Channel Modeling – Page 2

October 1, 2014October 31, 2014Channel Modeling, Fundamentals, LTE

Modified Young’s Fading Simulator

In the previous posts we had discussed generation of a correlated Rayleigh fading sequence using Smith’s method [1] and Young’s modification of Smith’s method [2]. The main contribution of Young was that he proposed a mechanism where the number of IDFTs was reduced by half. This was achieved by first adding two length N IID zero mean Gaussian sequences filtered by the filter F[k] and then performing the IDFT on the resulting complex sequence. This was different to Smith’s method where the IDFT was performed simultaneously on two branches and then the outputs of these branches were added in quadrature […]

June 26, 2012June 29, 2012Channel Modeling, Fundamentals

Uniform, Gaussian and Rayleigh Distribution

It is sometimes important to know the relationship between various distributions. This can be useful if there is a function available for one distribution and it can be used to derive other distributions. In the context of Wireless Communications it is important to know the relationship between the Uniform, Gaussian and Rayleigh distribution. According to Central Limit Theorem the sum of a large number of independent and identically distributed random variables has a Gaussian distribution. This is used to model the amplitude of the in-phase and quadrature components of a wireless signal. Shown below is the model for the received […]

January 10, 2012February 2, 2012Channel Modeling, LTE

Implementing a Non-Uniformly Spaced Tapped Delay Line Channel Model

Question: Since you are good on fundamentals I would like to ask you a question that puzzles me. LTE channels models are defined at irregular time intervals as shown in [1]. The EPA, EVA and ETU channel taps can best be described as being sampled at multiples of 10 nsec. However, LTE signal is sampled at multiples of 3.84 MHz (Ts=260.416667 nsec). So how does one perform convolution operation. Answer: Empirical multipath channel is usually characterized as a τ-spaced tapped delay line (TDL), whose power delay profile (PDP) is either uniformly spaced, or more frequently, spaced with arbitrary time delay(s). […]

January 6, 2012June 7, 2012Channel Modeling, LTE

WINNER-II Path Loss Model

In simple terms the path loss is the difference between the transmitted power and the received power of a wireless communication system. This may range from tens of dB to more than a 100 dB e.g. if the transmitted power of a wireless communication system is 30 dBm and the received power is -90 dBm then the path loss is calculated as 30-(-90)=120 dB. Path loss is sometimes categorized as a large scale effect (in contrast to fading which is a small scale effect). According to the WINNER-II model the path loss can be calculated as: Here d is the […]

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 […]