OFDM – RAYmaps

August 24, 2017August 28, 2017BER Performance, Channel Modeling, LTE, WiMAX

BER for BPSK-OFDM in Frequency Selective Channel

As the data rates supported by wireless networks continue to rise the bandwidth requirements also continue to increase (although spectral efficiency has also improved). Remember GSM technology which supported 125 channels of 200KHz each, which was further divided among eight users using TDMA. Move on to LTE where the channel bandwidth could be as high as 20MHz (1.4MHz, 3MHz, 5MHz, 10MHz, 15MHz and 20MHz are standardized). This advancement poses a unique challenge referred to as frequency selective fading. This means that different parts of the signal spectrum would see a different channel (different amplitude and different phase offset). Look at […]

January 26, 2015January 26, 2015Capacity, Fundamentals

Shannon Capacity CDMA vs OFDMA

We have previously discussed Shannon Capacity of CDMA and OFMDA, here we will discuss it again in a bit more detail. Let us assume that we have 20 MHz bandwidth for both the systems which is divided amongst 20 users. For OFDMA we assume that each user gets 1 MHz bandwidth and there are no guard bands or pilot carriers. For CDMA we assume that each user utilizes full 20 MHz bandwidth. We can say that for OFDMA each user has a dedicated channel whereas for CDMA the channel is shared between 20 simultaneous users. We know that Shannon Capacity […]

Udemy Course

Introduction to Wireless Communications • In this course you will learn the basic principles of wireless communications from 1G to 5G and beyond. You will learn about frequency reuse, capacity, channel coding, modulation and demodulation, OFDM, MIMO, and a host of other topics. • This course is for you if you are a student and have just started learning about wireless communications or if you are a guy in the field who wants to get a better handle on the fundamental concepts of wireless communications. Here is the link to the course.

April 9, 2013April 9, 2013Capacity, Fundamentals, LTE

Does Shannon Capacity Increase by Dividing a Frequency Band into Narrow Bins

Somebody recently asked me this question “Does Shannon Capacity Increase by Dividing a Frequency Band into Narrow Bins”. To be honest I was momentarily confused and thought that this may be the case since many of the modern Digital Communication Systems do use narrow frequency bins e.g. LTE. But on closer inspection I found that the Shannon Capacity does not change, in fact it remains exactly the same. Following is the reasoning for that. Shannon Capacity is calculated as: C=B*log2(1+SNR) or C=B*log2(1+P/(B*No)) Now if the bandwidth ‘B’ is divided into 10 equal blocks then the transmit power ‘P’ for each […]

May 16, 2012May 16, 2012Fundamentals

Fast Fourier Transform – Code

Most of us have used the FFT routine in MATLAB. This routine has become increasingly important in simulation of communication systems as it is being used in Orthogonal Frequency Division Multiplexing (OFDM) which is employed in 4G technologies like LTE and WiMAX. We would not go into the theoretical details of the FFT, rather, we would produce the MATLAB code for it and leave the theoretical discussion for a later time. The underlying technique of the FFT algorithm is to divide a big problem into several smaller problems which are much easier to solve and then combine the results in […]

May 2, 2012May 2, 2012Fundamentals

Detecting Sinusoids in Noise

We have previously discussed the problem of detecting two closely spaced sinusoids using the Discrete Fourier Transform (DFT). We assumed that the data set we got was pure i.e. there was no noise. However, in reality this is seldom the case. There is always some noise, corrupting the signal. Let us now see how it effects the detection problem. We consider Additive White Gaussian Noise (AWGN) as the corrupting source. The noise power is set equal to the power of the two sinusoids i.e. we have an SNR of 0 dB. This is quite a severe case, the noise power […]

April 30, 2012April 30, 2012Fundamentals

Resolution of Discrete Fourier Transform

In the previous post we had introduced the Discrete Fourier Transform (DFT) as a method to perform the spectral analysis of a time domain signal. We now discuss an important property of the DFT, its spectral resolution i.e. its ability to resolve two signals with similar spectral content. Initially one might think that increasing the sampling frequency would increase the spectral resolution but this totally incorrect. In fact if the sampling frequency is increased, keeping the number of time domain samples to be the same, the resolution actually decreases. So how do we calculate the spectral resolution. One simple way is […]

April 27, 2012April 29, 2012Fundamentals

Discrete Fourier Transform

Discrete Fourier Transform or DFT is a mathematical operation that transforms a time domain signal to frequency domain. It is usually implemented using the Fast Fourier Transform (FFT). The computational complexity of the DFT is N2 whereas its (N)log2N for the FFT, where N is the number of samples of the the time domain signal. Mathematically, the DFT is written as and this operation can be easily implemented in MATLAB as shown below. clear all close all fm=100; fs=1000; Ts=1/fs; N=1024; t=0:Ts:(N-1)*Ts; x=cos(2*pi*fm*t); W=exp(-j*2*pi/N); n=0:N-1; for k=0:N-1 X(k+1)=sum(x.*(W.^(k*n))); end plot((0:k)/N,abs(X)) xlabel (‘Normalized Frequency’) ylabel (‘X’) The DFT of the cosine […]

November 13, 2011November 18, 2011LTE, WCDMA, WiMAX

CDMA vs OFDMA

Property CDMA OFDMA 1. Channel bandwidth Full system bandwidth Variable system bandwidth to accommodate users with different data rates, 1.25, 2.50, 5.00, 10.00, 15.00 and 20.00 MHz, actual transmission bandwidth is a bit lower than this 2. Frequency-selective scheduling Not possible A key advantage of OFDMA, although it requires accurate real-time feedback of channel conditions from receiver to transmitter 3. Symbol period Very short—inverse of the system bandwidth Very long—defined by subcarrier spacing and independent of system bandwidth 4. Equalization Complicated time domain equalization Simple frequency domain equalization 5. Resistance to mulitpath Rake receiver can combine various multipath components Highly resistant […]