1. GUIDED BY:- Mr. ABHAY GOEL (ET DEPT) SUBMITTED BY:- Amit Krishna Dwivedi(ET VII-A) Ankita Gupta(ET VII-A) Chanchal Singh(ET VII-A) Anuj Kumar(ET VII-A)

2. Agenda  Introduction  Communication using OFDM  Block diagram  Working of OFDM  BER & SNR calculation  Application of OFDM  Advantages & disadvantages  Summary  References

3. OFDM(Orthogonal Frequency Division Multiplexing)  A large number of orthogonal, overlapping, narrow band subcarriers, transmitted in parallel, divide the available transmission bandwidth.  The separation of the subcarriers is theoretically minimal such that there is a very compact spectral utilization.  In wireless communication parallel transmission of symbol is applied to achieve high throughput and better transmission quality.  The idea of OFDM is to split the total transmission bandwidth into a number of orthogonal subcarriers in order to transmit the symbol rate using these subcarriers in parallel.

4.  The attraction of OFDM is mainly due to how system handles the multipath interference at the receiver.  Four main criteria were used to assess the performance of the OFDM system, which were its tolerance to multipath delay spread, peak power clipping, channel noise and time synchronization errors.

5. OFDM model used

6. BER(Bit Error Rate)  In telecommunication transmission, the bit error rate (BER) is the percentage of bits that have errors relative to the total number of bits received in a transmission.  The BER is an indication of how often a packet has to be retransmitted because of an error.

7. Calculation of BER  For calculating BER we use the formula = no. of bits in error/total no. of bits.  For example, a transmission might have a BER of 10 to the minus 6, meaning that, out of 1,000,000 bits transmitted, one bit was in error.

8. SNR(Signal to Noise ratio)  The ratio of the amplitude of a desired analog or digital data signal to the amplitude of noise in a transmission channel at a specific point in time.  It measures the quality of a transmission channel or an audio signal over a network channel.

9. Calculation of SNR  Calculate the SNR when the power of both the signal and noise is measured across the same impedance.  Under this condition, the SNR may be calculated as SNR = Ps/Pn = (Rs/Rn)^2, where Rs is a measurement of an RMS amplitude (typically voltage) of the signal, and Rn is a measurement of an RMS amplitude of the noise.

10. OFDM system implementation  OFDM is to use it for wireless communications systems such as cellular mobile phone systems, fixed wireless phone systems, wireless data links and wireless computer local area networks.  USING GENERAL PURPOSE DSPs-The processing efficiency of a DSP processor depends on the architecture of the processor, however for most single instruction DSP’s the number of cycles required to calculate an FFT is twice the total number of calculations

11. MATLAB Implementation  The current version of the MATLAB simulation accepts binary, text, or sound as input.  In MATLAB simulation we have generated an OFDM signal X and use MATLAB function AWGN to noise to the signal X. Then the signal is demodulated at receiver end.  Calculation of BER and SNR is done using MATLAB function.

12. Example Using MATLAB  Take an example of the graphs generated by the current MATLAB code.  The upper left plot is the input sound file. After modulation, the corresponding OFDM transmission is shown on the upper right.  For this example, a perfect channel was assumed which means that the

13. Applications  OFDM is extensively used in wireless LAN and MAN applications.  It is used by many powerline devices to extend internet connections to other rooms in a home through its power wiring.  Terrestrial digital radio and television broadcasting.  OFDM is to use it for wireless communications systems such as cellular mobile phone systems, fixed wireless phone systems, wireless data links and wireless computer local area networks

14. ADVANTAGES  Makes efficient use of the spectrum by allowing overlap.  By dividing the channel into narrowband sub channels, transmitted in parallel, divide the available transmission bandwidth.  The OFDM system handles the multipath interference at the receiver.  Using adequate channel coding the recovery of symbols lost due to the frequency selectivity of the channel.

15. DISADVANTAGES  The OFDM signal has a noise like amplitude with a very large dynamic range, therefore it requires RF power amplifiers  Due to use of multipath the two major effects of “frequency selective fading” & “intersymbol interference”.  Powerful error correcting codes and the insertion of an extra guard interval between consecutive OFDM symbols is required.

16. SUMMARY  OFDM is a frequency division multiplexing system, used for modulation for various wireless communication.  By calculating its BER we can evaluate the amount of error.  We can also evaluate the amount of noise interference in the signal.  We can find many advantages in OFDM, but there are still many complex problems to solve. The purpose of this project is to provide a basic simulation. We hope that by using the specifications of a working system we are able to provide a much better explanation of the fundamentals of OFDM.

17. IMPLEMENTATION USING MATLAB

18. Way For Performing The Task  Generate a random binary stream.  Modulate the signal using 16-QAM.  Add White Gaussian Noise.  Create a scatter plot.  Demodulate using 16-QAM.  Compute BER.

19. SAMPLING  In Matlab, we represent continuous-time signals with a sequence of numbers, or samples, which are generally stored in a vector or an array.  Before we can perform a bit-error-rate test, we must precisely understand the meaning of these samples. We  must know what aspect of the signal the value of these samples represents. Wemust  also know the time interval between successive samples.

20.  For communications simulations, the numeric value of the sample represents the amplitude of the continuous-time signal at a specific instant in time.  We assume this amplitude is a measurement of voltage, though it could just as easily be a measurement of current.  The time between successive samples is, by definition, Ts. This tells us how often the continuous-time signal was sampled. Instead of specifying Ts,we usually specify the sampling frequency, fs, which is the inverse of Ts.

21. POWER CALCULATION  Suppose we have a signal x(n), where n is an index of the sample number. We define the instantaneous power of the signal as: Pins ≡ x2(n).  In other words, the instantaneous power of a sample is just the value of that sample squared. Since the units of the sample are volts, the units of the power are watts.  A far more useful quantity is the average power, which is simply the average of the instantaneous power of every sample in the signal. For signal x(n), of N samples, the average power, ‘pav’, of signal ‘x’, using Matlab is:  pav= sum(x.^2)/length(x).

22. ENERGY CALCULATION  Since energy is the product of power and time, the total energy of a signal must be equal to its average power multiplied by its duration.  Furthermore, the duration of a signal is its length in samples, divided by the sampling frequency, in samples per second. Therefore: The Matlab command for finding the total energy, ‘et’, of signal ‘x’, that has sampling rate ‘fs’, is: et= sum(x.^2)/fs.

23. SIMULATION PROCEDURE  RUN TRANSMITTER :-The first step in the simulation is to use the transmitter to create a digitally modulated signal from a sequence of pseudo-random bits. Once we have created this signal, x(n), we need to make some measurements of it.  ESTABLISH SNR :-The signal-to-noise-ratio (SNR), Eb /N0, is usually expressed in decibels, but we must convert decibels to an ordinary ratio before we can make further use of the SNR. If we set the SNR to m dB, then Eb/N0 = 10m/10. Using Matlab, we find the ratio, ‘ebn0’, from the SNR in decibels, ‘snr db’, as:ebn0= 10^(snr db/10).

24. CALCULATE Eb :- eb = sum(x.^2)/(length(x) ∗ fb). Since our signal, x(n), is in units of volts, the units of Eb are Joules. CALCULATE No :- Using Matlab, we find the power spectral density of the noise, ‘n0’, given energy- per-bit ‘eb’, and SNR ‘ebn0’, as:n0 = eb/ebn0.The power spectral density of the noise has units of Watts per Hertz.

25. GENERATE NOISE  The Matlab function ‘randn’ generates normally distributed random numbers with a mean of zero and a variance of one.  We must scale the output so the result has the desired variance, ¾n. To do this, we simply multiply the output of the ‘randn’ function by p¾n.  We can generate the noise vector ‘n’, as: n = sqrt(pn) ∗ randn(1,length(x));. Like the signal vector, the samples of the noise vector have units of volts.

26. CALCULATION OF BIT ERROR RATE  Using Matlab, we calculate the error vector, ‘err’, fromthe transmitted bit vector,‘tx’, and the received bit vector, ‘rx’, having an offset of ‘off’, as: err= tx−rx(off+1 : length(tx)+off);.  we calculate the total number of bit errors, ‘te’, fromthe error vector ‘err’ as:te= sum(abs(err)).

27. CONT….  we compute the bit-error-rate, ‘ber’, as: ber= te/length(tx),where ‘te’ is the total number of bit errors, and ‘tx’ is the transmitted bit vector.

28. SIMULATION RESULT  we compute the bit-error-rate, ‘ber’, as: ber= te/length(tx),where ‘te’ is the total number of bit errors, and ‘tx’ is the transmitted bit vector.

29. THANK YOU