Simulation – Page 9

June 3, 2011November 15, 2011BER Performance

Equal Gain Combining in Rayleigh Fading

When wireless signals travel from a single transmit antenna to multiple receive antennas they experience different fading conditions. While signal from one path may experience a deep fade the signal from another path may be stronger. Therefore selecting the stronger of the two signals (selection combining, threshold combining) or adding the signals (equal gain combining, maximal ratio combining) would always yield much better results (lower bit error rate). However, there must be sufficient spacing between the different receive antennas for the received signals to be dissimilar (uncorrelated). In the simulation below we consider a 1-Tx, 2-Rx scenario. The signals arriving […]

June 2, 2011March 22, 2013BER Performance

Bit Error Rate of QPSK in Rayleigh Fading

So far we have considered the bit error rate (BER) of BPSK and QPSK in an AWGN channel. Now we turn our attention to a Rayleigh fading channel which is a more realistic representation of a wireless communication channel. We consider a single tap Rayleigh fading channel which is good approximation of a flat fading channel i.e. a channel that has flat frequency response (but varying with time). The complex channel coefficient is given as (a+j*b) where a and b are Gaussian random variables with mean 0 and variance 0.5. We use the envelope of this channel coefficient in our […]

June 2, 2011June 3, 2014BER Performance

Bit Error Rate of QPSK

Simulating a QPSK system is equivalent to simulating two BPSK systems in parallel. So there is no difference in bit error rate(BER). Since the simulation is at baseband we multiply the in-phase and quadrature streams by 1 and j respectively (instead of cos and sin carriers). At the receiver we just use the real and imag functions to separate the two symbol streams. The BER is the average BER of the two parallel streams. As in the case of BPSK we can show that the baseband representation (using 1 and j) is equivalent to using the passband representation (using cosine […]

June 1, 2011May 21, 2020BER Performance

Bit Error Rate of BPSK

Modulation is the process by which a binary stream (zeros and ones) is converted to a format that is suitable for transmission over a wired or wireless channel that is prone to noise and interference as well as distortion. The most basic modulation scheme is BPSK or Binary Phase Shift Keying. It transmits the information in the phase of the signal which could be one of two values (0 degrees or 180 degrees). BPSK signal can be represented as (called the passband representation) s(t)=a(t)*cos(2*pi*f*t) where a(t) is a time varying parameter which can have one of two values (+1 or […]

May 29, 2011August 23, 2011Channel Modeling

Simulating a SISO Ring Model

We simulate the SISO Ring Model described previously by varying the transmit receive separation from 50m to 500m. Keeping the ring radius fixed at 20m the angular spread of the channel decreases as the receiver moves away from the transmitter. It is observed that the power level of the received signal fluctuates as the distance ‘d’ is varied. However, after a certain critical distance (around 250 m) the power vs distance curve approaches a straight line.

May 28, 2011March 22, 2013Channel Modeling

Simulating a SISO Ring Model

A Ring Model is a well known spatial channel model. It models the propagation channel as an unobstructed transmitter and a receiver surrounded by a ring of reflectors. The distance between the transmitter and receiver is usually much larger than the radius of the ring. The reflectors are distributed uniformly around the ring. This model is useful for modeling a scenario where a base station is located at sufficient altitude and is unobstructed whereas the mobile station is at ground level and is surrounded by a bunch of reflectors. Ring Model Given below is the MATLAB code for a SISO Ring […]

May 27, 2011November 15, 2011Channel Modeling

Simulating a MIMO Ring Model

A Ring Model is a well known spatial channel model. It models the propagation channel as an unobstructed transmitter and a receiver surrounded by a ring of reflectors. The distance between the transmitter and receiver is usually much larger than the radius of the ring. The reflectors are distributed uniformly around the ring. This model is useful for modeling a scenario where a base station is located at sufficient altitude and is unobstructed whereas the mobile station is at ground level and is surrounded by a bunch of reflectors. Given below is the MATLAB code that calculates the composite signal (from […]