.0000 0.0000 Percentage = 2.6709 % 2024 2025 2026 2027 2028 2029 2030 0.02 0.025 0.03 0.035 0.04 0.045 0.05 0.055 0.06 Bandwidth(MHz) Center Frequency (MHz) Detected Signals remaining after threshold test 2.025GHz 2.03GHz

91. 91 As you can see we were able to scan and identify a wide range of different signals: cell phone LTE, GSM, meteorological, aeronautical radio-navigation,and Earth-Space transmission; within the local SUNY Oswego area on 07/16/2015 between 1:00pm 4:00pm. This date and time is of course unique, as the signals and data change all the time throughout the year. The time it takes for this model USRP to scan and use the algorithm is very inefficient, and that is the main reason as to why large parts of the spectrum were not scanned all at once. We scanned the whole FM spectrum from 88MHz-108MHz and it took a while, MATLAB crashed,and the plot eventually was so heavily concentrated it was impossible to identify anything. But we were able to run the algorithm to return the FM utilization and it was measured at 21.2% (rounded). It is important to remember that this EBDSS method is very basic and that the data and utilization percentages should be interpreted as LOWER BOUNDS for the actuallocal signal spectrum in SUNY Oswego. That is, one should interpret the previous pages as: at least x% of the spectrum is being used. Furthermore, sensing a wide frequency band would require a wideband antenna with constant gain over a wide frequency range. In our case,however,the USRP was equipped with a monopole antenna which does not satisfy the wideband sensing characteristics. This has made the detection of weak signals a more challenging task. VIII. CONCLUSION The objective of this report was to show the steps needed to create an algorithm that would be able to gather spectrum data via USRP and then produce the relevant conclusions about active frequencies, their bandwidths, and spectrum utilization. The understanding of basic signal generation, modulation, and threshold detection is needed before any work considering CRs research and/or manipulation. This is why we went through the steps of showing these basic fundamentals before delving into the main topic of USRP spectrum sensing. Understanding and manipulating signals, ultimately finding a solution considering Gaussian Noise and Internal Hardware Harmonics (IHH) is the end goal. Not only does this report cover the basic characteristics of spectrum sensing, but also what needs to be accounted for when receiving and testing these signals. Successfuldetection through the use of a certain threshold has proven to be a successfulCR approach. Incorporating a smoothing expression into the formulae and plots also has its advantages,as described in this report, but also the necessity of having a large SWL a lower probability of false alarm detection means a higher efficiency rating. The purpose of this report was to create an algorithm that will be able to detect signals with respect to some threshold; we first tested this algorithm with known variables and known functions as to see that it works. Once satisfied further testing through MATLAB and Simulink was undertaken as to further improve the algorithm until we were contempt that USRP raw data could be tested. As described throughout this report, we presented the steps taken to reach this result, but also the logical thought processes too. It must be reiterated that the work done here is to be taken as a lower bound for spectrum sensing analytics and data within the local SUNY Oswego area. Hopefully the work produced here will allow for further investigation, perhaps a more focused approach could be focusing on a particular sub-band for a period of time and relaying it back to the government or owners. Also, if any future work is to be done, a different antenna would be advised as the model: NI USRP 2920 was simply not strong enough to give as accurate results as we would have expected. For now, one can conclude that Energy Detection Based Spectrum Sensing is a fundamental step into understanding the multidimensional problem that is dynamic frequency allocation through CRs.

92. 92 IX. FUTURE WORK For future work I, Max Robertson, would like to pursue this idea of EBDSS with a different USRP, perhaps some investigation into antenna design would be a good place to start. Essentially I would like to replicate the work done in this report but with more accurate hardware and be able to create some constant live stream with multiple CRs covering the spectrum at all times. Some further CR theory will be needed,especially as to not interfere with and real signals in the area. Also testing communication between USRPs would be a great step towards networking them. Lots of work was researched on receiving, and I would like to try transmission too, to try and cover all bases for the CR work overall. If its possible to have two USRPs sensing in different locations but communicating through some shifting un-used sub-band, that would be very interesting, as it would beg the question could you replicate this on a larger scale? It is not difficult to add another for/while loop in the code to repeat the sensing process continuously to give us this live stream,it would be constantly sensing the assigned sub-bands, and with many CRs you could constantly scan the entire spectrum completely autonomously. If I come to the conclusion that EBDSS is not as accurate as another technique, then perhaps a whole new approach would be more rewarding, for example: Waveform-Based Sensing (WBS),Cyclostaionary-Based Sensing (CBS), Radio Identification Based Sensing (RIBS), Matched Filtering (MF), and many more; not to mention the cooperative approach which simply combines some of the previously stated methods. I think there is a lot of potential for the continuation of this project, as most of the hard work is done it would be a matter of implementing this algorithm on to multiple CRs and try to create some network that can scan and feed the user data, but also represent this data in a way that is easily understood for those that have little to no prior knowledge of spectrum sensing.

93. 93 BIBLIOGRAPHY [1] Yucek, Tevfik, and Huseyin Arslan. "A Survey of Spectrum Sensing Algorithms for Cognitive Radio Applications." IEEE Commun. Surv. Tutorials IEEE Communications Surveys & Tutorials 11.1 (2009): 116-30. Web. 19 July 2015. [2], Bkassiny, Mario, Sudharman K. Jayaweera, and Yang Li. "Blind Cyclostationary Feature Detection Based Spectrum Sensing for Autonomous Self-learning Cognitive Radios." Comp. Keith A. Avery. IEEE ICC 2012 - Cognitive Radio and Networks Symposium (2012): 1507-511.IEEE Xplore.Reference: Page 1511,Appendix. Web. 19 July 2015. [3] Kim, Young Min, Guanbo Zheng, Sung Hwan Sohn, and Jae Moung Kim. "An Alternative Energy Detection Using Sliding Window for Cognitive Radio System." 2008 10thInternational Conference on Advanced Communication Technology (2008): 481-85. Web. 19 July 2015. [4] United States Department of Commerce. "File:United States Frequency Allocations Chart 2011 - The Radio Spectrum.pdf." Https://en.wikipedia.org/.United States Department of Commerce, 01 Aug. 2011. Web. Source: http://www.ntia.doc.gov/files/ntia/publications/spectrum_wall_chart_aug2011.pdf. Web. 19 July 2015.

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