WiMAX – Page 2

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 […]

October 7, 2011November 21, 2013Channel Modeling, LTE, WiMAX

LTE Multipath Channel Models

When a wireless signal travels from a transmitter to a receiver it follows multiple paths. The signal may travel directly following the line of sight between the transmitter and receiver, it may bounce off the ground and reach the receiver or it may be reflected by multiple buildings on the way to the receiver. When these copies of the same signal arrive at the receiver they are delayed and attenuated based upon the path length that they have followed and various other factors. A well known technique to model such a wireless channel is to model it as an FIR […]

October 3, 2011November 15, 2011Antennas, LTE, Network Planning, WiMAX

Antenna Radiation Pattern and Antenna Tilt

An introductory text in Communication Theory would tell you that antennas radiate uniformly in all directions and the power received at a given distance ‘d’ is proportional to 1/(d)^2. Such an antenna is called an isotropic radiator. However, real world antennas are not isotropic radiators. They transmit energy in only those directions where it is needed. The Gain of a antenna is defined as the ratio of the power transmitted (or received) in a given direction to the power transmitted in that direction by an isotropic source and is expressed in dBi. Although antenna Gain is a three dimensional quantity, […]

September 27, 2011November 15, 2011Antennas, LTE, Network Planning, WiMAX

Base Station Antenna Tilt and Path Loss

Path loss is basically the difference in transmit and receive powers of a wireless communication link. In a Free Space Line of Sight (LOS) channel the path loss is defined as: L=20*log10(4*pi*d/lambda) where ‘d’ is the transmit receive separation and ‘lambda’ is the wavelength. It is also possible to include the antenna gains in the link budget calculation to find the end to end path loss (cable and connector losses may also be factored in). Antenna gains are usually defined along a horizontal plane and vertical plane passing through the center of the antenna. The antenna gain can then be […]

September 24, 2011November 15, 2011Network Planning, WiMAX

WiMAX Path Loss and Antenna Height

As discussed previously the SUI (Stanford University Interim) model can be used to calculate the path loss of a WiMAX link. The SUI model is given as: SUI Path Loss Equation It has five components: 1. The free space path loss (A) up to the reference distance of ‘do’. 2. Additional path loss for distance ‘d’ with path loss exponent ‘n’. 3. Additional path loss (Xf) for frequencies above 2000 MHz. 4. Path gain (Xh) for receive antenna heights greater than 2 m. 5. Shadowing factor (s). The most important factor in this equation is the distance dependent path loss. […]

September 23, 2011February 14, 2014Channel Modeling, WiMAX

WiMAX Path Loss Calculation

Calculation of the path loss is fundamental to Wireless System Design. There are many models available for calculating the path loss such as Okumura Model, Hata Model, COST-231 Model and more recently the SUI (Stanford University Interim) Model. The SUI Model has been specifically proposed for Broadband Wireless Access Systems such as WiMAX. It defines three types of environments namely A, B and C which are equivalent to the urban, suburban and rural environments defined in the earlier models. According to this model the path loss can be calculated as: PL=A+10*n*log10(d/do)+Xf+Xh+s where n=a-(b*hb)+(c/hb) A=20*log10(4*pi*do/lambda) Xf=6.0*log10(f/2000) Xh=-10.8*log10(hr/2) for A&B Xh=-20.0*log10(hr/2) for […]