Simulation Modeling Of Rayleigh Fading

Published Date: 02 Nov 2017

This chapter of the thesis describes the simulation modeling and realization of Rayleigh fading and optimization of the effect of path loss in simulation using a packet oriented simulation tool known as Optimized Network Engineering Tool (OPNET).

OPNET is a set of decision support tools, supplying a comprehensive development environment for specification, simulation and performance analysis of communication networks, devices and applications. It enables us to build models, execute simulations and analyse the output result.

However, as a packet oriented tool, it is not well suited for simulation at the physical layer which involves bits and signals in communication. Moreover it is difficult to simulate some intrinsic effects of wireless signal propagation such as multipath fading, path loss and shadowing fading.

In this chapter, we address some of the above problems of OPNET for wireless network simulation. First, we note that OPNET Modeler 14.5 ignores the fading effect, treating all wireless channels as Gaussian channel. As a result, the simulation results obtained by OPNET are usually over-optimistic and do not reflect what really occurs in a fading environment. To solve this problem, we create new modulation curve models to realize the effect of fading in simulation.

Simulation modeling of Rayleigh fading

Using the BER- Eb/N0 expression for DPSK and QAM64 modulation over a Rayleigh fading channel, we make two programs in MATLAB to generate a series of data of BERs. Since the modulation curves over a Gaussian channel in OPNET have a sampling resolution of 100, each program generates 100 values of BERs over a certain range of Eb/N0. Then, using OPNET’s EMA (External model Access) modeling function, we shape new modulation curves model for DPSK and QAM64.

Realization of Rayleigh fading in simulation

The model for the BER pipeline stage in the Wireless Suite of OPNET is called wlan_ber. In this model, BER is evaluated based on the previously computed average power SNR and the processing gain at the receiver. The SNR in dB is obtained by the pipeline stage and is added to the processing gain (also in dB) to obtain an effective SNR. The effective SNR can be converted from log scale and expressed as Eb/N0. The bit error rate is derived from the effective SNR based on a modulation curve assigned to a receiver. By default, OPNET assumes a Gaussian channel model is used and does not consider any fading. To address this problem, we have created new BER curves in OPNET. The BER curves consider the fading effect and affects OPNET simulation results at the network level.

To realize the effect of fading, the pipeline stage file "wlan_ber" is modified such that the bit error rate will now be derived from the effective SNR based on the new modulation curves assigned to the receiver. To examine the fading effect, we model two wireless nodes separated by 200m as shown in the figure below. One of the nodes behaves as a transmitter and the other one as a receiver.

Figure 1.

The physical architecture of the WLAN network is an Ad-hoc (Infrastructureless) architecture. It is the simplest WLAN configuration. In an ad-hoc network, there is no access point and the devices in the LAN configure themselves at the same radio channel to enable peer-to-peer communication. In the configuration above, the two devices are configured at the same radio channel that is a Rayleigh fading channel. In the above scenario, the MAC mechanism being used is 802.11b which uses DPSK modulation with a data rate of 2Mbps. The transmit power of the transmitter node is set to 0.030 and the received power threshold of the receiver node for arriving packets is set to -75dBm. Packets with a power less than the threshold are not sensed and decoded by the receiver.

Figure 2.

The LAN MAC Address of the transmitter is 1 and its destination address is 2, which is the MAC address of the receiver. The characteristic of the traffic that the transmitter node generates is as shown by the figure above. The time at which the application generates traffic is 0.000001. The application will be on for 1000000seconds and will never be off. The packet size is 512 bytes. The interarrival time attribute of the transmitter node is set to 0.001seconds which implies that a packet will be generated (sent) every 0.001 seconds.

Using OPNET 14.5, we analyses the throughput and delay at the receiver node for DPSK and QAM64 with and without Rayleigh fading.

To investigate the relationship between throughput and distance for DPSK and QAM64, a new model is used where the receiver is made to move over 500 metres at a speed of 1ms-1 from the transmitter using the trajectory attribute in OPNET. The network model is as shown in figure.

Figure 3.

The sample mean of the throughput and delay of the receiver node is captured every 0.5 seconds using the discrete event simulation tool on OPNET. Graphs of throughput against simulation time for DPSK and QAM64 with and without Rayleigh fading are obtained where the simulation time implies the actual distance from the transmitter.

The main simulation parameters are set according to Table 1.

Modulation

MAC mechanism

Maximum data rate (Mbps)

Transmit power (W)

Threshold of received (dBm)

Packet lengh (bytes)

QAM64

802.11a

54

0.03

-75

512

DPSK

802.11b

2

0.03

-75

512

QAM64

802.11g

54

0.03

-75

512

Table 1. Simulation parameters for the effect of Rayleigh faidng

Optimization of the effect of path loss

The second problem with the OPNET Wireless Suite is that it uses a fixed path loss exponent that is set to 2 without considering that different environments have different path loss exponent. This may lead to inaccurate simulation results. The model that computes average power level of signals received by radio receiver channel in OPNET Wireless Suite is the pipeline stage file wlan_power. To solve the problem, we modify the pipeline stage file wlan_power. The free space propagation loss in the default pipeline stage wlan_power is computed as a function of wavelength and propagation distance with the relation given by the equation below.

In OPNET, the following expression is utilized to compute the received power

where B is the bandwidth. and are the maximum and minimum frequency of the radio signal, respectively. and are the antenna gains of the transmitter and the receiver, respectively.

From the above equations, it is observed that OPNET uses the simplified path loss model with path loss exponent fixed to 2 to compute the received power at the receiver. The propagation path loss expression is modified according to the equations in Table 2 with different path loss exponent.

, where d is distance between the transmitter and receiver and d0 is the reference distance for the antenna far field which is set to 10m.

Path loss exponent

Propagation path loss expression

1.6

2(Free-space)

2.5

2.8(usually outdoors)

3

3.5

4(Two-ray model)

Table 2. Propagation path loss equations for path loss exponents ranging from 3 to 6

To investigate the effect of different values of path loss exponent, we design a scenario similar to that in figure 3. A series of simulation experiments are implemented with different path loss exponents using 802.11a, 802.11b and 802.11g MAC mechanisms. Throughput and delay at the receiver and also the distance at which the throughput goes to zero are recorded. The simulation parameters are set according to Table 1. We analyses the impact of the path loss exponent parameter on throughput and delay at the receiver and the communication range of the transmitter.

Integrated effect of Rayleigh fading and path loss

A scenario similar to that in figure 3 is implemented to examine the integrated effect of Rayleigh fading and path loss in terms of throughput and delay. The main parameters are set according to Table 1. In addition, the path loss exponents is set to 2, 2.8 and 4 which corresponds to path loss exponents for free space, usual environment, and the two ray model, respectively.