WiMAX – RAYmaps

April 6, 2012April 6, 2012Antennas, LTE, WiMAX

Antennas on Samsung Galaxy S

We have previously discussed the theory of Planar Inverted F Antennas (PIFA), now let us look at a practical example. Shown below is the rear view of a Samsung Galaxy S phone with six antennas. The description of these antennas is given below. 1. 2.6 GHz WiMAX Tx/Rx Antenna 2. 2.6 GHz WiMAX Antenna Rx Only (as a diversity antenna) 3. WiFi/Bluetooth Tx/Rx Antenna 4. Cell/PCS CDMA/EVDO Tx/Rx Antenna 5. Cell/PCS CDMA/EVDO Rx Only (as a diversity antenna) 6. GPS Antenna Rx Only The figure above shows the top conducting plane of the PIFAs. The bottom conducting plane (ground plane) is […]

March 27, 2012April 6, 2012Antennas, LTE, WiMAX

Planar Inverted F Antenna (PIFA)

A Planar Inverted F Antenna or PIFA is a very common antenna type being used in cell phones. In fact a cell phone would have multiple PIFAs for LTE, WiMAX, WiFi, GPS etc. Furthermore, there would be multiple PIFAs for diversity reception and transmission. A PIFA is composed of 5 basic elements. 1. A large metallic ground plane 2. A resonating metallic plane 3. A substrate separating the two planes 4. A shorting pin (or plane) 5. A feeding mechanism The resonant frequency of the PIFA can be calculated from the relationship between the wavelength of the antenna and the […]

December 8, 2011June 4, 2014BER Performance, LTE, WiMAX

QAM Theoretical BER in AWGN

Quadrature Amplitude Modulation (QAM) is an important modulation scheme as it allows for higher data rates and spectral efficiencies. The bit error rate (BER) of QAM can be calculated through Monte Carlo simulations. However this becomes quite complex as the constellation size of the modulation schemes increases. Therefore a theoretical approach is sometimes preferred. The BER for Gray coded QAM, for even number of bits per symbol, is shown below. Gray coding ensures that a symbol error results in a single bit error. The code for calculating the theoretical QAM BER for k even (even number of bits per symbol) […]

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 […]

November 1, 2011June 16, 2014Channel Modeling

Computationally Efficient Rayleigh Fading Simulator

We had previously presented a method of generating a temporally correlated Rayleigh fading sequence. This was based on Smith’s fading simulator which was based on Clark and Gan’s fading model. We now present a highly efficient method of generating a correlated Rayleigh fading sequence, which has been adapted from Young and Beaulieu’s technique [1]. The architecture of this fading simulator is shown below. This method essentially involves five steps. 1. Generate two Gaussian random sequences of length N each. 2. Multiply these sequences by the square root of Doppler Spectrum S=1.5./(pi*fm*sqrt(1-(f/fm).^2). 3. Add the two sequences in quadrature with each other […]

October 21, 2011June 7, 2012Channel Modeling, LTE, WiMAX

A Rayleigh Fading Simulator with Temporal and Spatial Correlation

Just to recap, building an LTE fading simulator with the desired temporal and spatial correlation is a three step procedure. 1. Generate Rayleigh fading sequences using Smith’s method which is based on Clarke and Gan’s fading model. 2. Introduce spatial correlation based upon the spatial correlation matrices defined in 3GPP 36.101. 3. Use these spatially and temporally correlated sequences as the filter taps for the LTE channel models. We have already discussed step 1 and 3 in our previous posts. We now focus on step 2, generating spatially correlated channels coefficients. 3GPP has defined spatial correlation matrices for the Node-B […]

October 21, 2011November 15, 2011Channel Modeling, LTE, WiMAX

Building an LTE Channel Simulator

As discussed previously building an LTE fading simulator is a three step procedure. 1. Generate a temporally correlated Rayleigh fading sequence. This step would be repeated for each channel tap and transmit receive antenna combination e.g. for a 2×2 MIMO system and EPA channel model with 7 taps the number of fading sequences to be generated is 4×7=28. The temporal correlation of these fading sequences is controlled by the Doppler frequency. A higher Doppler frequency results in faster channel variations and vice versa. 2. Introduce spatial correlation between the parallel paths e.g. for a 2×2 MIMO system a 4×4 antenna […]

October 19, 2011November 15, 2011BER Performance, Channel Modeling, LTE, WiMAX

Can We Do Without a Cyclic Prefix

Have you ever thought that Cyclic Prefix in OFDM is just a gimmick and we could do equally well by using a guard period i.e. a period of no transmission between two OFDM symbols. Well, one way to find out if this is true is by running a bit error rate simulation with and without a cyclic prefix (only a vacant guard period). We use the 64-QAM OFDM simulation that we developed previously. The channel is modeled as 7-tap FIR filter with each tap having a Rayleigh distribution. BER with and without Cyclic Prefix We simulate the case of a […]

October 12, 2011June 24, 2020Channel Modeling, LTE, WiMAX

LTE Fading Simulator

As discussed previously an LTE channel can be modeled as an FIR filter. The filter taps are described by the EPA, EVA and ETU channel models. If x(k) is the original signal then the signal at the output of the FIR filter y(k) is given as: y(k)=x(k)*c(0)+x(k-1)*c(1)+…..+x(k-L+2)*c(L-2)+x(k-L+1)*c(L-1) Since the wireless channel is time varying the channel taps c(0) c(1)…..c(L-1) are also time varying with either Rayleigh or Rician distribution. It is quite easy to generate Rayleigh random variables with the desired power and distribution, however, when these Rayleigh random variables are required to have temporal correlation the process becomes a […]